My Customized Papers

[ScienceDirect Publication: Solid State Ionics] Engineering dense superionic Li₁₊ₓAlₓTi₂₋ₓ(PO₄)₃ solid electrolytes for safer solid-state Li-ion batteries: Impact of sintering temperature and Al³⁺ doping

ScienceDirect Publication: Solid State Ionics — Thu, 15 Jan 2026 18:35:51 GMT

Publication date: 1 March 2026

Source: Solid State Ionics, Volume 436

Author(s): Sumit Khatua, K. Ramakrushna Achary, K. Sasikumar, Lakshmi Hrushita Korlapati, L.N. Patro

[ScienceDirect Publication: Nano Energy] Magnetic–Current Coupling Matched with Pore Geometry Boosts Ion Transport in LiFePO₄ Cathodes

ScienceDirect Publication: Nano Energy — Thu, 15 Jan 2026 18:35:42 GMT

Publication date: Available online 14 January 2026

Source: Nano Energy

Author(s): Yue Li, Jiabao Sun, Jianxin Deng, Rui Zhang, Ning Wang, Xingai Wang, Lei Wang, Qiyu Wang, Haichang Zhang, Fei Ding

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Predicting Activation Energy of Hydrocarbon Dehydrogenation on Au(111) via Machine Learning

The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications) — Thu, 15 Jan 2026 12:50:31 GMT

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03941

[cond-mat updates on arXiv.org] Emergent chiral Higgs mode in $\pi$-flux frustrated lattices

cond-mat updates on arXiv.org — Fri, 16 Jan 2026 05:00:00 GMT

arXiv:2601.09980v1 Announce Type: new +Abstract: In this work we explore the performance and behavior of reasoning large language models to autonomously optimize atomic layer deposition (ALD) processes. In the ALD process optimization task, an agent built on top of a reasoning LLM has to find optimal dose times for an ALD precursor and a coreactant without any prior knowledge on the process, including whether it is actually self-limited. The agent is meant to interact iteratively with an ALD reactor in a fully unsupervised way. We evaluate this agent using a simple model of an ALD tool that incorporates ALD processes with different self-limited surface reaction pathways as well as a non self-limited component. Our results show that agents based on reasoning models like OpenAI's o3 and GPT5 consistently succeeded at completing this optimization task. However, we observed significant run-to-run variability due to the non deterministic nature of the model's response. In order to understand the logic followed by the reasoning model, the agent uses a two step process in which the model first generates an open response detailing the reasoning process. This response is then transformed into a structured output. An analysis of these reasoning traces showed that the logic of the model was sound and that its reasoning was based on the notions of self-limited process and saturation expected in the case of ALD. However, the agent can sometimes be misled by its own prior choices when exploring the optimization space.

[cond-mat updates on arXiv.org] Advanced Manufacturing with Renewable and Bio-based Materials: AI/ML workflows and Process Optimization

cond-mat updates on arXiv.org — Fri, 16 Jan 2026 05:00:00 GMT

arXiv:2601.10382v1 Announce Type: new +Abstract: Advanced manufacturing with new bio-derived materials can be achieved faster and more economically with first-principle-based artificial intelligence and machine learning (AI/ML)-derived models and process optimization. Not only is this motivated by increased industry profitability, but it can also be optimized to reduce waste generation, energy consumption, and gas emissions through additive manufacturing (AM) and AI/ML-directed self-driving laboratory (SDL) process optimization. From this perspective, the benefits of using 3D printing technology to manufacture durable, sustainable materials will enable high-value reuse and promote a better circular economy. Using AI/ML workflows at different levels, it is possible to optimize the synthesis and adaptation of new bio-derived materials with self-correcting 3D printing methods, and in-situ characterization. Working with training data and hypotheses derived from Large Language Models (LLMs) and algorithms, including ML-optimized simulation, it is possible to demonstrate more field convergence. The combination of SDL and AI/ML Workflows can be the norm for improved use of biobased and renewable materials towards advanced manufacturing. This should result in faster and better structure, composition, processing, and properties (SCPP) correlation. More agentic AI tasks, as well as supervised or unsupervised learning, can be incorporated to improve optimization protocols continuously. Deep Learning (DL), Reinforcement Learning (RL), and Deep Reinforcement Learning (DRL) with Deep Neural Networks (DNNs) can be applied to more generative AI directions in both AM and SDL, with bio-based materials.

[cond-mat updates on arXiv.org] A Generalizable Framework for Building Executable Domain-Specific LLMs under Data Scarcity: Demonstration on Semiconductor TCAD Simulation

cond-mat updates on arXiv.org — Fri, 16 Jan 2026 05:00:00 GMT

arXiv:2601.10128v1 Announce Type: cross +Abstract: Scientific and engineering verticals often suffer from data scarcity and strict executability requirements: models must generate not only fluent text, but also syntactically valid, tool-compilable scripts. We present a schema-first alignment framework for building compact, executable domain-specific LLMs in low-resource settings. The framework integrates three core components: (i) large-scale synthetic QA data generation from expert documentation to instill foundational domain knowledge; (ii) a code-centric IR->DPO workflow that converts verified tool decks into interpretable intermediate representations (IR), performs equivalence-preserving diversification, and constructs preference pairs to directly optimize instruction compliance and code executability; and (iii) a controlled evaluation of Retrieval-Augmented Generation (RAG), showing that while RAG benefits general LLMs, it can marginally degrade the performance of already domain-aligned models. + We demonstrate the framework by instantiating TcadGPT for semiconductor Technology Computer-Aided Design (TCAD). Using 1.5M synthetic QA pairs and an IR-driven DPO dataset, TcadGPT attains 85.6% semantic accuracy and an 80.0% syntax pass rate on SDE executability tests, substantially outperforming state-of-the-art general LLMs such as GPT-4o. To probe portability beyond TCAD, we apply the same recipe to the open-source FEM solver Elmer, observing consistent improvements in script-level success rates over general-purpose baselines. All datasets, benchmarks, and code (including P1, P2, and IR->DPO) are released for reproducibility. Together, these results suggest that the proposed framework provides a robust and reproducible path toward executable LLMs in specialized, data-scarce professional domains.

[cond-mat updates on arXiv.org] Molecular electrostatic potentials from machine learning models for dipole and quadrupole predictions

cond-mat updates on arXiv.org — Fri, 16 Jan 2026 05:00:00 GMT

arXiv:2601.10320v1 Announce Type: cross +Abstract: The molecular electrostatic potential (MEP) is a key quantity for describing and predicting intermolecular and ion-molecule interactions. Here, we assess the ability of machine-learning (ML) models to infer the MEP, based on the equivariant graph-convolutional neural network architecture PiNet2 and trained on dipole and quadrupole moments. For the established QM9 dataset, we find that including the quadrupole contribution in the ML models substantially improves their ability to recover the MEP compared to dipole-only models. This trend is confirmed on the SPICE dataset, which spans a much broader region of organic chemical space. Together, this study underscores the central role of the quadrupole moment as a fitting target for ML models aiming at rapid access to the MEP.

[cond-mat updates on arXiv.org] Interfacial Behavior from the Atomic Blueprint: Machine Learning-Guided Design of Spatially Functionalized a-SiO2 Surfaces

cond-mat updates on arXiv.org — Fri, 16 Jan 2026 05:00:00 GMT

arXiv:2504.20929v2 Announce Type: replace +Abstract: a-Quartz surfaces functionalized with hydroxyl and methyl groups provide a versatile platform for controlling interfacial properties critical to applications such as catalysis, protective coatings, and energy conversion. The arrangement of these functional groups strongly influences interfacial interactions at solid-liquid interfaces, highlighting their relevance to colloid and interface science. However, conventional models often treat surface functionalization as spatially homogeneous, overlooking the atomic-scale organization of surface groups. We hypothesize that this spatial distribution, beyond overall composition, plays a decisive role in governing surface stability and interfacial behavior. + To test this hypothesis, we employ a multi-scale simulation workflow combining density functional theory, ab initio molecular dynamics (AIMD), and machine-learned force fields (MLFFs). This approach allows us to explore a range of spatial patterns of OH/CH3 functionalization on the a-quartz (0001) surface. We evaluate the impact of spatial arrangements on mixing energy, hydrogen bonding networks, and vibrational properties with high accuracy and robustness. + Our results reveal that spatial patterning strongly influences surface stability and interfacial structure. A thermodynamically favored unpaired configuration emerges near 67 % CH3 substitution, where isolated OH groups form secondary hydrogen bonds through reorientation toward subsurface oxygen atoms. This rearrangement induces a characteristic blue shift in OH stretching frequencies, indicating weaker H-bonding. These effects are absent in clustered arrangements. By establishing a clear link between functional group patterning and interfacial behavior, our work uncovers the underlying mechanisms to guide and accelerate the rational design of silica-based materials and coatings, directly relevant to colloid and interface science.

[cond-mat updates on arXiv.org] Discovering quasiorder parameters in the Potts model: A bridge between machine learning and critical phenomena

cond-mat updates on arXiv.org — Fri, 16 Jan 2026 05:00:00 GMT

arXiv:2505.06159v2 Announce Type: replace +Abstract: Machine-learning (ML) models trained on Ising spin configurations have demonstrated surprising effectiveness in classifying phases of Potts models, even when processing severely reduced representations that retain only two spin states. To unravel this remarkable capability, we identify a family of alternative order parameters for the $q=3$ and $q=4$ Potts models on a square lattice, constructed from the occupancies of secondary and minimal spin states rather than the conventional dominant-state order parameter. Through systematic finite-size scaling analyses, we demonstrate that these quantities, along with a magnetization-like quantity derived from a reduced spin representation, accurately capture critical behavior, yielding critical temperatures and exponents consistent with established theoretical predictions and numerical benchmarks. Furthermore, we rigorously establish the fundamental relationships between these alternative (quasi)order parameters, demonstrating how they collectively encode criticality through different aspects of spin configurations. Our results clarify, within this specific setting, how reduced spin representations can retain the essential thermodynamic information needed for identifying critical behavior. Taken together, this work establishes a concrete bridge between Ising-trained ML models and critical phenomena in Potts systems by showing that Potts criticality can be encoded in more compact, non-traditional forms, thereby opening avenues for discovering analogous order parameters in broader spin systems.

[cond-mat updates on arXiv.org] Influence of Exchange-Correlation Functionals and Neural Network Architectures on Li$^+$-Ion Conductivity in Solid-State Electrolyte from Molecular Dynamics Simulations with Machine-Learning Force Fields

cond-mat updates on arXiv.org — Fri, 16 Jan 2026 05:00:00 GMT

arXiv:2512.11650v2 Announce Type: replace +Abstract: With the rapid advancement of machine learning techniques for materials simulations, machine-learned force fields (MLFFs) have become a powerful tool that complements first-principles calculations by enabling high-accuracy molecular dynamics simulations over extended timescales. Typically, MLFFs are trained on data generated from density functional theory (DFT) using a specific exchange-correlation (XC) functional, with the goal of reproducing DFT-level properties. However, the uncertainties in MLFF-based simulations--arising from variations in both MLFF model architectures and the choice of XC functionals--remain insufficiently understood. In this work, we construct MLFF models of different architectures trained on DFT data from both semilocal and hybrid functionals to describe Li$^+$ diffusion in the solid-state electrolyte Li$_6$PS$_5$Cl. We systematically investigate how different XC functionals influence the Li$^+$ diffusion coefficient. To reduce statistical uncertainty, the mean squared displacements are averaged over 300 independent molecular dynamics (MD) trajectories of 70 ps each, yielding statistical variations below $1\%$. This enables a clear assessment of the respective influences of the functional and the MLFF model. Due to its tendency to underestimate band gaps and migration barriers, the semilocal functional predicts consistently higher Li$^+$ diffusion coefficients, compared to the hybrid functional. Furthermore, comparisons among various neural network methods reveal that the differences in predicted diffusion coefficients arising from different network architectures are of the same order of magnitude as those caused by different functionals, indicating that the choice of the network model itself substantially influences the MLFF predictions. This observation calls from an urgent need for standardized protocols to minimize model-dependent biases in MLFF-based MD.

[cond-mat updates on arXiv.org] A survey of active learning in materials science: Data-driven paradigm for accelerating the research pipeline

cond-mat updates on arXiv.org — Fri, 16 Jan 2026 05:00:00 GMT

arXiv:2601.06971v2 Announce Type: replace +Abstract: The exploration of materials composition, structure, and processing spaces is constrained by high dimensionality and the cost of data acquisition. While machine learning has supported property prediction and design, its effectiveness depends on labeled data, which remains expensive to generate via experiments or high-fidelity simulations. Improving data efficiency is thus a central concern in materials informatics. + Active learning (AL) addresses this by coupling model training with adaptive data acquisition. Instead of static datasets, AL iteratively prioritizes candidates based on uncertainty, diversity, or task-specific objectives. By guiding data collection under limited budgets, AL offers a structured approach to decision-making, complementing physical insight with quantitative measures of informativeness. + Recently, AL has been applied to computational simulation, structure optimization, and autonomous experimentation. However, the diversity of AL formulations has led to fragmented methodologies and inconsistent assessments. This Review provides a concise overview of AL methods in materials science, focusing on their role in improving data efficiency under realistic constraints. We summarize key methodological principles, representative applications, and persistent challenges, aiming to clarify the scope and limitations of AL as a practical tool within contemporary materials informatics.

[cond-mat updates on arXiv.org] Agentic AI and Machine Learning for Accelerated Materials Discovery and Applications

cond-mat updates on arXiv.org — Fri, 16 Jan 2026 05:00:00 GMT

arXiv:2601.09027v2 Announce Type: replace +Abstract: Artificial Intelligence (AI), especially AI agents, is increasingly being applied to chemistry, healthcare, and manufacturing to enhance productivity. In this review, we discuss the progress of AI and agentic AI in areas related to, and beyond polymer materials and discovery chemistry. More specifically, the focus is on the need for efficient discovery, core concepts, and large language models. Consequently, applications are showcased in scenarios such as (1) flow chemistry, (2) biosensors, and (3) batteries.

[cond-mat updates on arXiv.org] Revisiting Jahn--Teller Transitions in Correlated Oxides with Monte Carlo Modeling

cond-mat updates on arXiv.org — Fri, 16 Jan 2026 05:00:00 GMT

arXiv:2601.09705v2 Announce Type: replace +Abstract: Jahn--Teller (JT) distortions are a key driver of physical properties in many correlated oxide materials. Cooperative JT distortions, in which long-range orbital order reduces the symmetry of the average structure macroscopically, are common in JT-distorted materials at low temperatures. This long-range order will often melt on heating, \textit{via} a transition to a high-temperature state without long-range orbital order. The nature of this transition has been observed to vary with different materials depending on crystal structure; in LaMnO$_3$ the transition has generally been interpreted as order-disorder, whereas in layered nickelates $A$NiO$_2$ ($A$=Li,Na) there is a displacive transition. Alternatively, recent theoretical work has suggested that previous attributions of order-disorder may in fact be a consequence of phonon anharmonicity, rather than persistence of JT distortions, which would suggest that the displacive transition may be more common than currently believed. In this work, we run Monte Carlo simulations with a simple Hamiltonian which is modified to include terms dependent on the JT amplitude $\rho$, which is allowed to vary within the simulation \textit{via} the Metropolis algorithm. Our simulations yield distributions of JT amplitudes consistent with displacive rather than order-disorder behaviour for both perovskites and layered nickelates, suggesting that displacive-like JT transitions may be more common than previously assumed in both perovskites and layered nickelates. We also find significant differences between the transition observed for perovskites compared with layered nickelates, which we attribute to differing extensivity of configurational entropy on the two lattices, showing the crucial role of lattice geometry in determining behaviour.

[cond-mat updates on arXiv.org] An information-matching approach to optimal experimental design and active learning

cond-mat updates on arXiv.org — Fri, 16 Jan 2026 05:00:00 GMT

arXiv:2411.02740v5 Announce Type: replace-cross +Abstract: The efficacy of mathematical models heavily depends on the quality of the training data, yet collecting sufficient data is often expensive and challenging. Many modeling applications require inferring parameters only as a means to predict other quantities of interest (QoI). Because models often contain many unidentifiable (sloppy) parameters, QoIs often depend on a relatively small number of parameter combinations. Therefore, we introduce an information-matching criterion based on the Fisher Information Matrix to select the most informative training data from a candidate pool. This method ensures that the selected data contain sufficient information to learn only those parameters that are needed to constrain downstream QoIs. It is formulated as a convex optimization problem, making it scalable to large models and datasets. We demonstrate the effectiveness of this approach across various modeling problems in diverse scientific fields, including power systems and underwater acoustics. Finally, we use information-matching as a query function within an Active Learning loop for material science applications. In all these applications, we find that a relatively small set of optimal training data can provide the necessary information for achieving precise predictions. These results are encouraging for diverse future applications, particularly active learning in large machine learning models.

[cond-mat updates on arXiv.org] Quantum circuit complexity and unsupervised machine learning of topological order

cond-mat updates on arXiv.org — Fri, 16 Jan 2026 05:00:00 GMT

arXiv:2508.04486v2 Announce Type: replace-cross +Abstract: Inspired by the close relationship between Kolmogorov complexity and unsupervised machine learning, we explore quantum circuit complexity, an important concept in quantum computation and quantum information science, as a pivot to understand and to build interpretable and efficient unsupervised machine learning for topological order in quantum many-body systems. We argue that Nielsen's quantum circuit complexity represents an intrinsic topological distance between topological quantum many-body phases of matter, and as such plays a central role in interpretable manifold learning of topological order. To span a bridge from conceptual power to practical applicability, we present two theorems that connect Nielsen's quantum circuit complexity for the quantum path planning between two arbitrary quantum many-body states with quantum Fisher complexity (Bures distance) and entanglement generation, respectively. Leveraging these connections, fidelity-based and entanglement-based similarity measures or kernels, which are more practical for implementation, are formulated. Using the two proposed distance measures, unsupervised manifold learning of quantum phases of the bond-alternating XXZ spin chain, the ground state of Kitaev's toric code and random product states, is conducted, demonstrating their superior performance. Moreover, we find that the entanglement-based approach, which captures the long-range structure of quantum entanglement of topological orders, is more robust to local Haar random noises. Relations with classical shadow tomography and shadow kernel learning are also discussed, where the latter can be naturally understood from our approach. Our results establish connections between key concepts and tools of quantum circuit computation, quantum complexity, quantum metrology, and machine learning of topological quantum order.

[ChemRxiv] YBi₂O_3.5+δSe_1-xCl_x: A family of semiconductors with a charge- and defect-tunable triple fluorite layer

ChemRxiv — Fri, 16 Jan 2026 00:00:00 GMT

Abstract: The design and development of new materials is crucial for advanced next-generation technologies, necessitating the modification of materials with tuneable atomic and electronic frameworks. This is often achieved by addition or replacement of metals and cations. Substitution of anions is also possible; here, we present the YBi₂O_3.5+δSe_1-xCl_x family which represent a new class of layered mixed anionic semiconductor materials with complete substitution of Cl by Se charge balanced by the introduction of oxide vacancies. The introduction of Se anions into the visible band gap material YBi₂O₄Cl shifts the band gap from 2.4 to 1.2 eV. This coupled Se/Cl substitution with O vacancy generation enables defect-engineered, tuneable new layered materials with tailored electronic properties in the visible band gap region, opening new pathways for development of semiconductors, thermoelectrics, and mixed oxide ion conductors

[ChemRxiv] K5Ir: Reduced Iridium Stabilized in a High-Pressure Semimetal

ChemRxiv — Fri, 16 Jan 2026 00:00:00 GMT

Alkali binary compounds offer a way to expand our understanding of the periodic table. Specifically, the redox inert nature of these cations, even within some intermetallic compounds, enables one to access exotic oxidation states. Prior work on semiconducting alkali aurides(I-) and platinides(II-) containing transition metal anions stimulated theoretical predictions of the monatomic iridide(III-) anion by reduction of iridium with alkali metals under pressure. We tested these predictions by reacting a K-rich mixture of K and Ir at 19.5(6) GPa and 493 K. This reaction yields K5Ir, which adopts the rare but simple BaSn5 crystal structure. Hybrid functional electronic structure calculations, net atomic charge analysis, and Ir L3-edge X-ray absorption spectroscopy reveal K5Ir is a semimetal with a carrier density ~10^20 cm^−3 which features anionic Ir and both cationic and neutral K on different sites. While the net atomic charge of Ir in K5Ir falls short of that in hypothetical, semiconducting K3Ir, it exceeds those of Pt(II-) in Cs2Pt and Ir(III-) in [Ir(CO)3]^3−, suggesting an extreme for the distribution of charge in the vicinity of a transition metal. First-principles crystal structure prediction corroborates the thermodynamic stability of K5Ir under the preparatory conditions and indicates that several other K−Ir compounds await discovery.

[ChemRxiv] Nanopore sequencing with proteins: synchronization and dischronization of molecular dynamics simulations with laboratory and industrial developments

ChemRxiv — Fri, 16 Jan 2026 00:00:00 GMT

Protein nanopores have revolutionized DNA sequencing by enabling long-read, real-time, and portable genomic analysis. This review traces the experimental evolution of three key protein nanopores—α-hemolysin, MspA, and CsgG—highlighting how iterative engineering overcame challenges such as translocation control and homopolymer resolution. Concurrently, molecular dynamics (MD) simulations have elucidated DNA–pore interactions, ion current modulation, free-energy landscapes, and so forth, providing mechanistic insights and guiding rational design. However, MD studies consistently lag behind experimental and industrial advances, resulting in a reactive “simulate-after-validate” paradigm. We identify critical gaps in simulating motor–pore complexes, experimental timescales, and emerging designs like dual-constriction pores. To bridge these, we propose leveraging deep learning-based structure prediction, de novo protein design, and advanced multiscale simulations to foster proactive, integrated development of next-generation nanopore technologies.

[ChemRxiv] Surface Segregation in Compositionally Complex Ag-Au-Pd-Pt Solid Solutions: Insights from High-Throughput Experimentation and Atomistic Simulations

ChemRxiv — Fri, 16 Jan 2026 00:00:00 GMT

The activity of compositionally complex electrocatalysts depends on their surface composition, which can be different from the volume composition. In solid solutions, surface segregation under vacuum can be estimated based on the surface energy of the constituent elements. Upon exposure to ambient conditions, the surface reactivity of the elements, particularly their tendency to oxidize, is also important. Here, we investigate differences between the surface and volume composition of a model noble metal system, Ag-Au-Pd-Pt, fabricated by co-sputter deposition in the form of thin-film materials libraries (MLs), spanning a compositional range of Ag12-55Au7-50Pd6-60Pt7-58. The volume compositions of 684 measurement areas of these libraries were determined with energy dispersive X-ray spectroscopy (EDX). For each library, a set of nine selected areas was additionally measured by X-ray photoelectron spectroscopy (XPS), to determine the surface compositions, i.e. the first few nanometers of the films. The XPS data reveal surface segregation of Ag up to 8 at.% and Pt depletion of similar magnitude. The results were further validated by large-scale molecular dynamics/Monte-Carlo simulations using accurate machine learning interatomic potentials (MLIP), providing theoretical insights of surface segregation under vacuum conditions across the quaternary composition space.

[ChemRxiv] Assignment Promoting Use of Generative AI as an Active Learning Tool in General Chemistry

ChemRxiv — Fri, 16 Jan 2026 00:00:00 GMT

The value of generative artificial intelligence (AI) for teaching and learning is currently hotly debated. Concerns regarding the accuracy of information produced by generative AI as well as student over-reliance on this tool coexist with excitement about tailored opportunities that AI may provide for educational purposes. Student ability to responsibly utilize AI is growing into a key competency that we expect can be co-taught with domain course contents. Driven by this idea, we developed an assignment for first-year undergraduate students in a general chemistry course with the aim to promote the use of AI as an active learning tool. By having students judge AI responses to advanced chemistry-related questions, this assignment is designed to promote the assessment of information accuracy and combat over-reliance. Here, we detail the assignment and report the aggregate student responses that we received while implementing this assignment in our course.

[ChemRxiv] Auditing Domain Alignment in Latent Representations for Cross-Domain Materials Machine Learning

ChemRxiv — Fri, 16 Jan 2026 00:00:00 GMT

Machine learning models are increasingly applied to heterogeneous materials datasets spanning different synthesis routes, measurement protocols, and structural classes. Although multi-task and representation-learning approaches are commonly used to improve predictive performance, the latent representations learned by such models are rarely examined directly, allowing domain misalignment to remain hidden until model transfer fails. Here, we present a practical, representation-level framework for auditing domain alignment in latent spaces learned by multi-task neural networks for materials property prediction. Using porous carbon materials and metal–organic frameworks as a representative cross-domain case study, we analyze a shared latent space trained across multiple target properties and regularization strategies. Global and local dimensionality-reduction visualizations are combined with quantitative measures of domain separability and latent–target correlation structure to assess alignment quality. We find that domain-alignment objectives induce measurable geometric reorganization and partial large-scale mixing in latent space, while domain identity remains locally recoverable and physically interpretable monotonic associations between latent dimensions and target properties are preserved. These diagnostics reveal structural differences in learned representations that are not apparent from conventional performance metrics alone and enable model comparison based on latent-space geometry rather than predictive accuracy. The proposed auditing framework is model-agnostic and can be readily integrated into existing materials machine learning workflows, providing a practical tool for improving robustness and interpretability in cross-domain materials modeling.

[ChemRxiv] Asymmetric Multi-Site Ion Exchange in Porous Carbon Electrodes

ChemRxiv — Fri, 16 Jan 2026 00:00:00 GMT

Ion transport in porous carbon electrodes underpins the performance of electrochemical energy-storage and separation technologies, yet exchange dynamics within heterogeneous pore networks remain difficult to quantify. Here, we present a rigorous analytical framework for extracting quantitative ion-exchange kinetics in confined electrolytes from two-dimensional exchange spectroscopy (2D EXSY) NMR, explicitly accounting for asymmetric ion populations, relaxation effects, and microscopic reversibility. By describing exchange with a lognormal distribution of rates, the framework captures the intrinsic heterogeneity of ion motion and overcomes the limitations of conventional discrete-site models. Applied to aqueous LiTFSI electrolytes confined in mesoporous CMK-3 and hierarchically structured ST-CMK-3 carbons, the method resolves a hierarchy of ion-exchange processes spanning fast near-surface exchange to slow in-pore diffusion in two and three-site exchange systems. We report the first observation of three-site ion exchange in porous electrodes, revealing enhanced directional ion mobility and higher effective diffusion coefficients at interconnected micro–mesopore boundaries in hierarchical carbons. These findings establish direct, quantitative links between pore architecture and ion-transport efficiency. This distribution-based framework provides a generalizable route to identifying rate-limiting transport pathways in complex porous energy materials and offers design principles for optimizing porous carbons for high-rate supercapacitors, capacitive deionization, and gas-storage applications.

[ScienceDirect Publication: Solid State Ionics] Engineering dense superionic Li₁₊ₓAlₓTi₂₋ₓ(PO₄)₃ solid electrolytes for safer solid-state Li-ion batteries: Impact of sintering temperature and Al³⁺ doping

ScienceDirect Publication: Solid State Ionics — Thu, 15 Jan 2026 18:35:51 GMT

Publication date: 1 March 2026

Source: Solid State Ionics, Volume 436

Author(s): Sumit Khatua, K. Ramakrushna Achary, K. Sasikumar, Lakshmi Hrushita Korlapati, L.N. Patro

[ScienceDirect Publication: Nano Energy] Magnetic–Current Coupling Matched with Pore Geometry Boosts Ion Transport in LiFePO₄ Cathodes

ScienceDirect Publication: Nano Energy — Thu, 15 Jan 2026 18:35:42 GMT

Publication date: Available online 14 January 2026

Source: Nano Energy

Author(s): Yue Li, Jiabao Sun, Jianxin Deng, Rui Zhang, Ning Wang, Xingai Wang, Lei Wang, Qiyu Wang, Haichang Zhang, Fei Ding

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Predicting Activation Energy of Hydrocarbon Dehydrogenation on Au(111) via Machine Learning

The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications) — Thu, 15 Jan 2026 12:50:31 GMT

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03941

[Recent Articles in Phys. Rev. Lett.] Sub-Doppler Cooling of a Trapped Ion in a Phase-Stable Polarization Gradient

Recent Articles in Phys. Rev. Lett. — Thu, 15 Jan 2026 10:00:00 GMT

Author(s): Ethan Clements, Felix W. Knollmann, Sabrina Corsetti, Zhaoyi Li, Ashton Hattori, Milica Notaros, Reuel Swint, Tal Sneh, May E. Kim, Aaron D. Leu, Patrick Callahan, Thomas Mahony, Gavin N. West, Cheryl Sorace-Agaskar, Dave Kharas, Robert McConnell, Colin D. Bruzewicz, Isaac L. Chuang, Jelena Notaros, and John Chiaverini

Trapped ions provide a highly controlled platform for quantum sensors, clocks, simulators, and computers, all of which depend on cooling ions close to their motional ground state. Existing methods like Doppler, resolved sideband, and dark resonance cooling balance trade-offs between the final temper…

[Phys. Rev. Lett. 136, 023201] Published Thu Jan 15, 2026

[cond-mat updates on arXiv.org] Emergent chiral Higgs mode in $\pi$-flux frustrated lattices

cond-mat updates on arXiv.org — Thu, 15 Jan 2026 05:00:00 GMT

arXiv:2601.08925v1 Announce Type: new Abstract: Neutral-atom quantum simulators provide a powerful platform for realizing strongly correlated phases, enabling access to dynamical signatures of quasiparticles and symmetry breaking processes. Motivated by recent observations of quantum phases in flux-frustrated ladders with non-vanishing ground state currents, we investigate interacting bosons on the dimerized BBH lattice in two dimensions-originally introduced in the context of higher-order topology. After mapping out the phase diagram, which includes vortex superfluid (V-SF), vortex Mott insulator (V-MI), and featureless Mott insulator (MI) phases, we focus on the integer filling case. There, the MI/V-SF transition simultaneously breaks the $\mathbb Z_2^{T}$ and U(1) symmetries, where $\mathbb Z_2^{T}$ corresponds to time-reversal symmetry (TRS). Using a slave-boson description, we resolve the excitation spectrum across the transition and uncover a chiral Higgs mode whose mass softens at criticality, providing a dynamical hallmark of emergent chirality that we numerically probe via quench dynamics. Our results establish an experimentally realistic setting for probing unconventional TRS-broken phases and quasiparticles with intrinsic chirality in strongly interacting quantum matter.

[cond-mat updates on arXiv.org] Machine Learning-Driven Creep Law Discovery Across Alloy Compositional Space

cond-mat updates on arXiv.org — Thu, 15 Jan 2026 05:00:00 GMT

arXiv:2601.08970v1 Announce Type: new Abstract: Hihg-temperature creep characterization of structural alloys traditionally relies on serial uniaxial tests, which are highly inefficient for exploring the large search space of alloy compositions and for material discovery. Here, we introduce a machine-learning-assisted, high-throughput framework for creep law identification based on a dimple array bulge instrument (DABI) configuration, which enables parallel creep testing of 25 dimples, each fabricated from a different alloy, in a single experiment. Full-field surface displacements of dimples undergoing time-dependent creep-induced bulging under inert gas pressure are measured by 3D digital image correlation. We train a recurrent neural network (RNN) as a surrogate model, mapping creep parameters and loading conditions to the time-dependent deformation response of DABI. Coupling this surrogate with a particle swarm optimization scheme enables rapid and global inverse identification with sparsity regularization of creep parameters from experiment displacement-time histories. In addition, we propose a phenomenological creep law with a time-dependent stress exponent that captures the sigmoidal primary creep observed in wrought INCONEL 625 and extracts its temperature dependence from DABI test at multiple temperatures. Furthermore, we employ a general creep law combining several conventional forms together with regularized inversion to identify the creep laws for 47 additional Fe-, Ni-, and Co-rich alloys and to automatically select the dominant functional form for each alloy. This workflow combined with DABI experiment provides a quantitative, high-throughput creep characterization platform that is compatible with data mining, composition-property modeling, and nonlinear structural optimization with creep behavior across a large alloy design space.

[cond-mat updates on arXiv.org] Agentic AI and Machine Learning for Accelerated Materials Discovery and Applications

cond-mat updates on arXiv.org — Thu, 15 Jan 2026 05:00:00 GMT

arXiv:2601.09027v1 Announce Type: new Abstract: Artificial Intelligence (AI), especially AI agents, is increasingly being applied to chemistry, healthcare, and manufacturing to enhance productivity. In this review, we discuss the progress of AI and agentic AI in areas related to, and beyond polymer materials and discovery chemistry. More specifically, the focus is on the need for efficient discovery, core concepts, and large language models. Consequently, applications are showcased in scenarios such as (1) flow chemistry, (2) biosensors, and (3) batteries.

[cond-mat updates on arXiv.org] Data-Driven Exploration and Insights into Temperature-Dependent Phonons in Inorganic Materials

cond-mat updates on arXiv.org — Thu, 15 Jan 2026 05:00:00 GMT

arXiv:2601.09123v1 Announce Type: new @@ -10,7 +27,7 @@ Abstract: Metal-organic frameworks (MOFs) are porous crystalline materials with Abstract: Many factors in perovskite X-ray detectors, such as crystal lattice and carrier dynamics, determine the final device performance (e.g., sensitivity and detection limit). However, the relationship between these factors remains unknown due to the complexity of the material. In this study, we employ machine learning to reveal the relationship between 15 intrinsic properties of halide perovskite materials and their device performance. We construct a database of X-ray detectors for the training of machine learning. The results show that the band gap is mainly influenced by the atomic number of the B-site metal, and the lattice length parameter b has the greatest impact on the carrier mobility-lifetime product ({\mu}{\tau}). An X-ray detector (m-F-PEA)2PbI4 were generated in the experiment and it further verified the accuracy of our ML models. We suggest further study on random forest regression for X-ray detector applications.

[cond-mat updates on arXiv.org] Sharp spectroscopic fingerprints of disorder in an incompressible magnetic state

cond-mat updates on arXiv.org — Thu, 15 Jan 2026 05:00:00 GMT

arXiv:2506.08112v2 Announce Type: replace Abstract: Disorder significantly impacts the electronic properties of conducting quantum materials by inducing electron localization and thus altering the local density of states and electric transport. In insulating quantum magnetic materials, the effects of disorder are less understood and can drastically impact fluctuating spin states like quantum spin liquids. In the absence of transport tools, disorder is typically characterized using chemical methods or by semi-classical modeling of spin dynamics. This requires high magnetic fields that may not always be accessible. Here, we show that magnetization plateaus -- incompressible states found in many quantum magnets -- provide an exquisite platform to uncover small amounts of disorder, regardless of the origin of the plateau. Using optical magneto-spectroscopy on the Ising-Heisenberg triangular-lattice antiferromagnet K$_2$Co(SeO$_3$)$_2$ exhibiting a 1/3 magnetization plateau, we identify sharp spectroscopic lines, the fine structure of which serves as a hallmark signature of disorder. Through analytical and numerical modeling, we show that these fingerprints not only enable us to quantify minute amounts of disorder but also reveal its nature -- as dilute vacancies. Remarkably, this model explains all details of the thermomagnetic response of our system, including the existence of multiple plateaus. Our findings provide a new approach to identifying disorder in quantum magnets.

[cond-mat updates on arXiv.org] Data-Driven Review and Machine Learning Prediction of Diamond Vacancy Center Synthesis

cond-mat updates on arXiv.org — Thu, 15 Jan 2026 05:00:00 GMT

arXiv:2507.02808v2 Announce Type: replace Abstract: Diamond and diamond color centers have become prime hardware candidates for solid state-based technologies in quantum information and computing, optics, photonics and (bio)sensing. The synthesis of diamond materials with specific characteristics and the precise control of the hosted color centers is thus essential to meet the demands of advanced applications. Yet, challenges remain in improving the concentration, uniform distribution and quality of these centers. Here, we perform a review and meta-analysis of some of the main diamond synthesis methods and their parameters for the synthesis of N-, Si-, Ge- and Sn-vacancy color-centers. We extract quantitative data from over 60 experimental papers and organize it in a large database (170 data sets and 1692 entries). We then use the database to train two machine learning algorithms to make robust predictions about the fabrication of diamond materials with specific properties from careful combinations of synthesis parameters. We use traditional statistical indicators to benchmark the performance of the algorithms and show that they are powerful and resource-efficient tools for researchers and material scientists working with diamond color centers and their applications.

[cond-mat updates on arXiv.org] Investigating Anharmonicities in Polarization-Orientation Raman Spectra of Acene Crystals with Machine Learning

cond-mat updates on arXiv.org — Thu, 15 Jan 2026 05:00:00 GMT

arXiv:2510.04843v2 Announce Type: replace -Abstract: We present a first-principles machine-learning computational framework to investigate anharmonic effects in polarization-orientation (PO) Raman spectra of molecular crystals, focusing on anthracene and naphthalene. By combining machine learning models for interatomic potentials and polarizability tensors, we enable efficient, large-scale simulations that capture temperature-dependent vibrational dynamics beyond the harmonic approximation. Our approach reproduces key qualitative features observed experimentally. We show, systematically, what are the fingerprints of anharmonic lattice dynamics, thermal expansion, and Raman tensor symmetries on PO-Raman intensities. However, we find that the simulated polarization dependence of Raman intensities shows only subtle deviations from quasi-harmonic predictions, failing to capture the pronounced temperature-dependent changes that have been reported experimentally in anthracene. We propose that part of these inconsistencies stem from the impossibility to deconvolute certain vibrational peaks when only experimental data is available. This work therefore provides a foundation to improve the interpretation of PO-Raman experiments in complex molecular crystals with the aid of theoretical simulations.

[ScienceDirect Publication: Journal of Energy Storage] Design of corrosion resistant Ce-enhanced hybrid solid electrolyte interphase by Ce(TFSI)₃ additives for lithium metal batteries

ScienceDirect Publication: Journal of Energy Storage — Thu, 15 Jan 2026 01:41:46 GMT

Publication date: 10 March 2026

Source: Journal of Energy Storage, Volume 150

Author(s): Mingdong Du, Yanxia Liu, Chenxing Wang, Fulai Qi, Mingyu Shi, Wenjing Liu, Shengnan He, Zhijun Wu, Zhenglong Li, Chenchen Li, Hongge Pan

[ChemRxiv] Assigning the Stereochemistry of Natural Products by Machine Learning

ChemRxiv — Thu, 15 Jan 2026 00:00:00 GMT

Nature has settled for L-chirality for proteinogenic amino acids and D-chirality for the carbohydrate backbone of nucleotides. Further stereochemical patterns exist among natural products produced by common biosynthetic pathways. Here we asked the question whether these regularities might be sufficiently prevalent among natural products (NPs) such that their stereochemistry could be machine learned and assigned automatically. Indeed, we report that a language model can be trained to assign the stereochemistry of NPs using the open access NP database COCONUT. In detail, our language model, called NPstereo, translates an NP structure written as absolute SMILES into the corresponding isomeric SMILES notation containing stereochemical information, with 80.2% per-stereocenter accuracy for full assignments and 85.9% per-stereocenter accuracy for partial assignments, across various NP classes including secondary metabolites such as alkaloids, polyketides, lipids and terpenes. NPstereo might be useful to assign or correct the stereochemistry of newly discovered NPs. Scientific contribution: Our study reports that a language model, NPstereo, can learn and predict the stereochemistry of natural products (NPs) from their 2D structures with high accuracy. This work demonstrates that NP stereochemical patterns are machine learnable from data and represents a first step for scalable computational methodologies for stereochemical assignment of newly discovered NPs.

[ChemRxiv] Advancing Calcium-Ion Batteries with a Novel Hydrated Eutectic Electrolyte

ChemRxiv — Thu, 15 Jan 2026 00:00:00 GMT

Ca-ion batteries (CIBs) have garnered significant attention in the past few years by virtue of their economic and physicochemical advantages, positioning them as a promising electrochemical energy storage technology in the post-lithium-ion battery era. However, the current research progress is insufficient, leaving this emerging field far from practical application, most of which focuses on developing high-performance electrode materials. Herein, we design a novel hydrated eutectic electrolyte (HEE) prepared by mixing Ca(ClO4)2·4H2O and acetamide at room temperature. The solvation structure of Ca2+ can be precisely regulated by controlling the molar ratio of the two chemical components. The HEE with optimal formulation features a wide electrochemical stability window, good ionic conductivity, appropriate viscosity, and a low melting point. Employing this novel HEE, a full CIB assembled with a PEDOT/V2O5 cathode and a PTCDI anode demonstrated outstanding electrochemical performance at room temperature (30 mAh·g−1 at 0.5 A·g−1 after 30,000 cycles) and environmental adaptability within a wide operating temperature range (−20 to 60 ℃). This work may not only provide a sustainable, safe, and cost-effective electrolyte for CIBs but also pave the way for the comprehensive development of CIBs.

[ChemRxiv] DFT-guided discovery of bisureido ester of 2-(methylthio)ethanol: A novel lead against triple-negative breast cancer inducing apoptosis via pH-sensitive chloride transport

ChemRxiv — Thu, 15 Jan 2026 00:00:00 GMT

Dysregulation of transmembrane chloride flux has emerged as a promising strategy for inducing cancer-cell death through disruption of intracellular pH gradients and subsequent apoptosis. Herein, we report the design and synthesis of novel ortho-phenylenediamine (o-PDA) bisurea derivatives bearing carboxyl (5a–f) and 2-(methylthio)ethyl ester (6a,b) groups for pH-sensitive anion transport and resultant anticancer activity. Esterification of 5c–f proved synthetically challenging due to an unprecedented CDI-catalyzed intramolecular cyclization that yielded the 4-oxo-benzo[d][1,3,6]oxadiazepines 8a,b, rationalized via a DFT-supported four-step mechanism involving hydrogen-borrowing catalysis. All target compounds demonstrated 1:1 chloride ion-binding as shown by 1H NMR titration results and demonstrated a pH-gradient-induced transmembrane chloride transport (as shown by vesicular transport studies) which was enhanced by electron-withdrawing substituents on the outer phenyl rings. Ester derivatives 6a,b exhibited significantly higher chloride binding and up to ~67-fold increased transport efficiency over their carboxylic acid analogs. Compound 6b emerged as the lead, combining efficient chloride binding (Ka = 163 M⁻¹), potent transport (EC50 = 0.007 mol%), and superior antiproliferative activity (IC50 = 6.34 µM) against MDA-MB-231 cells via apoptosis induction. These findings establish 6b as a promising lead for chloride-transport-mediated cancer therapy, with further optimization recommended via EWG diversification and strategic consideration of the cyclization pathway in retrosynthetic design.

[Nature Nanotechnology] Superionic composite electrolytes with continuously perpendicular-aligned pathways for pressure-less all-solid-state lithium batteries

Nature Nanotechnology — Thu, 15 Jan 2026 00:00:00 GMT

Nature Nanotechnology, Published online: 15 January 2026; doi:10.1038/s41565-025-02106-9

Highly ionically conductive and flexible solid-state composite battery electrolytes are engineered by alternately stacking inorganic LixMyPS3 (M = Cd or Mn) nanosheets with lithium-containing organic polymers in a perpendicular orientation to the surface of the electrodes.

[Nature Communications] Mechanistically guided residual learning for battery state monitoring throughout life

Nature Communications — Thu, 15 Jan 2026 00:00:00 GMT

Nature Communications, Published online: 15 January 2026; doi:10.1038/s41467-025-67565-z

Ensuring accurate state monitoring is vital for battery safety and efficiency in electric vehicles. Here, authors present mechanistically guided residual learners that integrate mechanistic knowledge and machine learning to enable continuous, reliable battery state tracking over the entire lifespan.

[ChemRxiv] Uncertainty Quantification for In Silico Chemistry

ChemRxiv — Thu, 15 Jan 2026 00:00:00 GMT

The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational cost. The resulting abundance of data, generated by quantum chemistry, molecular dynamics sim- ulations, and chemical machine learning natu- rally raises questions about accuracy, precision, and reliability, as well as the systematic treat- ment of errors and uncertainties. Addressing these questions through rigorous mathematical frameworks is at the heart of Uncertainty Quan- tification. In the past years, the incorpora- tion of uncertainty quantification into in silico chemistry has gained attraction, motivated by its ability to provide deeper insights into chem- ical phenomena. In this review, we establish a common language for uncertainty quantifica- tion with respect to in silico chemistry, intro- duce the key mathematical formalisms, and sur- vey the growing body of work that applies un- certainty quantification across different areas of in silico chemistry.

[ChemRxiv] Practical integration of machine learning into ab initio calculations and workflows: Accelerating the SCF cycle via density matrix predictions

ChemRxiv — Thu, 15 Jan 2026 00:00:00 GMT

Data-driven approaches offer great potential for accelerating ab initio electronic structure calculations of molecules and materials but their transferability is often limited due to the vast amount of data needed for training, including when addressing the need to fine-tune universal models for each specific system to be studied. Here, we demonstrate how contributions from system-specific electronic structure machine learning (ESML) models may be combined (“stitched”) to deliver density matrices of entire systems of interest, improving the initial guess for the self-consistent field cycle and delivering gains in computational efficiency. The “stitching” of density matrices is demonstrated for sequential calculations, such as geometry optimization and molecular dynamics, and we show that the synergistic use of ESML models and density matrix extrapolation algorithms can accelerate standard computational calculations. The algorithms are demonstrated for test cases relating to water clusters and a methane clathrate cage, with the benefits discussed. The future opportunities for hybrid quantum mechanical and ML (QM/ML), and also ML/ML paradigms, are broad-ranging with significant computational speed-up attainable.

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Strain-Induced Suppression of Coherent Phonon Transport and Thermoelectric Enhancement in CuBiSeCl2 with Strong Quartic Anharmonicity

The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications) — Wed, 14 Jan 2026 18:58:12 GMT

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03228

[ScienceDirect Publication: eScience] Emerging inorganic amorphous solid-state electrolytes in all-solid-state lithium batteries: From crystallographic order to atomic and lattice disorder

ScienceDirect Publication: eScience — Wed, 14 Jan 2026 18:33:27 GMT

Publication date: Available online 13 January 2026

Source: eScience

Author(s): Yijie Yan, Shuxian Zhang, Xiaoge Man, Qingyu Li, Haoyuan Xue, Peng Xiao, Yuanchang Shi, Longwei Yin, Rutao Wang

[Wiley: Small: Table of Contents] Asymmetric Ion Transport in Tunnel‐Type Cobalt Vanadate for High‐Performance Mn2+/H+ Hybrid Aqueous Batteries

Wiley: Small: Table of Contents — Wed, 14 Jan 2026 14:17:12 GMT

Small, Volume 22, Issue 3, 13 January 2026.

[Wiley: Small: Table of Contents] Machine Learning Accelerated Screening Advanced Single‐Atom Anchored MXenes Electrocatalyst for Hydrogen Evolution Reaction

Wiley: Small: Table of Contents — Wed, 14 Jan 2026 14:17:12 GMT

Small, Volume 22, Issue 3, 13 January 2026.

[ScienceDirect Publication: Materials Today Physics] Defect formation energy of impurities in 2D materials: How does data engineering shape machine learning model selection?

ScienceDirect Publication: Materials Today Physics — Wed, 14 Jan 2026 12:44:11 GMT

Publication date: Available online 13 January 2026

Source: Materials Today Physics

Author(s): A. El Alouani, M. Al Khalfioui, A. Michon, S. Vézian, P. Boucaud, M.T. Dau

[Wiley: Advanced Materials: Table of Contents] 2D Chitin Sub‐Nanosheets with Extreme Ion Transport for Nanofluidic Sensing

Wiley: Advanced Materials: Table of Contents — Wed, 14 Jan 2026 11:46:54 GMT

Advanced Materials, Volume 38, Issue 3, 13 January 2026.

[Wiley: Advanced Intelligent Discovery: Table of Contents] Machine Learning Driven Inverse Design of Broadband Acoustic Superscattering

Wiley: Advanced Intelligent Discovery: Table of Contents — Wed, 14 Jan 2026 11:46:36 GMT

Advanced Intelligent Discovery, EarlyView.

[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Robust LiNi0.6Mn0.4O2 Cathode Achieved from the Dual-Function Strategy of Microstructural Stress Dissipation and Crystalline Phase Ion Transport Improvement

ACS Nano: Latest Articles (ACS Publications) — Wed, 14 Jan 2026 11:35:44 GMT

ACS Nano

DOI: 10.1021/acsnano.5c19029

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Characterizing RNA Tetramer Conformational Landscape Using Explainable Machine Learning

The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications) — Wed, 14 Jan 2026 10:55:20 GMT

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03438

[Recent Articles in Phys. Rev. B] Quantum many-body scarring from Kramers-Wannier duality

Recent Articles in Phys. Rev. B — Wed, 14 Jan 2026 10:00:00 GMT

Author(s): Weslei B. Fontana, Fabrizio G. Oliviero, and Yi-Ping Huang

Kramers-Wannier duality, a hallmark of the Ising model, has recently gained renewed interest through its reinterpretation as a noninvertible symmetry with a state-level action. Using sequential quantum circuits (SQC), we argue that this duality governs the stability of quantum many-body scar (QMBS) …

[Phys. Rev. B 113, 024307] Published Wed Jan 14, 2026

[Wiley: Small: Table of Contents] Machine Learning‐Assisted Tailoring of Pore Structures in Coal‐Derived Porous Carbons for Enhanced Performance

Wiley: Small: Table of Contents — Wed, 14 Jan 2026 09:20:46 GMT

Small, EarlyView.

[Wiley: Advanced Energy Materials: Table of Contents] Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency

Wiley: Advanced Energy Materials: Table of Contents — Wed, 14 Jan 2026 08:50:01 GMT

Advanced Energy Materials, Volume 16, Issue 2, 14 January 2026.

[cond-mat updates on arXiv.org] Chiral Two-Body Bound States from Berry Curvature and Chiral Superconductivity

cond-mat updates on arXiv.org — Thu, 15 Jan 2026 05:00:00 GMT

arXiv:2601.08055v1 Announce Type: new +Abstract: We present a first-principles machine-learning computational framework to investigate anharmonic effects in polarization-orientation (PO) Raman spectra of molecular crystals, focusing on anthracene and naphthalene. By combining machine learning models for interatomic potentials and polarizability tensors, we enable efficient, large-scale simulations that capture temperature-dependent vibrational dynamics beyond the harmonic approximation. Our approach reproduces key qualitative features observed experimentally. We show, systematically, what are the fingerprints of anharmonic lattice dynamics, thermal expansion, and Raman tensor symmetries on PO-Raman intensities. However, we find that the simulated polarization dependence of Raman intensities shows only subtle deviations from quasi-harmonic predictions, failing to capture the pronounced temperature-dependent changes that have been reported experimentally in anthracene. We propose that part of these inconsistencies stem from the impossibility to deconvolute certain vibrational peaks when only experimental data is available. This work therefore provides a foundation to improve the interpretation of PO-Raman experiments in complex molecular crystals with the aid of theoretical simulations.

[ScienceDirect Publication: Journal of Energy Storage] Design of corrosion resistant Ce-enhanced hybrid solid electrolyte interphase by Ce(TFSI)₃ additives for lithium metal batteries

ScienceDirect Publication: Journal of Energy Storage — Thu, 15 Jan 2026 01:41:46 GMT

Publication date: 10 March 2026

Source: Journal of Energy Storage, Volume 150

Author(s): Mingdong Du, Yanxia Liu, Chenxing Wang, Fulai Qi, Mingyu Shi, Wenjing Liu, Shengnan He, Zhijun Wu, Zhenglong Li, Chenchen Li, Hongge Pan

[ChemRxiv] Assigning the Stereochemistry of Natural Products by Machine Learning

ChemRxiv — Thu, 15 Jan 2026 00:00:00 GMT

[ChemRxiv] Advancing Calcium-Ion Batteries with a Novel Hydrated Eutectic Electrolyte

ChemRxiv — Thu, 15 Jan 2026 00:00:00 GMT

[ChemRxiv] DFT-guided discovery of bisureido ester of 2-(methylthio)ethanol: A novel lead against triple-negative breast cancer inducing apoptosis via pH-sensitive chloride transport

ChemRxiv — Thu, 15 Jan 2026 00:00:00 GMT

[Nature Nanotechnology] Superionic composite electrolytes with continuously perpendicular-aligned pathways for pressure-less all-solid-state lithium batteries

Nature Nanotechnology — Thu, 15 Jan 2026 00:00:00 GMT

Nature Nanotechnology, Published online: 15 January 2026; doi:10.1038/s41565-025-02106-9

[Nature Communications] Mechanistically guided residual learning for battery state monitoring throughout life

Nature Communications — Thu, 15 Jan 2026 00:00:00 GMT

Nature Communications, Published online: 15 January 2026; doi:10.1038/s41467-025-67565-z

[ChemRxiv] Uncertainty Quantification for In Silico Chemistry

ChemRxiv — Thu, 15 Jan 2026 00:00:00 GMT

[ChemRxiv] Practical integration of machine learning into ab initio calculations and workflows: Accelerating the SCF cycle via density matrix predictions

ChemRxiv — Thu, 15 Jan 2026 00:00:00 GMT

[Applied Physics Letters Current Issue] Coherent transport in strongly correlated perovskite-manganite quantum wells

Applied Physics Letters Current Issue — Thu, 15 Jan 2026 00:00:00 GMT

Perovskite transition metal oxides (TMOs) are hallmark systems for studying electron correlations, with strong Coulomb interactions reaching the electron volt scale. Such interactions generally hinder coherent charge transport, limiting its observation to only moderately correlated TMOs. Among TMOs with strong electron correlations, the ferromagnetic perovskite manganite La_1−xSr_xMnO₃ (LSMO) has attracted significant attention for spintronics applications due to its half-metallic nature and robust ferromagnetism, with a Curie temperature above room temperature. In this Letter, we report the emergence of oscillatory conduction in tunnel diodes incorporating an epitaxial thin LSMO layer—a phenomenon not previously observed in strongly correlated oxides. The observed oscillations originate from discrete quantum-well states formed via quantum confinement, indicating coherent transport across the LSMO layer. These quantum-well states are quantitatively explained using a tight-binding model tailored for the electronic structure of LSMO. Our findings demonstrate that high-quality epitaxial perovskite manganites can sustain coherent transport, even in the presence of strong electron correlations, offering avenues for oxide-based quantum and spintronics devices.

[Applied Physics Letters Current Issue] Direct observation of the (de)lithiation process on the multi-particle LiFePO 4 by in situ TEM

Applied Physics Letters Current Issue — Thu, 15 Jan 2026 00:00:00 GMT

The development of nanostructured LiFePO₄ (LFP) electrodes represents a prominent research direction in the Li-ion battery field, owing to its intrinsic advantages such as high theoretical capacity and excellent structural stability. Studying the electrochemical reaction mechanisms at the atomic scale by in situ TEM is essential; however, the mechanisms of ion migration on LFP have not yet been fully elucidated. We report atomic-scale in situ TEM studies of delithiation and lithiation in multi-particle LFP coupled to a Li-rich garnet (LLZNO) solid electrolyte. During delithiation, LFP converts to a metastable L_0.5FP via a periodicity-doubling mechanism (every second layer) accompanied by the emergence of a solid-solution zone, and we directly observe interparticle Li⁺ transport that drives reversible LFP–L_0.5FP–LFP cycles. Conversely, under reductive bias, lithiation proceeds by an interface-dominated crystalline–amorphous transformation, identifying amorphization as a primary particle-level failure pathway. Tracking the structural evolution of LiFePO₄ at the atomic scale during (de)lithiation provides key insights into its kinetic limitations and phase stability, which is essential for optimizing its electrochemical performance.

[Applied Physics Letters Current Issue] Quantitative resolution of grain boundary impedance in Ce-doped Li 7 La 3 Zr 2 O 12 through the distribution of relaxation time analysis and electrochemical performance in quasi solid-state batteries

Applied Physics Letters Current Issue — Thu, 15 Jan 2026 00:00:00 GMT

Li₇La₃Zr₂O₁₂ (LLZO) garnets have many characteristics that make these materials a good solid electrolyte for Li-ion batteries, but their poor interfacial properties and limited ionic conductivity continue to limit their widespread applications. In this study, Ce-doped LLZO has been comprehensively analyzed through x-ray diffraction (XRD), scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS), tunneling electron microscopy-energy dispersive spectroscopy (TEM-EDS), impedance spectroscopy with distribution of relaxation times, and testing of cell experimentally. XRD confirmed the dominant cubic phase with minor Ce-rich secondary phase, while SEM/TEM-EDS shows dense grains with flaky structure along with the homogeneous distribution of elements. Electrochemical testing of symmetric cell indicates lower polarization voltage and stability up to 700 h, while quasi solid-state battery with LiFePO₄ as cathode leads to capacity retention of 72% after 24 cycles.

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Strain-Induced Suppression of Coherent Phonon Transport and Thermoelectric Enhancement in CuBiSeCl2 with Strong Quartic Anharmonicity

The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications) — Wed, 14 Jan 2026 18:58:12 GMT

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03228

[ScienceDirect Publication: eScience] Emerging inorganic amorphous solid-state electrolytes in all-solid-state lithium batteries: From crystallographic order to atomic and lattice disorder

ScienceDirect Publication: eScience — Wed, 14 Jan 2026 18:33:27 GMT

Publication date: Available online 13 January 2026

Source: eScience

Author(s): Yijie Yan, Shuxian Zhang, Xiaoge Man, Qingyu Li, Haoyuan Xue, Peng Xiao, Yuanchang Shi, Longwei Yin, Rutao Wang

[Wiley: Small: Table of Contents] Asymmetric Ion Transport in Tunnel‐Type Cobalt Vanadate for High‐Performance Mn2+/H+ Hybrid Aqueous Batteries

Wiley: Small: Table of Contents — Wed, 14 Jan 2026 14:17:12 GMT

Small, Volume 22, Issue 3, 13 January 2026.

[Wiley: Small: Table of Contents] Machine Learning Accelerated Screening Advanced Single‐Atom Anchored MXenes Electrocatalyst for Hydrogen Evolution Reaction

Wiley: Small: Table of Contents — Wed, 14 Jan 2026 14:17:12 GMT

Small, Volume 22, Issue 3, 13 January 2026.

[ScienceDirect Publication: Materials Today Physics] Defect formation energy of impurities in 2D materials: How does data engineering shape machine learning model selection?

ScienceDirect Publication: Materials Today Physics — Wed, 14 Jan 2026 12:44:11 GMT

Publication date: Available online 13 January 2026

Source: Materials Today Physics

Author(s): A. El Alouani, M. Al Khalfioui, A. Michon, S. Vézian, P. Boucaud, M.T. Dau

[Wiley: Advanced Materials: Table of Contents] 2D Chitin Sub‐Nanosheets with Extreme Ion Transport for Nanofluidic Sensing

Wiley: Advanced Materials: Table of Contents — Wed, 14 Jan 2026 11:46:54 GMT

Advanced Materials, Volume 38, Issue 3, 13 January 2026.

[Wiley: Advanced Intelligent Discovery: Table of Contents] Machine Learning Driven Inverse Design of Broadband Acoustic Superscattering

Wiley: Advanced Intelligent Discovery: Table of Contents — Wed, 14 Jan 2026 11:46:36 GMT

Advanced Intelligent Discovery, EarlyView.

[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Robust LiNi0.6Mn0.4O2 Cathode Achieved from the Dual-Function Strategy of Microstructural Stress Dissipation and Crystalline Phase Ion Transport Improvement

ACS Nano: Latest Articles (ACS Publications) — Wed, 14 Jan 2026 11:35:44 GMT

ACS Nano

DOI: 10.1021/acsnano.5c19029

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Characterizing RNA Tetramer Conformational Landscape Using Explainable Machine Learning

The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications) — Wed, 14 Jan 2026 10:55:20 GMT

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03438

[Recent Articles in Phys. Rev. B] Quantum many-body scarring from Kramers-Wannier duality

Recent Articles in Phys. Rev. B — Wed, 14 Jan 2026 10:00:00 GMT

Author(s): Weslei B. Fontana, Fabrizio G. Oliviero, and Yi-Ping Huang

[Phys. Rev. B 113, 024307] Published Wed Jan 14, 2026

[Wiley: Small: Table of Contents] Machine Learning‐Assisted Tailoring of Pore Structures in Coal‐Derived Porous Carbons for Enhanced Performance

Wiley: Small: Table of Contents — Wed, 14 Jan 2026 09:20:46 GMT

Small, EarlyView.

[Wiley: Advanced Energy Materials: Table of Contents] Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency

Wiley: Advanced Energy Materials: Table of Contents — Wed, 14 Jan 2026 08:50:01 GMT

Advanced Energy Materials, Volume 16, Issue 2, 14 January 2026.

[cond-mat updates on arXiv.org] Chiral Two-Body Bound States from Berry Curvature and Chiral Superconductivity

cond-mat updates on arXiv.org — Wed, 14 Jan 2026 05:00:00 GMT

arXiv:2601.08055v1 Announce Type: new Abstract: Motivated by the discovery of exotic superconductivity in rhombohedral graphene, we study the two-body problem in electronic bands endowed with Berry curvature and show that it supports chiral, non-$s$-wave bound states with nonzero angular momentum. In the presence of a Fermi sea, these interactions give rise to a chiral pairing problem featuring multiple superconducting phases that break time-reversal symmetry. These phases form a cascade of chiral topological states with different angular momenta, where the order-parameter phase winds by $2\pi m$ around the Fermi surface, with $m = 1,3,5,\ldots$, and the succession of phases is governed by the Berry-curvature flux through the Fermi surface area, $\Phi = b k_F^2/2$. As $\Phi$ increases, the system undergoes a sequence of first-order phase transitions between distinct chiral phases, occurring whenever $\Phi$ crosses integer values. This realizes a quantum-geometry analog of the Little--Parks effect -- oscillations in $T_c$ that provide a clear and experimentally accessible hallmark of chiral superconducting order.

[cond-mat updates on arXiv.org] Symmetry-aware Conditional Generation of Crystal Structures Using Diffusion Models

cond-mat updates on arXiv.org — Wed, 14 Jan 2026 05:00:00 GMT

arXiv:2601.08115v1 Announce Type: new Abstract: The application of generative models in crystal structure prediction (CSP) has gained significant attention. Conditional generation--particularly the generation of crystal structures with specified stability or other physical properties has been actively researched for material discovery purposes. Meanwhile, the generative models capable of symmetry-aware generation are also under active development, because space group symmetry has a strong relationship with the physical properties of materials. In this study, we demonstrate that the symmetry control in the previous conditional crystal generation model may not be sufficiently effective when space group constraints are applied as a condition. To address this problem, we propose the WyckoffDiff-Adaptor, which embeds conditional generation within a WyckoffDiff architecture that effectively diffuses Wyckoff positions to achieve precise symmetry control. We successfully generated formation energy phase diagrams while specifying stable structures of particular combination of elements, such as Li--O and Ti--O systems, while simultaneously preserving the symmetry of the input conditions. The proposed method with symmetry-aware conditional generation demonstrates promising results as an effective approach to achieving the discovery of novel materials with targeted physical properties.

[cond-mat updates on arXiv.org] A microscopic origin for the breakdown of the Stokes Einstein relation in ion transport

cond-mat updates on arXiv.org — Wed, 14 Jan 2026 05:00:00 GMT

arXiv:2601.08309v1 Announce Type: new Abstract: Ion transport underlies the operation of biological ion channels and governs the performance of electrochemical energy-storage devices. A long-standing anomaly is that smaller alkali metal ions, such as Li$^+$, migrate more slowly in water than larger ions, in apparent violation of the Stokes-Einstein relation. This breakdown is conventionally attributed to dielectric friction, a collective drag force arising from electrostatic interactions between a drifting ion and its surrounding solvent. Here, combining nanopore transport measurements over electric fields spanning several orders of magnitude with molecular dynamics simulations, we show that the time-averaged electrostatic force on a migrating ion is not a drag force but a net driving force. By contrasting charged ions with neutral particles, we reveal that ionic charge introduces additional Lorentzian peaks in the frequency-dependent friction coefficient. These peaks originate predominantly from short-range Lennard-Jones (LJ) interactions within the first hydration layer and represent additional channels for energy dissipation, strongest for Li$^+$ and progressively weaker for Na$^+$ and K$^+$. Our results demonstrate that electrostatic interactions primarily act to tighten the local hydration structure, thereby amplifying short-range LJ interactions rather than directly opposing ion motion. This microscopic mechanism provides a unified physical explanation for the breakdown of the Stokes-Einstein relation in aqueous ion transport.

My Customized Papers

[ScienceDirect Publication: Solid State Ionics] Engineering dense superionic Li₁₊ₓAlₓTi₂₋ₓ(PO₄)₃ solid electrolytes for safer solid-state Li-ion batteries: Impact of sintering temperature and Al3+ doping

[ScienceDirect Publication: Nano Energy] Magnetic–Current Coupling Matched with Pore Geometry Boosts Ion Transport in LiFePO4 Cathodes

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Predicting Activation Energy of Hydrocarbon Dehydrogenation on Au(111) via Machine Learning

[cond-mat updates on arXiv.org] Emergent chiral Higgs mode in $\pi$-flux frustrated lattices

[cond-mat updates on arXiv.org] Advanced Manufacturing with Renewable and Bio-based Materials: AI/ML workflows and Process Optimization

[cond-mat updates on arXiv.org] A Generalizable Framework for Building Executable Domain-Specific LLMs under Data Scarcity: Demonstration on Semiconductor TCAD Simulation

[cond-mat updates on arXiv.org] Molecular electrostatic potentials from machine learning models for dipole and quadrupole predictions

[cond-mat updates on arXiv.org] Interfacial Behavior from the Atomic Blueprint: Machine Learning-Guided Design of Spatially Functionalized a-SiO2 Surfaces

[cond-mat updates on arXiv.org] Discovering quasiorder parameters in the Potts model: A bridge between machine learning and critical phenomena

[cond-mat updates on arXiv.org] Influence of Exchange-Correlation Functionals and Neural Network Architectures on Li$^+$-Ion Conductivity in Solid-State Electrolyte from Molecular Dynamics Simulations with Machine-Learning Force Fields

[cond-mat updates on arXiv.org] A survey of active learning in materials science: Data-driven paradigm for accelerating the research pipeline

[cond-mat updates on arXiv.org] Agentic AI and Machine Learning for Accelerated Materials Discovery and Applications

[cond-mat updates on arXiv.org] Revisiting Jahn--Teller Transitions in Correlated Oxides with Monte Carlo Modeling

[cond-mat updates on arXiv.org] An information-matching approach to optimal experimental design and active learning

[cond-mat updates on arXiv.org] Quantum circuit complexity and unsupervised machine learning of topological order

[ChemRxiv] YBi2O3.5+δSe1-xClx: A family of semiconductors with a charge- and defect-tunable triple fluorite layer

[ChemRxiv] K5Ir: Reduced Iridium Stabilized in a High-Pressure Semimetal

[ChemRxiv] Nanopore sequencing with proteins: synchronization and dischronization of molecular dynamics simulations with laboratory and industrial developments

[ChemRxiv] Surface Segregation in Compositionally Complex Ag-Au-Pd-Pt Solid Solutions: Insights from High-Throughput Experimentation and Atomistic Simulations

[ChemRxiv] Assignment Promoting Use of Generative AI as an Active Learning Tool in General Chemistry

[ChemRxiv] Auditing Domain Alignment in Latent Representations for Cross-Domain Materials Machine Learning

[ChemRxiv] Asymmetric Multi-Site Ion Exchange in Porous Carbon Electrodes

[ScienceDirect Publication: Solid State Ionics] Engineering dense superionic Li₁₊ₓAlₓTi₂₋ₓ(PO₄)₃ solid electrolytes for safer solid-state Li-ion batteries: Impact of sintering temperature and Al3+ doping

[ScienceDirect Publication: Nano Energy] Magnetic–Current Coupling Matched with Pore Geometry Boosts Ion Transport in LiFePO4 Cathodes

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Predicting Activation Energy of Hydrocarbon Dehydrogenation on Au(111) via Machine Learning

[Recent Articles in Phys. Rev. Lett.] Sub-Doppler Cooling of a Trapped Ion in a Phase-Stable Polarization Gradient

[cond-mat updates on arXiv.org] Emergent chiral Higgs mode in $\pi$-flux frustrated lattices

[cond-mat updates on arXiv.org] Machine Learning-Driven Creep Law Discovery Across Alloy Compositional Space

[cond-mat updates on arXiv.org] Agentic AI and Machine Learning for Accelerated Materials Discovery and Applications

[cond-mat updates on arXiv.org] Data-Driven Exploration and Insights into Temperature-Dependent Phonons in Inorganic Materials

[cond-mat updates on arXiv.org] Sharp spectroscopic fingerprints of disorder in an incompressible magnetic state

[cond-mat updates on arXiv.org] Data-Driven Review and Machine Learning Prediction of Diamond Vacancy Center Synthesis

[cond-mat updates on arXiv.org] Investigating Anharmonicities in Polarization-Orientation Raman Spectra of Acene Crystals with Machine Learning

[ScienceDirect Publication: Journal of Energy Storage] Design of corrosion resistant Ce-enhanced hybrid solid electrolyte interphase by Ce(TFSI)3 additives for lithium metal batteries

[ChemRxiv] Assigning the Stereochemistry of Natural Products by Machine Learning

[ChemRxiv] Advancing Calcium-Ion Batteries with a Novel Hydrated Eutectic Electrolyte

[ChemRxiv] DFT-guided discovery of bisureido ester of 2-(methylthio)ethanol: A novel lead against triple-negative breast cancer inducing apoptosis via pH-sensitive chloride transport

[Nature Nanotechnology] Superionic composite electrolytes with continuously perpendicular-aligned pathways for pressure-less all-solid-state lithium batteries

[Nature Communications] Mechanistically guided residual learning for battery state monitoring throughout life

[ChemRxiv] Uncertainty Quantification for In Silico Chemistry

[ChemRxiv] Practical integration of machine learning into ab initio calculations and workflows: Accelerating the SCF cycle via density matrix predictions

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Strain-Induced Suppression of Coherent Phonon Transport and Thermoelectric Enhancement in CuBiSeCl2 with Strong Quartic Anharmonicity

[ScienceDirect Publication: eScience] Emerging inorganic amorphous solid-state electrolytes in all-solid-state lithium batteries: From crystallographic order to atomic and lattice disorder

[Wiley: Small: Table of Contents] Asymmetric Ion Transport in Tunnel‐Type Cobalt Vanadate for High‐Performance Mn2+/H+ Hybrid Aqueous Batteries

[Wiley: Small: Table of Contents] Machine Learning Accelerated Screening Advanced Single‐Atom Anchored MXenes Electrocatalyst for Hydrogen Evolution Reaction

[ScienceDirect Publication: Materials Today Physics] Defect formation energy of impurities in 2D materials: How does data engineering shape machine learning model selection?

[Wiley: Advanced Materials: Table of Contents] 2D Chitin Sub‐Nanosheets with Extreme Ion Transport for Nanofluidic Sensing

[Wiley: Advanced Intelligent Discovery: Table of Contents] Machine Learning Driven Inverse Design of Broadband Acoustic Superscattering

[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Robust LiNi0.6Mn0.4O2 Cathode Achieved from the Dual-Function Strategy of Microstructural Stress Dissipation and Crystalline Phase Ion Transport Improvement

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Characterizing RNA Tetramer Conformational Landscape Using Explainable Machine Learning

[Recent Articles in Phys. Rev. B] Quantum many-body scarring from Kramers-Wannier duality

[Wiley: Small: Table of Contents] Machine Learning‐Assisted Tailoring of Pore Structures in Coal‐Derived Porous Carbons for Enhanced Performance

[Wiley: Advanced Energy Materials: Table of Contents] Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency

[cond-mat updates on arXiv.org] Chiral Two-Body Bound States from Berry Curvature and Chiral Superconductivity

[ScienceDirect Publication: Journal of Energy Storage] Design of corrosion resistant Ce-enhanced hybrid solid electrolyte interphase by Ce(TFSI)3 additives for lithium metal batteries

[ChemRxiv] Assigning the Stereochemistry of Natural Products by Machine Learning

[ChemRxiv] Advancing Calcium-Ion Batteries with a Novel Hydrated Eutectic Electrolyte

[ChemRxiv] DFT-guided discovery of bisureido ester of 2-(methylthio)ethanol: A novel lead against triple-negative breast cancer inducing apoptosis via pH-sensitive chloride transport

[Nature Nanotechnology] Superionic composite electrolytes with continuously perpendicular-aligned pathways for pressure-less all-solid-state lithium batteries

[Nature Communications] Mechanistically guided residual learning for battery state monitoring throughout life

[ChemRxiv] Uncertainty Quantification for In Silico Chemistry

[ChemRxiv] Practical integration of machine learning into ab initio calculations and workflows: Accelerating the SCF cycle via density matrix predictions

[Applied Physics Letters Current Issue] Coherent transport in strongly correlated perovskite-manganite quantum wells

[Applied Physics Letters Current Issue] Direct observation of the (de)lithiation process on the multi-particle LiFePO 4 by in situ TEM

[Applied Physics Letters Current Issue] Quantitative resolution of grain boundary impedance in Ce-doped Li 7 La 3 Zr 2 O 12 through the distribution of relaxation time analysis and electrochemical performance in quasi solid-state batteries

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Strain-Induced Suppression of Coherent Phonon Transport and Thermoelectric Enhancement in CuBiSeCl2 with Strong Quartic Anharmonicity

[ScienceDirect Publication: eScience] Emerging inorganic amorphous solid-state electrolytes in all-solid-state lithium batteries: From crystallographic order to atomic and lattice disorder

[Wiley: Small: Table of Contents] Asymmetric Ion Transport in Tunnel‐Type Cobalt Vanadate for High‐Performance Mn2+/H+ Hybrid Aqueous Batteries

[Wiley: Small: Table of Contents] Machine Learning Accelerated Screening Advanced Single‐Atom Anchored MXenes Electrocatalyst for Hydrogen Evolution Reaction

[ScienceDirect Publication: Materials Today Physics] Defect formation energy of impurities in 2D materials: How does data engineering shape machine learning model selection?

[Wiley: Advanced Materials: Table of Contents] 2D Chitin Sub‐Nanosheets with Extreme Ion Transport for Nanofluidic Sensing

[Wiley: Advanced Intelligent Discovery: Table of Contents] Machine Learning Driven Inverse Design of Broadband Acoustic Superscattering

[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Robust LiNi0.6Mn0.4O2 Cathode Achieved from the Dual-Function Strategy of Microstructural Stress Dissipation and Crystalline Phase Ion Transport Improvement

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Characterizing RNA Tetramer Conformational Landscape Using Explainable Machine Learning

[Recent Articles in Phys. Rev. B] Quantum many-body scarring from Kramers-Wannier duality

[Wiley: Small: Table of Contents] Machine Learning‐Assisted Tailoring of Pore Structures in Coal‐Derived Porous Carbons for Enhanced Performance

[Wiley: Advanced Energy Materials: Table of Contents] Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency

[cond-mat updates on arXiv.org] Chiral Two-Body Bound States from Berry Curvature and Chiral Superconductivity

[cond-mat updates on arXiv.org] Symmetry-aware Conditional Generation of Crystal Structures Using Diffusion Models

[ScienceDirect Publication: Solid State Ionics] Engineering dense superionic Li₁₊ₓAlₓTi₂₋ₓ(PO₄)₃ solid electrolytes for safer solid-state Li-ion batteries: Impact of sintering temperature and Al³⁺ doping

[ScienceDirect Publication: Nano Energy] Magnetic–Current Coupling Matched with Pore Geometry Boosts Ion Transport in LiFePO₄ Cathodes

[ChemRxiv] YBi₂O_3.5+δSe_1-xCl_x: A family of semiconductors with a charge- and defect-tunable triple fluorite layer

[ScienceDirect Publication: Solid State Ionics] Engineering dense superionic Li₁₊ₓAlₓTi₂₋ₓ(PO₄)₃ solid electrolytes for safer solid-state Li-ion batteries: Impact of sintering temperature and Al³⁺ doping

[ScienceDirect Publication: Nano Energy] Magnetic–Current Coupling Matched with Pore Geometry Boosts Ion Transport in LiFePO₄ Cathodes

[ScienceDirect Publication: Journal of Energy Storage] Design of corrosion resistant Ce-enhanced hybrid solid electrolyte interphase by Ce(TFSI)₃ additives for lithium metal batteries

[ScienceDirect Publication: Journal of Energy Storage] Design of corrosion resistant Ce-enhanced hybrid solid electrolyte interphase by Ce(TFSI)₃ additives for lithium metal batteries