My Customized Papers

[cond-mat updates on arXiv.org] Performance of AI agents based on reasoning language models on ALD process optimization tasks

ScienceDirect Publication: Materials Today Physics — Fri, 16 Jan 2026 12:43:18 GMT

Publication date: Available online 15 January 2026

Source: Materials Today Physics

Author(s): Umair Haider, Gul Rahman, Imran Shakir, M.S. Al-Buriahi, Norah Alomayrah, Imen Kebaili

[Wiley: Small: Table of Contents] Machine Learning‐Accelerated Specific Surface Prediction Strategy in Janus‐Based Z‐Scheme Heterostructures for Efficient Photocatalytic Water Splitting

Wiley: Small: Table of Contents — Fri, 16 Jan 2026 08:21:14 GMT

Small, Volume 22, Issue 4, 16 January 2026.

[Wiley: Advanced Energy Materials: Table of Contents] Machine Learning‐Guided Design of L10‐PtCo Intermetallic Catalysts: Zn‐Mediated Atomic Ordering

Wiley: Advanced Energy Materials: Table of Contents — Fri, 16 Jan 2026 05:15:00 GMT

Advanced Energy Materials, EarlyView.

[cond-mat updates on arXiv.org] Performance of AI agents based on reasoning language models on ALD process optimization tasks

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. @@ -16,7 +16,19 @@ Abstract: The exploration of materials composition, structure, and processing sp 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 — Fri, 16 Jan 2026 05:00:00 GMT

arXiv:2601.08925v1 Announce Type: new +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

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

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

[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

[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

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

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

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

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

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

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

[Nature Materials] Heterogeneous doping via nanoscale coating impacts the mechanics of Li intrusion in brittle solid electrolytes

Nature Materials — Fri, 16 Jan 2026 00:00:00 GMT

Nature Materials, Published online: 16 January 2026; doi:10.1038/s41563-025-02465-7

Short-circuiting during fast charging through lithium dendrite intrusion into electrolytes is a major challenge in solid-state batteries. Here, using thermally annealed 3-nm-thick Ag coatings, lithium penetration into brittle electrolyte Li6.6La3Zr1.6Ta0.4O12 is inhibited at local current densities of 250 mA cm−2 due to an increase in surface fracture toughness.

[RSC - Digital Discovery latest articles] Evaluating large language models for inverse semiconductor design

RSC - Digital Discovery latest articles — Fri, 16 Jan 2026 00:00:00 GMT

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00544B, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Muhammed Nur Talha Kilic, Daniel Wines, Kamal Choudhary, Vishu Gupta, Youjia Li, Sayak Chakrabarty, Wei-Keng Liao, Alok Choudhary, Ankit Agrawal
Large Language Models (LLMs) can enable inverse materials discovery by generating text-encoded crystal structures from target properties.
To cite this article before page numbers are assigned, use the DOI form of citation above.
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[ChemRxiv] Data-Driven Transfer Learning Across MOF-Derived Zirconia Polymorphs

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

Despite extensive investigation of metal-organic framework (MOF) derived materials over the last +20 years, no systematic approach to predict the structural properties of the derived metal oxides is +available. We present an integrated machine learning (ML) approach leveraging Smooth Overlap of +Atomic Positions (SOAP) and multiple ML models, including Kernel Ridge Regression (KRR), to predict +thermally derived zirconium dioxide (ZrO2) polymorph from diverse Zr-based precursors. By a systematic +experimental dataset of calcination parameters, we train the ML model to quantitatively forecast material +properties and the weight fraction of crystalline phases of the resulting ZrO2. Experimental validation of +model predictions confirms that the chemical composition of precursors and calcination parameters have +a profound influence on the crystallinity of MOF-derived ZrO2 polymorph. Our findings demonstrate +the utility of small-data-driven predictive ML modeling and transfer learning for guiding the synthesis of +advanced oxide materials providing a blueprint for accelerated discovery of MOF-derived nanomaterials.

[ChemRxiv] The Role of Oxygen Excess on Fluoride Intercalation in Ruddlesden–Popper Electrodes for Fluoride Ion Batteries: The Case of LaSrMnO4

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

Ruddlesden–Popper–type compounds are particularly attractive electrode materials for fluoride-ion batteries. Among them, LaSrMnO4 has received significant attention due to its high fluoride incorporation capability and lower environmental impact compared to nickel- and cobalt-based analogues. In this work, neutron diffraction data are used to provide an experimental visualization of fluoride-ion diffusion in this class of materials, through Maximum Entropy Method (MEM) and Bond Valence Site Energy (BVSE) analysis. Additionally, since oxygen excess is well known in Ruddlesden–Popper oxides but its impact on fluoride-ion transport has not been previously investigated, molecular dynamics (MD) simulations were employed to reveal how oxygen over-stoichiometry affects fluoride intercalation mechanisms and energetics, unveiling new migration pathways that hinder fluoride mobility. These findings have direct implications for fluoride-ion battery performance, highlighting the critical role of oxygen content in determining anion transport and the electrochemical performance of this class of materials.

[ChemRxiv] From Cation Order to Disorder: Unlocking Ion Transport Pathways in Li-Zn-Zr-Cl Halospinels

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

Lithium metal chloride halospinels of the general formula Li2MCl4 are a promising class of earth-abundant ion conductors for all-solid-state batteries. However, poor room-temperature ionic conductivity has historically limited their use in practical applications. Here, we substitute Zr4+ into Li2ZnCl4 along the series Li2−2x/3Zn1−xZr2x/3Cl4 (x = 0, 0.1, 0.3, 0.6, 0.9, and 1.0) to understand how cation disorder and vacancy tuning impacts ion transport in “normal” halospinels. Aliovalent Zr4+ substitution increases ionic conductivity by nearly five orders of magnitude, from 1.320(3) × 10−9 S cm−1 in Li2ZnCl4 to 6.74(1) × 10−5 S cm−1 for x = 0.6. Average and local structure characterization through synchrotron X-ray diffraction +(SXRD) and neutron pair distribution function (nPDF) analysis reveal that Zr4+ redistributes the Zn2+ and Li+ sublattices into previously unoccupied interstitial sites that form new low-energy hopping pathways that facilitate ion transport. We rationalize the dramatic rearrangement of the cation local structure by considering the coordination +preferences of the cations and the potential electrostatic penalties incurred by the higher-valent Zr4+ cations. This work delivers an atomistic understanding of substitution-induced cation disorder and ion transport properties in a new family of earth-abundant halospinels.

[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

[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.

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