My Customized Papers

[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

[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

[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

[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:2601.08925v1 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 +Abstract: Phonons, quantized vibrations of the atomic lattice, are fundamental to understanding thermal transport, structural stability, and phase behavior in crystalline solids. Despite advances in computational materials science, most predictions of vibrational properties in large materials databases rely on the harmonic approximation and overlook crucial temperature-dependent anharmonic effects. Here, we present a scalable computational framework that combines machine learning interatomic potentials, anharmonic lattice dynamics, and high-throughput calculations to investigate temperature-dependent phonons across thousands of materials. By fine-tuning the universal M3GNet interatomic potential using high-quality phonon data, we improve phonon prediction accuracy by a factor of four while preserving computational efficiency. Integrating this refined model into a high-throughput implementation of the stochastic self-consistent harmonic approximation, we compute temperature-dependent phonons for 4,669 inorganic compounds. Our analysis identifies systematic elemental and structural trends governing anharmonic phonon renormalization, with particularly strong manifestations in alkali metals, perovskite-derived frameworks, and related systems. Machine learning models trained on this dataset identify key atomic-scale features driving strong anharmonicity, including weak bonding, large atomic radii, and specific coordination motifs. First-principles validation confirms that anharmonic effects can dramatically alter lattice thermal conductivity by factors of two to four in some materials. This work establishes a robust and efficient data-driven approach for predicting finite-temperature phonon behavior, offering new pathways for the design and discovery of materials with tailored thermal and vibrational properties.

[cond-mat updates on arXiv.org] Field report from Collaborative Research Center 1625: Heterogeneous research data management using ontology representations

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

arXiv:2601.09359v1 Announce Type: new +Abstract: The goal of the Collaborative Research Center 1625 is the establishment of a scientific basis for the atomic-scale understanding and design of multifunctional compositionally complex solid solution surfaces. Next to materials synthesis in form of thin-film materials libraries, various materials characterization and simulations techniques are used to explore the materials data space of the problem. Machine learning and artificial intelligence techniques guide its exploration and navigation. The effective use of the combined heterogeneous data requires more than just a simple research data management plan. Consequently, our research data management system maps different data modalities in different formats and resolutions from different labs to the correct spatial locations on physical samples. Besides a graphical user interface, the system can also be accessed through an application programming interface for reproducible data-driven workflows. It is implemented by a combination of a custom research data management system designed around a relational database, an ontology which builds upon materials science-specific ontologies, and the construction of a Knowledge Graph. Along with the technical solutions of research data management system and lessons learned, first use cases are shown which were not possible (or at least much harder to achieve) without it.

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

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

arXiv:2601.09705v1 Announce Type: new +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] Enhancing Spatial Reasoning in Large Language Models for Metal-Organic Frameworks Structure Prediction

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

arXiv:2601.09285v1 Announce Type: cross +Abstract: Metal-organic frameworks (MOFs) are porous crystalline materials with broad applications such as carbon capture and drug delivery, yet accurately predicting their 3D structures remains a significant challenge. While Large Language Models (LLMs) have shown promise in generating crystals, their application to MOFs is hindered by MOFs' high atomic complexity. Inspired by the success of block-wise paradigms in deep generative models, we pioneer the use of LLMs in this domain by introducing MOF-LLM, the first LLM framework specifically adapted for block-level MOF structure prediction. To effectively harness LLMs for this modular assembly task, our training paradigm integrates spatial-aware continual pre-training (CPT), structural supervised fine-tuning (SFT), and matching-driven reinforcement learning (RL). By incorporating explicit spatial priors and optimizing structural stability via Soft Adaptive Policy Optimization (SAPO), our approach substantially enhances the spatial reasoning capability of a Qwen-3 8B model for accurate MOF structure prediction. Comprehensive experiments demonstrate that MOF-LLM outperforms state-of-the-art denoising-based and LLM-based methods while exhibiting superior sampling efficiency.

[cond-mat updates on arXiv.org] Machine learning aided parameter analysis in Perovskite X-ray Detector

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

arXiv:2405.04729v2 Announce Type: replace +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.

[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

[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

[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.

[cond-mat updates on arXiv.org] DataScribe: An AI-Native, Policy-Aligned Web Platform for Multi-Objective Materials Design and Discovery

ChemRxiv — Wed, 14 Jan 2026 00:00:00 GMT

arXiv:2601.07966v1 Announce Type: cross @@ -28,7 +39,7 @@ Cl,Br, I)3 using available DFT data, enabling efficient exploration of structura in lead-free Sn perovskites and supporting absorber materials design. Together with the transformer based predictive, generative, and screening workflow, this establishes a multi-scale strategy in which sequence-aware transformers optimize device architectures while MLIPs guide atomic-scale materials -exploration for perovskite solar cells.

[ChemRxiv] Framework of Copolymer gel Dynamics and Ligand Interaction Factors, Objective-free exploration, and CVT-MAPElites

ChemRxiv — Wed, 14 Jan 2026 00:00:00 GMT

Discovery-oriented research is a fundamental pursuit in chemical and materials science, especially when objective-free or purpose-ambiguous exploration can yield unexpected novel compounds or materials. Recently, data-driven objective-free exploration methods have emerged to support such discovery in materials science. However, these methods have mostly been limited to relatively simple systems (e.g., chemical composition), and extending them to real-world multiscale, multi-component materials like soft matter remains a critical challenge. In this study, we present a novel framework for objective-free exploration of a complex gel–ligand interaction system that integrates multiscale data. We embed monomer physicochemical information using four different large-language models (LLMs), and combine these embeddings with polymer dynamics descriptors obtained via time-domain NMR measurements. Using this combined representation, we perform novelty-driven search with BLOX (BoundLess Objective-free eXploration) and utilize CVT-MAP-Elites to map candidates in an interpretable behavioral characteristic space. Unlike conventional molecular descriptors, the LLM-based embedding supports a much broader and richer exploration of gel-ligand interaction space evaluated by 13C NMR signal intensities. The results demonstrate that our framework uncovers a wider diversity of candidate materials than descriptor-based approaches, thereby establishing a new pathway for discovery-oriented design of functional materials that integrates multiscale and multi-feature information.

[ChemRxiv] What if We Were to Design Transport in Aqueous Electrolytes Using Learnings from Lithium Battery Research?

ChemRxiv — Wed, 14 Jan 2026 00:00:00 GMT

Electrolyte design has played a crucial role in the development of lithium batteries for a range of applications. Of late, there is a renewed interest in developing a new generation of aqueous batteries to cheaply store energy for the grid. Given the amount of research our community has carried out developing lithium electrolytes, an intriguing possibility is to leverage these learnings to accelerate the development of aqueous electrolytes - especially when battery development typically requires decades of effort. We herein examine this argument to guide aqueous battery electrolyte research. We discuss electrolyte design heuristics, transport mechanisms across molecular and continuum scales, and the criteria assessing electrolyte advances for improved ion transport.

[ScienceDirect Publication: Journal of Energy Storage] Comparative LCA of energy and environmental impacts in sulfide-based all-solid-state battery manufacturing: Wet vs. dry processes

ScienceDirect Publication: Journal of Energy Storage — Tue, 13 Jan 2026 18:30:44 GMT

Publication date: 10 March 2026

Source: Journal of Energy Storage, Volume 150

Author(s): Jiachen Xu, Tao Feng, Wei Guo, Jun Wu, Liurong Shi, Lin Hua, Ziwei Wang

[ScienceDirect Publication: Journal of Energy Storage] Multiscale modeling for all-solid-state batteries: An investigation on electro-chemo-thermo-mechanical degradation

ScienceDirect Publication: Journal of Energy Storage — Tue, 13 Jan 2026 18:30:44 GMT

Publication date: 10 March 2026

Source: Journal of Energy Storage, Volume 150

Author(s): Kejie Wang, Zhipeng Chen, Fenghui Wang, Xiang Zhao

[ScienceDirect Publication: Journal of Energy Storage] Self-assembled non-flammable poly(arylene ether sulfone)-grafted poly(ethylene glycol) solid electrolyte with improved lithium-ion transport for lithium–sulfur batteries

ScienceDirect Publication: Journal of Energy Storage — Tue, 13 Jan 2026 18:30:44 GMT

Publication date: 10 March 2026

Source: Journal of Energy Storage, Volume 150

Author(s): Anh Le Mong, Thi Cam Thach To, Thuy An Trinh, Dukjoon Kim

[ScienceDirect Publication: Journal of Energy Storage] Solution-processed poly(vinylidene difluoride)-cellulose acetate/Na_1+xAl_xTi_2-x(PO₄)₃ composite quasi-solid electrolyte for safe and high-performance quasi-solid-state sodium-ion batteries

ScienceDirect Publication: Journal of Energy Storage — Tue, 13 Jan 2026 18:30:44 GMT

Publication date: 10 March 2026

Source: Journal of Energy Storage, Volume 150

Author(s): Yi-Hung Liu, Pei-Xuan Chen, Yen-Shen Kuo, Yi-Yu Chiang, Meng-Lun Lee, Torng Jinn Lee

[ScienceDirect Publication: Journal of Energy Storage] Computational insights into the superionic behavior of amorphous lithium oxyhalide 1.6Li₂O-TaCl₅ solid electrolyte

ScienceDirect Publication: Journal of Energy Storage — Tue, 13 Jan 2026 18:30:44 GMT

Publication date: 10 March 2026

Source: Journal of Energy Storage, Volume 150

Author(s): Adil Saleem, Junquan Ou, Leon L. Shaw, Bushra Jabar, Mehwish Khalid Butt

[ScienceDirect Publication: Journal of Energy Storage] Enhancement of ion transport in Li₃InCl₆ solid electrolyte by in-rich strategy

ScienceDirect Publication: Journal of Energy Storage — Tue, 13 Jan 2026 18:30:44 GMT

Publication date: 10 March 2026

Source: Journal of Energy Storage, Volume 150

Author(s): Bo Li, Lei Xian, Fu-Jie Zhao, Zu-Tao Pan, Ling-Bin Kong

[ACS Materials Au: Latest Articles (ACS Publications)] [ASAP] How Realistic Are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper–Water Interfaces

ACS Materials Au: Latest Articles (ACS Publications) — Tue, 13 Jan 2026 16:10:34 GMT

ACS Materials Au

DOI: 10.1021/acsmaterialsau.5c00174

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Anharmonicity-Driven Modulation of Carrier Lifetime and Mobility in BF4-Doped All-Inorganic CsPbX3 (X = I, Br) Perovskites

The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications) — Tue, 13 Jan 2026 13:12:44 GMT

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03817

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Nature of Reverse Water–Gas Shift Reactions at Metal–Oxide Interfaces Uncovered via Interpretable Machine Learning

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Tue, 13 Jan 2026 09:21:41 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c19659

[Wiley: Advanced Science: Table of Contents] Machine Learning Driven Window Blinds Inspired Porous Carbon‐Based Flake for Ultra‐Broadband Electromagnetic Wave Absorption

Wiley: Advanced Science: Table of Contents — Tue, 13 Jan 2026 08:11:16 GMT

Advanced Science, EarlyView.

[Wiley: Advanced Functional Materials: Table of Contents] Toward Robust Ionic Conductivity Determination of Sulfide‐Based Solid Electrolytes for Solid‐State Batteries

Wiley: Advanced Functional Materials: Table of Contents — Tue, 13 Jan 2026 07:18:05 GMT

Advanced Functional Materials, Volume 36, Issue 4, 12 January 2026.

[cond-mat updates on arXiv.org] Mechanisms of alkali ionic transport in amorphous oxyhalides solid state conductors

ChemRxiv — Wed, 14 Jan 2026 00:00:00 GMT

arXiv:2601.06384v1 Announce Type: new +exploration for perovskite solar cells.

[ChemRxiv] Framework of Copolymer gel Dynamics and Ligand Interaction Factors, Objective-free exploration, and CVT-MAPElites

ChemRxiv — Wed, 14 Jan 2026 00:00:00 GMT

[ChemRxiv] What if We Were to Design Transport in Aqueous Electrolytes Using Learnings from Lithium Battery Research?

ChemRxiv — Wed, 14 Jan 2026 00:00:00 GMT

[Applied Physics Letters Current Issue] Unveiling bonding heterogeneity-driven anharmonicity and ultralow lattice thermal conductivity in NbSe 2 Br 2 : A machine learning accelerated discovery

Applied Physics Letters Current Issue — Wed, 14 Jan 2026 00:00:00 GMT

Transition metal chalcogenide halide (TM–Ch–X) compounds with significant heterogeneity in their chemical bonding have immense potential for thermoelectric applications. Their mixed ionic–covalent bonding nature, combined with intrinsic low lattice symmetry, provides a favorable platform for achieving strong lattice anharmonicity and ultralow lattice thermal conductivity. In this work, we developed a temperature-included crystal graph convolutional neural network to accurately predict mode-resolved Grüneisen parameters, a key descriptor of lattice anharmonicity. Using this approach, two-dimensional NbSe₂Br₂ is identified as a thermoelectric candidate with strong anharmonicity and ultralow lattice thermal conductivity. First-principles results reveal that the strong anharmonic lattice dynamics originate from its weak and heterogeneous chemical bonding, further leading to ultralow lattice thermal conductivity. NbSe₂Br₂ also exhibits favorable electronic transport behavior, resulting in a maximum ZT of 1.63. Our work provides a theoretical understanding of the origin of low lattice thermal conductivity in TM–Ch–X compounds with bonding heterogeneity and should encourage further exploration of potential thermoelectric materials.

[ScienceDirect Publication: Journal of Energy Storage] Comparative LCA of energy and environmental impacts in sulfide-based all-solid-state battery manufacturing: Wet vs. dry processes

ScienceDirect Publication: Journal of Energy Storage — Tue, 13 Jan 2026 18:30:44 GMT

Publication date: 10 March 2026

Source: Journal of Energy Storage, Volume 150

Author(s): Jiachen Xu, Tao Feng, Wei Guo, Jun Wu, Liurong Shi, Lin Hua, Ziwei Wang

[ScienceDirect Publication: Journal of Energy Storage] Multiscale modeling for all-solid-state batteries: An investigation on electro-chemo-thermo-mechanical degradation

ScienceDirect Publication: Journal of Energy Storage — Tue, 13 Jan 2026 18:30:44 GMT

Publication date: 10 March 2026

Source: Journal of Energy Storage, Volume 150

Author(s): Kejie Wang, Zhipeng Chen, Fenghui Wang, Xiang Zhao

[ScienceDirect Publication: Journal of Energy Storage] Self-assembled non-flammable poly(arylene ether sulfone)-grafted poly(ethylene glycol) solid electrolyte with improved lithium-ion transport for lithium–sulfur batteries

ScienceDirect Publication: Journal of Energy Storage — Tue, 13 Jan 2026 18:30:44 GMT

Publication date: 10 March 2026

Source: Journal of Energy Storage, Volume 150

Author(s): Anh Le Mong, Thi Cam Thach To, Thuy An Trinh, Dukjoon Kim

[ScienceDirect Publication: Journal of Energy Storage] Solution-processed poly(vinylidene difluoride)-cellulose acetate/Na_1+xAl_xTi_2-x(PO₄)₃ composite quasi-solid electrolyte for safe and high-performance quasi-solid-state sodium-ion batteries

ScienceDirect Publication: Journal of Energy Storage — Tue, 13 Jan 2026 18:30:44 GMT

Publication date: 10 March 2026

Source: Journal of Energy Storage, Volume 150

Author(s): Yi-Hung Liu, Pei-Xuan Chen, Yen-Shen Kuo, Yi-Yu Chiang, Meng-Lun Lee, Torng Jinn Lee

[ScienceDirect Publication: Journal of Energy Storage] Computational insights into the superionic behavior of amorphous lithium oxyhalide 1.6Li₂O-TaCl₅ solid electrolyte

ScienceDirect Publication: Journal of Energy Storage — Tue, 13 Jan 2026 18:30:44 GMT

Publication date: 10 March 2026

Source: Journal of Energy Storage, Volume 150

Author(s): Adil Saleem, Junquan Ou, Leon L. Shaw, Bushra Jabar, Mehwish Khalid Butt

[ScienceDirect Publication: Journal of Energy Storage] Enhancement of ion transport in Li₃InCl₆ solid electrolyte by in-rich strategy

ScienceDirect Publication: Journal of Energy Storage — Tue, 13 Jan 2026 18:30:44 GMT

Publication date: 10 March 2026

Source: Journal of Energy Storage, Volume 150

Author(s): Bo Li, Lei Xian, Fu-Jie Zhao, Zu-Tao Pan, Ling-Bin Kong

[ACS Materials Au: Latest Articles (ACS Publications)] [ASAP] How Realistic Are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper–Water Interfaces

ACS Materials Au: Latest Articles (ACS Publications) — Tue, 13 Jan 2026 16:10:34 GMT

ACS Materials Au

DOI: 10.1021/acsmaterialsau.5c00174

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Anharmonicity-Driven Modulation of Carrier Lifetime and Mobility in BF4-Doped All-Inorganic CsPbX3 (X = I, Br) Perovskites

The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications) — Tue, 13 Jan 2026 13:12:44 GMT

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03817

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Nature of Reverse Water–Gas Shift Reactions at Metal–Oxide Interfaces Uncovered via Interpretable Machine Learning

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Tue, 13 Jan 2026 09:21:41 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c19659

[Wiley: Advanced Science: Table of Contents] Machine Learning Driven Window Blinds Inspired Porous Carbon‐Based Flake for Ultra‐Broadband Electromagnetic Wave Absorption

Wiley: Advanced Science: Table of Contents — Tue, 13 Jan 2026 08:11:16 GMT

Advanced Science, EarlyView.

[Wiley: Advanced Functional Materials: Table of Contents] Toward Robust Ionic Conductivity Determination of Sulfide‐Based Solid Electrolytes for Solid‐State Batteries

Wiley: Advanced Functional Materials: Table of Contents — Tue, 13 Jan 2026 07:18:05 GMT

Advanced Functional Materials, Volume 36, Issue 4, 12 January 2026.

[cond-mat updates on arXiv.org] Mechanisms of alkali ionic transport in amorphous oxyhalides solid state conductors

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2601.06384v1 Announce Type: new Abstract: Amorphous oxyhalides have attracted significant attention due to their relatively high ionic conductivity ($>$1 mS cm$^{-1}$), excellent chemical stability, mechanical softness, and facile synthesis routes via standard solid-state reactions. These materials exhibit an ionic conductivity that is almost independent of the underlying chemistry, in stark contrast to what occurs in crystalline conductors. In this work, we employ an accurately fine-tuned machine learning interatomic potential to construct large-scale molecular dynamics trajectories encompassing hundreds of nanoseconds to obtain statistically converged transport properties. We find that the amorphous state consists of chain fragments of metal-anion tetrahedra of various lenght. By analyzing the residence time of alkali cations migrating around tetrahedrally-coordinated trivalent metal ions, we find that oxygen anions on the metal-anion tetrahedra limit alkali diffusion. By computing the full Einstein expression of the ionic conductivity, we demonstrate that the alkali transference number of these materials is strongly influenced by distinct-particles correlations, while at the same time they are characterized by an alkali Haven ratio close to one, implying that ionic transport is largely dictated by uncorrelated self-diffusion. Finally, by extending this analysis to chemical compositions $AMX_{2.5}\textsf{O}_{0.75}$, spanning different alkaline ($A$ = Li, Na, K), metallic ($M$ = Al, Ga, In), and halogen ($X$ = Cl, Br, I) species, we clarify why the diffusion properties of these materials remain largely insensitive to variations in atomic chemistry.

[cond-mat updates on arXiv.org] Beyond Predicted ZT: Machine Learning Strategies for the Experimental Discovery of Thermoelectric Materials

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2601.06571v1 Announce Type: new Abstract: The discovery of high-performance thermoelectric (TE) materials for advancing green energy harvesting from waste heat is an urgent need in the context of looming energy crisis and climate change. The rapid advancement of machine learning (ML) has accelerated the design of thermoelectric (TE) materials, yet a persistent "gap" remains between high-accuracy computational predictions and their successful experimental validation. While ML models frequently report impressive test scores (R^2 values of 0.90-0.98) for complex TE properties (zT, power factor, and electrical/thermal conductivity), only a handful of these predictions have culminated in the experimental discovery of new high-zT materials. In this review, we identify and discuss that the primary obstacles are poor model generalizability-stemming from the "small-data" problem, sampling biases in cross-validation, and inadequate structural representation-alongside the critical challenge of thermodynamic phase stability. Moreover, we argue that standard randomized validation often overestimates model performance by ignoring "hidden hierarchies" and clustering within chemical families. Finally, to bridge this gap between ML-predictions and experimental realization, we advocate for advanced validation strategies like PCA-based sampling and a synergetic active learning loop that integrates ML "fast filters" for stability (e.g., GNoME) with high-throughput combinatorial thin-film synthesis to rapidly map stable, high-zT compositional spaces.

[cond-mat updates on arXiv.org] Altermagnetism-driven FFLO superconductivity in finite-filling 2D lattices

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2601.06735v1 Announce Type: new Abstract: We systematically investigate the emergence of finite-momentum Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) superconductivity in a square lattice Hubbard model with finite filling, driven by either $d_{xy}$-wave or $d_{x^{2}-y^{2}}$-wave altermagnetic order in the presence of on-site $s$-wave attractive interactions. Our study combines mean-field calculation in the superconducting phase with pairing instability analysis of the normal state, incorporating the next-nearest-neighbor hopping in the single-particle dispersion relation. We demonstrate that the two types of altermagnetism have markedly different impacts on the stabilization of FFLO states. Specifically, $d_{xy}$-wave altermagnetism supports FFLO superconductivity over a broad parameter regime at low fillings, whereas $d_{x^{2}-y^{2}}$-wave altermagnetism only induces FFLO pairing in a narrow range at high fillings. Furthermore, we find that the presence of a Van Hove singularity in the density of states tends to suppress FFLO superconductivity. These findings may provide guidance for experimental exploration of altermagnetism-induced FFLO states in real materials with more complex electronic structures.

My Customized Papers

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

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

[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] 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] Field report from Collaborative Research Center 1625: Heterogeneous research data management using ontology representations

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

[cond-mat updates on arXiv.org] Enhancing Spatial Reasoning in Large Language Models for Metal-Organic Frameworks Structure Prediction

[cond-mat updates on arXiv.org] Machine learning aided parameter analysis in Perovskite X-ray Detector

[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

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

[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

[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] DataScribe: An AI-Native, Policy-Aligned Web Platform for Multi-Objective Materials Design and Discovery

[ChemRxiv] Framework of Copolymer gel Dynamics and Ligand Interaction Factors, Objective-free exploration, and CVT-MAPElites

[ChemRxiv] What if We Were to Design Transport in Aqueous Electrolytes Using Learnings from Lithium Battery Research?

[ScienceDirect Publication: Journal of Energy Storage] Comparative LCA of energy and environmental impacts in sulfide-based all-solid-state battery manufacturing: Wet vs. dry processes

[ScienceDirect Publication: Journal of Energy Storage] Multiscale modeling for all-solid-state batteries: An investigation on electro-chemo-thermo-mechanical degradation

[ScienceDirect Publication: Journal of Energy Storage] Self-assembled non-flammable poly(arylene ether sulfone)-grafted poly(ethylene glycol) solid electrolyte with improved lithium-ion transport for lithium–sulfur batteries

[ScienceDirect Publication: Journal of Energy Storage] Solution-processed poly(vinylidene difluoride)-cellulose acetate/Na1+xAlxTi2-x(PO4)3 composite quasi-solid electrolyte for safe and high-performance quasi-solid-state sodium-ion batteries

[ScienceDirect Publication: Journal of Energy Storage] Computational insights into the superionic behavior of amorphous lithium oxyhalide 1.6Li2O-TaCl5 solid electrolyte

[ScienceDirect Publication: Journal of Energy Storage] Enhancement of ion transport in Li3InCl6 solid electrolyte by in-rich strategy

[ACS Materials Au: Latest Articles (ACS Publications)] [ASAP] How Realistic Are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper–Water Interfaces

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Anharmonicity-Driven Modulation of Carrier Lifetime and Mobility in BF4-Doped All-Inorganic CsPbX3 (X = I, Br) Perovskites

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Nature of Reverse Water–Gas Shift Reactions at Metal–Oxide Interfaces Uncovered via Interpretable Machine Learning

[Wiley: Advanced Science: Table of Contents] Machine Learning Driven Window Blinds Inspired Porous Carbon‐Based Flake for Ultra‐Broadband Electromagnetic Wave Absorption

[Wiley: Advanced Functional Materials: Table of Contents] Toward Robust Ionic Conductivity Determination of Sulfide‐Based Solid Electrolytes for Solid‐State Batteries

[cond-mat updates on arXiv.org] Mechanisms of alkali ionic transport in amorphous oxyhalides solid state conductors

[ChemRxiv] Framework of Copolymer gel Dynamics and Ligand Interaction Factors, Objective-free exploration, and CVT-MAPElites

[ChemRxiv] What if We Were to Design Transport in Aqueous Electrolytes Using Learnings from Lithium Battery Research?

[Applied Physics Letters Current Issue] Unveiling bonding heterogeneity-driven anharmonicity and ultralow lattice thermal conductivity in NbSe 2 Br 2 : A machine learning accelerated discovery

[ScienceDirect Publication: Journal of Energy Storage] Comparative LCA of energy and environmental impacts in sulfide-based all-solid-state battery manufacturing: Wet vs. dry processes

[ScienceDirect Publication: Journal of Energy Storage] Multiscale modeling for all-solid-state batteries: An investigation on electro-chemo-thermo-mechanical degradation

[ScienceDirect Publication: Journal of Energy Storage] Self-assembled non-flammable poly(arylene ether sulfone)-grafted poly(ethylene glycol) solid electrolyte with improved lithium-ion transport for lithium–sulfur batteries

[ScienceDirect Publication: Journal of Energy Storage] Solution-processed poly(vinylidene difluoride)-cellulose acetate/Na1+xAlxTi2-x(PO4)3 composite quasi-solid electrolyte for safe and high-performance quasi-solid-state sodium-ion batteries

[ScienceDirect Publication: Journal of Energy Storage] Computational insights into the superionic behavior of amorphous lithium oxyhalide 1.6Li2O-TaCl5 solid electrolyte

[ScienceDirect Publication: Journal of Energy Storage] Enhancement of ion transport in Li3InCl6 solid electrolyte by in-rich strategy

[ACS Materials Au: Latest Articles (ACS Publications)] [ASAP] How Realistic Are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper–Water Interfaces

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Anharmonicity-Driven Modulation of Carrier Lifetime and Mobility in BF4-Doped All-Inorganic CsPbX3 (X = I, Br) Perovskites

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Nature of Reverse Water–Gas Shift Reactions at Metal–Oxide Interfaces Uncovered via Interpretable Machine Learning

[Wiley: Advanced Science: Table of Contents] Machine Learning Driven Window Blinds Inspired Porous Carbon‐Based Flake for Ultra‐Broadband Electromagnetic Wave Absorption

[Wiley: Advanced Functional Materials: Table of Contents] Toward Robust Ionic Conductivity Determination of Sulfide‐Based Solid Electrolytes for Solid‐State Batteries

[cond-mat updates on arXiv.org] Mechanisms of alkali ionic transport in amorphous oxyhalides solid state conductors

[cond-mat updates on arXiv.org] Beyond Predicted ZT: Machine Learning Strategies for the Experimental Discovery of Thermoelectric Materials

[cond-mat updates on arXiv.org] Altermagnetism-driven FFLO superconductivity in finite-filling 2D lattices

[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

[ScienceDirect Publication: Journal of Energy Storage] Solution-processed poly(vinylidene difluoride)-cellulose acetate/Na_1+xAl_xTi_2-x(PO₄)₃ composite quasi-solid electrolyte for safe and high-performance quasi-solid-state sodium-ion batteries

[ScienceDirect Publication: Journal of Energy Storage] Computational insights into the superionic behavior of amorphous lithium oxyhalide 1.6Li₂O-TaCl₅ solid electrolyte

[ScienceDirect Publication: Journal of Energy Storage] Enhancement of ion transport in Li₃InCl₆ solid electrolyte by in-rich strategy

[ScienceDirect Publication: Journal of Energy Storage] Solution-processed poly(vinylidene difluoride)-cellulose acetate/Na_1+xAl_xTi_2-x(PO₄)₃ composite quasi-solid electrolyte for safe and high-performance quasi-solid-state sodium-ion batteries

[ScienceDirect Publication: Journal of Energy Storage] Computational insights into the superionic behavior of amorphous lithium oxyhalide 1.6Li₂O-TaCl₅ solid electrolyte

[ScienceDirect Publication: Journal of Energy Storage] Enhancement of ion transport in Li₃InCl₆ solid electrolyte by in-rich strategy