My Customized Papers

[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

[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

[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

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

arXiv:2601.06384v1 Announce Type: new +My Customized Papershttps://github.com/your_username/your_repoAggregated research papers

[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

[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

[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.

[cond-mat updates on arXiv.org] Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery

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

arXiv:2601.06820v1 Announce Type: new @@ -27,7 +27,7 @@ Abstract: The common exact diagonalization-based techniques to solving tight-bin Abstract: Machine learning models have become firmly established across all scientific fields. Extracting features from data and making inferences based on them with neural network models often yields high accuracy; however, this approach has several drawbacks. Symbolic regression is a powerful technique for discovering analytical equations that describe data, providing interpretable and generalizable models capable of predicting unseen data. Symbolic regression methods have gained new momentum with the advancement of neural network technologies and offer several advantages, the main one being the interpretability of results. In this work, we examined the application of the deep symbolic regression algorithm SEGVAE to determine the properties of two-dimensional materials with defects. Comparing the results with state-of-the-art graph neural network-based methods shows comparable or, in some cases, even identical outcomes. We also discuss the applicability of this class of methods in natural sciences.

[cond-mat updates on arXiv.org] MiST: Understanding the Role of Mid-Stage Scientific Training in Developing Chemical Reasoning Models

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

arXiv:2512.21231v2 Announce Type: replace-cross Abstract: Large Language Models can develop reasoning capabilities through online fine-tuning with rule-based rewards. However, recent studies reveal a critical constraint: reinforcement learning succeeds only when the base model already assigns non-negligible probability to correct answers -- a property we term 'latent solvability'. This work investigates the emergence of chemical reasoning capabilities and what these prerequisites mean for chemistry. We identify two necessary conditions for RL-based chemical reasoning: 1) Symbolic competence, and 2) Latent chemical knowledge. We propose mid-stage scientific training (MiST): a set of mid-stage training techniques to satisfy these, including data-mixing with SMILES/CIF-aware pre-processing, continued pre-training on 2.9B tokens, and supervised fine-tuning on 1B tokens. These steps raise the latent-solvability score on 3B and 7B models by up to 1.8x, and enable RL to lift top-1 accuracy from 10.9 to 63.9% on organic reaction naming, and from 40.6 to 67.4% on inorganic material generation. Similar results are observed for other challenging chemical tasks, while producing interpretable reasoning traces. Our results define clear prerequisites for chemical reasoning training and highlight the broader role of mid-stage training in unlocking reasoning capabilities.

[cond-mat updates on arXiv.org] Exact Multimode Quantization of Superconducting Circuits via Boundary Admittance

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

arXiv:2601.04407v2 Announce Type: replace-cross Abstract: We show that the Schur complement of the nodal admittance matrix, which reduces a multiport electromagnetic environment to the driving-point admittance $Y_{\mathrm{in}}(s)$ at the Josephson junction, naturally leads to an eigenvalue-dependent boundary condition determining the dressed mode spectrum. This identification provides a four-step quantization procedure: (i) compute or measure $Y_{\mathrm{in}}(s)$, (ii) solve the boundary condition $sY_{\mathrm{in}}(s) + 1/L_J = 0$ for dressed frequencies, (iii) synthesize an equivalent passive network, (iv) quantize with the full cosine nonlinearity retained. Within passive lumped-element circuit theory, we prove that junction participation decays as $O(\omega_n^{-1})$ at high frequencies when the junction port has finite shunt capacitance, ensuring ultraviolet convergence of perturbative sums without imposed cutoffs. The standard circuit QED parameters, coupling strength $g$, anharmonicity $\alpha$, and dispersive shift $\chi$, emerge as controlled limits with explicit validity conditions.

[ChemRxiv] A data-efficient reactive machine learning potential to accelerate automated exploration of complex reaction networks

ChemRxiv — Tue, 13 Jan 2026 00:00:00 GMT

Reactive machine learning potentials (MLPs) significantly benefits high-throughput exploration of complex reaction networks. However, the development of such MLPs is currently constrained by the scarcity of datasets generated through efficient sampling methods that adequately capture elusive non-equilibrium configurations. To address this, we introduce an integrated molecular dynamics/coordinate driving-active learning (MD/CD-AL) framework that strategically samples reactive configurations, yielding a compact but comprehensive dataset, MDCD20, with ~1.4 million neutral and radical H/C/N/O structures. The MDCD-NN MLP trained on MDCD20 surpasses existing available MLPs trained on much larger datasets in reconstructing 181 elementary reactions with widespread types. It also accelerates automatic reaction network exploration by 10^4-fold in real-world systems relative to its reference quantum chemistry (QM) calculations, tackling complex scenarios such as multiple reaction centers, enantioselectivity and dynamic free-energy landscapes that are beyond the reach of traditional QM methods. This work establishes an efficient paradigm for constructing reactive datasets, enabling the training of reliable MLPs for complex reactive systems at an affordable cost.

[Nature Communications] A unified time-frequency foundation model for sleep decoding

Nature Communications — Tue, 13 Jan 2026 00:00:00 GMT

Nature Communications, Published online: 13 January 2026; doi:10.1038/s41467-025-67970-4

SleepGPT is a time-frequency foundation model for sleep decoding, built on a generative pretrained transformer, achieving superior performance in various downstream tasks across datasets.

[npj Computational Materials] Physically interpretable interatomic potentials via symbolic regression and reinforcement learning

npj Computational Materials — Tue, 13 Jan 2026 00:00:00 GMT

npj Computational Materials, Published online: 13 January 2026; doi:10.1038/s41524-025-01952-4

Physically interpretable interatomic potentials via symbolic regression and reinforcement learning

[ChemRxiv] Can simple exchange heuristics guide us in predicting magnetic properties of solids?

ChemRxiv — Tue, 13 Jan 2026 00:00:00 GMT

The Kanamori-Goodenough-Anderson rules are a textbook heuristic for predicting magnetism. They connect bond angles to magnetic ordering for some transition metal compounds. Such domain knowledge is of high importance for building predictive machine learning models in scenarios with scarce data. Yet, there has been no statistical, large-scale evaluation of the heuristic. Here, we evaluate this heuristic on an experimental database of magnetic structures. We observe that the heuristic is largely satisfied, and we discuss the exceptions. We then demonstrate how integrating this heuristic into machine learning models for predicting magnetic ordering enhances prediction quality. Notably, these magnetism models are also capable of predicting if non-collinear magnetic ordering might occur. Furthermore, the heuristic provides a useful benchmark for evaluating theoretical methods that calculate magnetic properties.

[ChemRxiv] AIQM-PBSA: Integrating Machine Learning Interatomic -Potentials with MMPBSA for Accurate Protein–Ligand Binding Free Energy Calculations

ChemRxiv — Tue, 13 Jan 2026 00:00:00 GMT

End-point binding free energy (BFE) methods, such as molecular mechanics Poisson–Boltzmann surface area (MMPBSA), are widely used to estimate protein–ligand binding affinity due to their favorable balance between accuracy and computational efficiency. Their reliability, however, is often limited by approximations in intramolecular interactions and solvation effects. Given the critical role of force field quality in determining accuracy, we developed a hybrid framework named AIQM-PBSA, which integrates the ONIOM scheme with the PBSA model. Within this framework, the AIQM3 machine learning interatomic potential (MLIP) —an advanced Δ-learning quantum mechanical (QM) model—is employed to refine the molecular mechanics (MM) energy term, while polar and non polar solvation contributions are evaluated under the PBSA formalism. Extensive validation across diverse protein–ligand systems demonstrates that AIQM-PBSA significantly improves the correlation with experimental binding affinities compared to MMPBSA based on classical force fields and other MLIPs, with a representative benchmark showing up to 31% higher Pearson correlation relative to the MMPBSA baseline and 16% higher than ANI-2x. Furthermore, incorporating entropic contributions can further provide modest, target-dependent improvements. In summary, AIQM-PBSA offers a robust and transferable framework that combines QM level accuracy with MM level efficiency, substantially enhancing the reliability of endpoint free energy calculations for biomolecular recognition.

[ChemRxiv] To Be or Not to Be: The Elusive Nature of Wheland-type Intermediates in Zeolite-Catalyzed Aromatic Alkylation Revealed by CCSD(T)-quality Metadynamics

ChemRxiv — Tue, 13 Jan 2026 00:00:00 GMT

The sustainable utilization of biomass feedstocks to produce value-added chemicals is a central challenge in heterogeneous catalysis. Cyclic alcohols constitute a major fraction of biomass-derived compounds, and their catalytic upgrading via zeolite-catalyzed alkylation provides an efficient route toward fuels and fine chemicals. In particular, benzene alkylation enables the synthesis of industrially relevant alkylated aromatics, while phenol alkylation is crucial for the valorization of lignin-derived feedstocks. Here, we employ machine-learning interatomic potentials (MLIPs) combined with well-tempered Metadynamics (WTMetaD) to investigate the alkylation of benzene and phenol using cyclohexene---the dehydrated form of cyclohexanol---as the alkylating agent within a zeolite framework. Free-energy surfaces (FES) obtained from enhanced sampling simulations are refined beyond standard generalized gradient approximation (GGA) density functional theory (DFT) using free-energy perturbation (FEP) to achieve MP2 and CCSD(T) accuracy. Our results reveal that it is essential to move beyond standard GGA-based DFT to accurately assess the stability of charged intermediates. The arenium ion (or Wheland intermediate), a key $\sigma$-complex in electrophilic aromatic substitution, appears relatively stable at the GGA level of theory. However, higher-level CCSD(T) calculations show that it corresponds to only a weakly stabilized, shallow minimum, indicating a highly transient character. The presence of an activating group such as the hydroxyl substituent in phenol significantly stabilizes both the arenium intermediate and the corresponding transition state, thereby lowering the overall alkylation activation barrier.

[ChemRxiv] Retrieval-Augmented Large Language Models for Chemistry: A Comprehensive Survey

ChemRxiv — Tue, 13 Jan 2026 00:00:00 GMT

The rapid proliferation of Large Language Models (LLMs) has heralded a new era in artificial intelligence, demonstrating remarkable capabilities in understanding, generating, and reasoning with human language. Their potential to revolutionize scientific discovery, particularly in chemistry, is immense. However, standalone LLMs are inherently limited by their reliance on static pre-training data, leading to issues such as factual hallucination, outdated knowledge, and a lack of transparency in their reasoning processes. Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm to mitigate these limitations by grounding LLM responses in external, up-to-date, and verifiable knowledge sources. This survey provides a comprehensive overview of the intersection of RAG and LLMs within the chemical sciences. We delve into the foundational concepts of LLMs and RAG, detail the unique architectures and methodologies required for handling diverse chemical data, and systematically review their applications across drug discovery, materials science, reaction prediction, and chemical literature mining. Furthermore, we critically examine the existing challenges, limitations, and ethical considerations inherent in deploying RAG-LLMs in chemistry. Finally, we discuss promising future directions, emphasizing the need for robust evaluation benchmarks and advanced multimodal RAG systems to unlock the full potential of these transformative technologies in accelerating chemical innovation.

[ChemRxiv] A Construction of Arbitrary Order Internal Coordinate Transformations to Improve Studies of Large Amplitude Motions

ChemRxiv — Tue, 13 Jan 2026 00:00:00 GMT

Internal coordinates and their derivatives underpin the efficient treatment of geometry optimizations, high-resolution spectroscopic simulation, and the fitting of potential surfaces in quantum chemistry. Existing descriptions of the construction of internal coordinate derivatives generally either lack simplicity or generality. In this paper, we provide a simple framework for evaluating any internal coordinate derivative to any order and an automatic approach to obtain the corresponding inverse transformation. Through further extension to transformations between internal coordinate systems, this approach provides a complete, generic method for handling a wide variety of molecular problems. The utility of this construction is demonstrated by investigations into the behavior of internal coordinate interpolations for studying isomerizations, quantifying the coupling between carbonyl stretches and a complex stretch coordinate in an organometallic system, and analysis of the performance of a machine learned interatomic potential in computing anharmonic frequencies as a function of low-frequency coordinate distortions. This approach is shown to be numerically efficient as well as general, and a reference implementation is provided.

[RSC - Chem. Sci. latest articles] Mitigation strategies for Li2CO3 contamination in garnet-type solid-state electrolytes: Formation mechanisms and interfacial engineering

RSC - Chem. Sci. latest articles — Tue, 13 Jan 2026 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC09699E, Review Article

Open Access

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

Bin Hao, Qiushi Wang, Fangyuan Zhao, Jialong Wu, Weiheng Chen, Zhong-Jie Jiang, Zhongqing Jiang
Garnet-type solid-state electrolytes (SSEs) are promising candidates for next-generation solid-state batteries (SSBs) owing to their high ionic conductivity, robust mechanical strength, and broad electrochemical stability window. However, exposure to ambient...
The content of this RSS Feed (c) The Royal Society of Chemistry

[ChemRxiv] Navigating high-dimensional fabrication-parameter space in organic photovoltaics device optimization by a multi-tier machine learning framework

ChemRxiv — Tue, 13 Jan 2026 00:00:00 GMT

Organic photovoltaics (OPV) have achieved significant advances over recent decades, driven by synergistic innovations in molecular design and device engineering. However, precise morphological control within bulk-heterojunction active layers remains a crucial barrier to commercial viability, primarily due to the high-dimensional parameter spaces and complex interdependencies among processing variables. To overcome this challenge, we established a standardized materials-processing-performance database integrating donor/acceptor pairs, nine key active layer processing parameters, and device efficiencies. This database, curated from over a decade of experimental results, resolves critical data heterogeneity issues and provides the field's most comprehensive optimization resource. Leveraging this resource, we developed a novel three-tiered machine learning framework employing gradient boosting regression trees to progressively decode active layer processing complexities. Our strategy initiates with single-parameter models for targeted optimization, advances through stage-combined models revealing intra-process synergies (e.g., solvent-additive interplay), and culminates in a global optimization tier. Remarkably, this final tier achieves unprecedented performance, demonstrating >0.9 overall Pearson correlations, and >80% success rates in identifying optimal nine-dimensional configurations. Experimental validation on 78 novel systems confirms exceptional generalization, yielding >75% accuracy in predicting either optimal or secondary parameters across eight active layer processing conditions. This work establishes a robust framework for navigating processing complexity in high-dimensional spaces, enabling accelerated optimization of OPV photoactive layers and providing a transferable data-driven paradigm for rational process design in emerging photovoltaic technologies.

[RSC - Chem. Sci. latest articles] The interlocking process in molecular machines explained by a combined approach: the nudged elastic band method and a machine learning potential

RSC - Chem. Sci. latest articles — Tue, 13 Jan 2026 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC08303F, Edge Article

Open Access

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

Lucio Peña-Zarate, Alberto Vela, Jorge Tiburcio
Engineering molecular machines requires a precise knowledge of the mechanisms involved in programmed motions. Among artificial molecular machines, rotaxanes have emerged as a noteworthy model due to their ability to...
The content of this RSS Feed (c) The Royal Society of Chemistry

[Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)] [ASAP] Multiscale Modeling of Solid Electrolyte Interphase Formation on Oxygen-Functionalized Graphite Anodes for Lithium-Ion Batteries

Journal of Chemical Theory and Computation: Latest Articles (ACS Publications) — Mon, 12 Jan 2026 20:10:43 GMT

Journal of Chemical Theory and Computation

DOI: 10.1021/acs.jctc.5c01561

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Ultrahigh Ionic Conductivity in Halide Electrolytes Enabled by Anion Framework Flexibility Engineering

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Mon, 12 Jan 2026 17:43:24 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c15937

[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Mechanochemistry-Driven Optimization of Halide-Based Solid-State Electrolytes via Orthogonal Design of Experiments and Regression Modeling

ACS Materials Letters: Latest Articles (ACS Publications) — Mon, 12 Jan 2026 16:07:51 GMT

ACS Materials Letters

DOI: 10.1021/acsmaterialslett.5c01492

[Wiley: Small: Table of Contents] Multifunctional Cellulose Derivative Enables Efficient and Stable Wide‐Bandgap Perovskite Solar Cells by Inhibiting Ion Migration

Wiley: Small: Table of Contents — Mon, 12 Jan 2026 15:04:35 GMT

Small, EarlyView.

[Wiley: Advanced Energy Materials: Table of Contents] “Ionic Tug‐of‐War” Effect Decoupling Li+‐Coordination Enables High Ion Conductivity and Interface Stability for Solid‐State Electrolytes

Wiley: Advanced Energy Materials: Table of Contents — Mon, 12 Jan 2026 14:21:25 GMT

Advanced Energy Materials, EarlyView.

[ScienceDirect Publication: Computational Materials Science] Machine learning guided prediction of solute segregation at coherent and semi-coherent metal/oxide interfaces

ScienceDirect Publication: Computational Materials Science — Mon, 12 Jan 2026 12:46:02 GMT

Publication date: October 2026

Source: Computational Materials Science, Volume 264

Author(s): Yizhou Lu, Blas Pedro Uberuaga, Samrat Choudhury

[cond-mat updates on arXiv.org] The effect of normal stress on stacking fault energy in face-centered cubic metals

ChemRxiv — Tue, 13 Jan 2026 00:00:00 GMT

arXiv:2601.05453v1 Announce Type: new +Potentials with MMPBSA for Accurate Protein–Ligand Binding Free Energy Calculationshttps://dx.doi.org/10.26434/chemrxiv-2026-h0kn1?rft_dat=source%3DdrssEnd-point binding free energy (BFE) methods, such as molecular mechanics Poisson–Boltzmann surface area (MMPBSA), are widely used to estimate protein–ligand binding affinity due to their favorable balance between accuracy and computational efficiency. Their reliability, however, is often limited by approximations in intramolecular interactions and solvation effects. Given the critical role of force field quality in determining accuracy, we developed a hybrid framework named AIQM-PBSA, which integrates the ONIOM scheme with the PBSA model. Within this framework, the AIQM3 machine learning interatomic potential (MLIP) —an advanced Δ-learning quantum mechanical (QM) model—is employed to refine the molecular mechanics (MM) energy term, while polar and non polar solvation contributions are evaluated under the PBSA formalism. Extensive validation across diverse protein–ligand systems demonstrates that AIQM-PBSA significantly improves the correlation with experimental binding affinities compared to MMPBSA based on classical force fields and other MLIPs, with a representative benchmark showing up to 31% higher Pearson correlation relative to the MMPBSA baseline and 16% higher than ANI-2x. Furthermore, incorporating entropic contributions can further provide modest, target-dependent improvements. In summary, AIQM-PBSA offers a robust and transferable framework that combines QM level accuracy with MM level efficiency, substantially enhancing the reliability of endpoint free energy calculations for biomolecular recognition.

[ChemRxiv] To Be or Not to Be: The Elusive Nature of Wheland-type Intermediates in Zeolite-Catalyzed Aromatic Alkylation Revealed by CCSD(T)-quality Metadynamics

ChemRxiv — Tue, 13 Jan 2026 00:00:00 GMT

[ChemRxiv] Retrieval-Augmented Large Language Models for Chemistry: A Comprehensive Survey

ChemRxiv — Tue, 13 Jan 2026 00:00:00 GMT

[ChemRxiv] A Construction of Arbitrary Order Internal Coordinate Transformations to Improve Studies of Large Amplitude Motions

ChemRxiv — Tue, 13 Jan 2026 00:00:00 GMT

[RSC - Chem. Sci. latest articles] Mitigation strategies for Li2CO3 contamination in garnet-type solid-state electrolytes: Formation mechanisms and interfacial engineering

RSC - Chem. Sci. latest articles — Tue, 13 Jan 2026 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC09699E, Review Article

Open Access

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

[ChemRxiv] Navigating high-dimensional fabrication-parameter space in organic photovoltaics device optimization by a multi-tier machine learning framework

ChemRxiv — Tue, 13 Jan 2026 00:00:00 GMT

[RSC - Chem. Sci. latest articles] The interlocking process in molecular machines explained by a combined approach: the nudged elastic band method and a machine learning potential

RSC - Chem. Sci. latest articles — Tue, 13 Jan 2026 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC08303F, Edge Article

Open Access

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

[RSC - Digital Discovery latest articles] LivePyxel: Accelerating image annotations with a Python-integrated webcam live streaming

RSC - Digital Discovery latest articles — Tue, 13 Jan 2026 00:00:00 GMT

Digital Discovery, 2025, Accepted Manuscript
DOI: 10.1039/D5DD00421G, Paper

Open Access

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

Uriel Garcilazo-Cruz, Joseph O. Okeme, Rodrigo Vargas-Hernandez
The lack of flexible annotation tools has hindered the deployment of AI models in some scientific areas. Most existing image annotation software requires users to upload a precollected dataset, which...
The content of this RSS Feed (c) The Royal Society of Chemistry

[iScience] Machine Learning Identifies Proteomic Risk Factors Across 23 Diseases

iScience — Tue, 13 Jan 2026 00:00:00 GMT

Achieving minimally invasive and rapid detection is a crucial goal in modern medicine. The comprehensive characterization of the blood proteome holds great promise in advancing our understanding of disease etiology, facilitating early diagnosis, risk stratification, and improved monitoring across various diseases and their subtypes. In this study, we collected plasma proteomes from over 3000 patients, representing 23 distinct diseases, encompassing a total of 1462 proteins. Based on histological knowledge, we developed a two-stage hierarchical multi-disease classifier and applied it to perform multi-disease classification on the collected proteomic data.

[iScience] Gut fungal landscape in colorectal cancer and its cross-kingdom interplay with gut microbial ecology

iScience — Tue, 13 Jan 2026 00:00:00 GMT

The gut microbiota is a key hallmark of colorectal cancer (CRC), yet gut fungi remain understudied. We characterized the gut fungal landscape and its associations with bacteria, metabolites, and trace elements in CRC using fecal samples from healthy controls (n = 401), colorectal polyp patients (n = 162), and CRC patients (n = 253). Fungal annotation was performed using genomic data from NCBI (PRJNA833221) as reference. Fungal diversity increased in CRC patients, with seven genera showing differential abundance.

[Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)] [ASAP] Multiscale Modeling of Solid Electrolyte Interphase Formation on Oxygen-Functionalized Graphite Anodes for Lithium-Ion Batteries

Journal of Chemical Theory and Computation: Latest Articles (ACS Publications) — Mon, 12 Jan 2026 20:10:43 GMT

Journal of Chemical Theory and Computation

DOI: 10.1021/acs.jctc.5c01561

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Ultrahigh Ionic Conductivity in Halide Electrolytes Enabled by Anion Framework Flexibility Engineering

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Mon, 12 Jan 2026 17:43:24 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c15937

[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Mechanochemistry-Driven Optimization of Halide-Based Solid-State Electrolytes via Orthogonal Design of Experiments and Regression Modeling

ACS Materials Letters: Latest Articles (ACS Publications) — Mon, 12 Jan 2026 16:07:51 GMT

ACS Materials Letters

DOI: 10.1021/acsmaterialslett.5c01492

[Wiley: Small: Table of Contents] Multifunctional Cellulose Derivative Enables Efficient and Stable Wide‐Bandgap Perovskite Solar Cells by Inhibiting Ion Migration

Wiley: Small: Table of Contents — Mon, 12 Jan 2026 15:04:35 GMT

Small, EarlyView.

[Wiley: Advanced Energy Materials: Table of Contents] “Ionic Tug‐of‐War” Effect Decoupling Li+‐Coordination Enables High Ion Conductivity and Interface Stability for Solid‐State Electrolytes

Wiley: Advanced Energy Materials: Table of Contents — Mon, 12 Jan 2026 14:21:25 GMT

Advanced Energy Materials, EarlyView.

[ScienceDirect Publication: Computational Materials Science] Machine learning guided prediction of solute segregation at coherent and semi-coherent metal/oxide interfaces

ScienceDirect Publication: Computational Materials Science — Mon, 12 Jan 2026 12:46:02 GMT

Publication date: October 2026

Source: Computational Materials Science, Volume 264

Author(s): Yizhou Lu, Blas Pedro Uberuaga, Samrat Choudhury

[cond-mat updates on arXiv.org] The effect of normal stress on stacking fault energy in face-centered cubic metals

cond-mat updates on arXiv.org — Mon, 12 Jan 2026 05:00:00 GMT

arXiv:2601.05453v1 Announce Type: new Abstract: Plastic deformation and fracture of FCC metals involve the formation of stable or unstable stacking faults (SFs) on (111) plane. Examples include dislocation cross-slip and dislocation nucleation at interfaces and near crack tips. The stress component normal to (111) plane can strongly affect the SF energy when the stress magnitude reaches several to tens of GPa. We conduct a series of DFT calculations of SF energies in six FCC metals: Al, Ni, Cu, Ag, Au, and Pt. The results show that normal compression significantly increases the stable and unstable SF energies in all six metals, while normal tension decreases them. The SF formation is accompanied by inelastic expansion in the normal direction. The DFT calculations are compared with predictions of several representative classical and machine-learning interatomic potentials. Many potentials fail to capture the correct stress effect on the SF energy, often predicting trends opposite to the DFT calculations. Possible ways to improve the ability of potentials to represent the stress effect on SF energy are discussed.

[cond-mat updates on arXiv.org] Autonomous Discovery of the Ising Model's Critical Parameters with Reinforcement Learning

cond-mat updates on arXiv.org — Mon, 12 Jan 2026 05:00:00 GMT

arXiv:2601.05577v1 Announce Type: new Abstract: Traditional methods for determining critical parameters are often influenced by human factors. This research introduces a physics-inspired adaptive reinforcement learning framework that enables agents to autonomously interact with physical environments, simultaneously identifying both the critical temperature and various types of critical exponents in the Ising model with precision. Interestingly, our algorithm exhibits search behavior reminiscent of phase transitions, efficiently converging to target parameters regardless of initial conditions. Experimental results demonstrate that this method significantly outperforms traditional approaches, particularly in environments with strong perturbations. This study not only incorporates physical concepts into machine learning to enhance algorithm interpretability but also establishes a new paradigm for scientific exploration, transitioning from manual analysis to autonomous AI discovery.

[cond-mat updates on arXiv.org] Phase Frustration Induced Intrinsic Bose Glass in the Kitaev-Bose-Hubbard Model

cond-mat updates on arXiv.org — Mon, 12 Jan 2026 05:00:00 GMT

arXiv:2601.05781v1 Announce Type: new Abstract: We report an intrinsic "Bubble Phase" in the two-dimensional Kitaev-Bose-Hubbard model, driven purely by phase frustration between complex hopping and anisotropic pairing. By combining Inhomogeneous Gutzwiller Mean-Field Theory with a Bogoliubov-de Gennes stability analysis augmented by a novel Energy Penalty Method, we demonstrate that this phase spontaneously fragments into coherent islands, exhibiting the hallmark Bose glass signature of finite compressibility without global superfluidity. Notably, we propose a unified framework linking disorder-driven localization to deterministic phase frustration, identifying the Bubble Phase as a pristine, disorder-free archetype of the Bose glass. Our results provide a theoretical blueprint for realizing glassy dynamics in clean quantum simulators.

My Customized Papers

[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

[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

[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

[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

[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

[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

[cond-mat updates on arXiv.org] Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery

[cond-mat updates on arXiv.org] MiST: Understanding the Role of Mid-Stage Scientific Training in Developing Chemical Reasoning Models

[cond-mat updates on arXiv.org] Exact Multimode Quantization of Superconducting Circuits via Boundary Admittance

[ChemRxiv] A data-efficient reactive machine learning potential to accelerate automated exploration of complex reaction networks

[Nature Communications] A unified time-frequency foundation model for sleep decoding

[npj Computational Materials] Physically interpretable interatomic potentials via symbolic regression and reinforcement learning

[ChemRxiv] Can simple exchange heuristics guide us in predicting magnetic properties of solids?

[ChemRxiv] AIQM-PBSA: Integrating Machine Learning Interatomic -Potentials with MMPBSA for Accurate Protein–Ligand Binding Free Energy Calculations

[ChemRxiv] To Be or Not to Be: The Elusive Nature of Wheland-type Intermediates in Zeolite-Catalyzed Aromatic Alkylation Revealed by CCSD(T)-quality Metadynamics

[ChemRxiv] Retrieval-Augmented Large Language Models for Chemistry: A Comprehensive Survey

[ChemRxiv] A Construction of Arbitrary Order Internal Coordinate Transformations to Improve Studies of Large Amplitude Motions

[RSC - Chem. Sci. latest articles] Mitigation strategies for Li2CO3 contamination in garnet-type solid-state electrolytes: Formation mechanisms and interfacial engineering

[ChemRxiv] Navigating high-dimensional fabrication-parameter space in organic photovoltaics device optimization by a multi-tier machine learning framework

[RSC - Chem. Sci. latest articles] The interlocking process in molecular machines explained by a combined approach: the nudged elastic band method and a machine learning potential

[Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)] [ASAP] Multiscale Modeling of Solid Electrolyte Interphase Formation on Oxygen-Functionalized Graphite Anodes for Lithium-Ion Batteries

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Ultrahigh Ionic Conductivity in Halide Electrolytes Enabled by Anion Framework Flexibility Engineering

[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Mechanochemistry-Driven Optimization of Halide-Based Solid-State Electrolytes via Orthogonal Design of Experiments and Regression Modeling

[Wiley: Small: Table of Contents] Multifunctional Cellulose Derivative Enables Efficient and Stable Wide‐Bandgap Perovskite Solar Cells by Inhibiting Ion Migration

[Wiley: Advanced Energy Materials: Table of Contents] “Ionic Tug‐of‐War” Effect Decoupling Li+‐Coordination Enables High Ion Conductivity and Interface Stability for Solid‐State Electrolytes

[ScienceDirect Publication: Computational Materials Science] Machine learning guided prediction of solute segregation at coherent and semi-coherent metal/oxide interfaces

[cond-mat updates on arXiv.org] The effect of normal stress on stacking fault energy in face-centered cubic metals

[ChemRxiv] To Be or Not to Be: The Elusive Nature of Wheland-type Intermediates in Zeolite-Catalyzed Aromatic Alkylation Revealed by CCSD(T)-quality Metadynamics

[ChemRxiv] Retrieval-Augmented Large Language Models for Chemistry: A Comprehensive Survey

[ChemRxiv] A Construction of Arbitrary Order Internal Coordinate Transformations to Improve Studies of Large Amplitude Motions

[RSC - Chem. Sci. latest articles] Mitigation strategies for Li2CO3 contamination in garnet-type solid-state electrolytes: Formation mechanisms and interfacial engineering

[ChemRxiv] Navigating high-dimensional fabrication-parameter space in organic photovoltaics device optimization by a multi-tier machine learning framework

[RSC - Chem. Sci. latest articles] The interlocking process in molecular machines explained by a combined approach: the nudged elastic band method and a machine learning potential

[RSC - Digital Discovery latest articles] LivePyxel: Accelerating image annotations with a Python-integrated webcam live streaming

[iScience] Machine Learning Identifies Proteomic Risk Factors Across 23 Diseases

[iScience] Gut fungal landscape in colorectal cancer and its cross-kingdom interplay with gut microbial ecology

[Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)] [ASAP] Multiscale Modeling of Solid Electrolyte Interphase Formation on Oxygen-Functionalized Graphite Anodes for Lithium-Ion Batteries

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Ultrahigh Ionic Conductivity in Halide Electrolytes Enabled by Anion Framework Flexibility Engineering

[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Mechanochemistry-Driven Optimization of Halide-Based Solid-State Electrolytes via Orthogonal Design of Experiments and Regression Modeling

[Wiley: Small: Table of Contents] Multifunctional Cellulose Derivative Enables Efficient and Stable Wide‐Bandgap Perovskite Solar Cells by Inhibiting Ion Migration

[Wiley: Advanced Energy Materials: Table of Contents] “Ionic Tug‐of‐War” Effect Decoupling Li+‐Coordination Enables High Ion Conductivity and Interface Stability for Solid‐State Electrolytes

[ScienceDirect Publication: Computational Materials Science] Machine learning guided prediction of solute segregation at coherent and semi-coherent metal/oxide interfaces

[cond-mat updates on arXiv.org] The effect of normal stress on stacking fault energy in face-centered cubic metals

[cond-mat updates on arXiv.org] Autonomous Discovery of the Ising Model's Critical Parameters with Reinforcement Learning

[cond-mat updates on arXiv.org] Phase Frustration Induced Intrinsic Bose Glass in the Kitaev-Bose-Hubbard Model

[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