My Customized Papers

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

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

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

[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 @@ -26,7 +26,8 @@ Abstract: We report comprehensive measurements of the refractive index as a func Abstract: The common exact diagonalization-based techniques to solving tight-binding models suffer from O(N^2) and O(N^3) scaling with respect to model size in memory and CPU time, hindering their applications in large tight-binding models. On the contrary, the tight-binding propagation method (TBPM) can achieve linear scaling in both memory and CPU time, and is capable of handling large tight-binding models with billions of orbitals. In this paper, we introduce version 2.0 of TBPLaS, a package for large-scale simulation based on TBPM. This new version brings significant improvements with many new features. Existing Python/Cython modeling tools have been thoroughly optimized, and a compatible C++ implementation of the modeling tools is now available, offering efficiency enhancement of several orders. The solvers have been rewritten in C++ from scratch, with the efficiency enhanced by several times or even by an order of magnitude. The workflow of utilizing solvers has also been unified into a more comprehensive and consistent manner. New features include spin texture, Berry curvature and Chern number calculation, partial diagonalization for specific eigenvalues and eigenstates, analytical Hamiltonian, and GPU computing support. The documentation and tutorials have also been updated to the new version. In this paper, we discuss the revisions with respect to version 1.3 and demonstrate the new features. Benchmarks on modeling tools and solvers are also provided.

[cond-mat updates on arXiv.org] Symbolic regression for defect interactions in 2D materials

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

arXiv:2512.20785v2 Announce Type: replace-cross 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.

[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

[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 — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2601.05453v1 Announce Type: new +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

[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

[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

[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.

[cond-mat updates on arXiv.org] A Critical Examination of Active Learning Workflows in Materials Science

cond-mat updates on arXiv.org — Tue, 06 Jan 2026 05:00:00 GMT

arXiv:2601.05946v1 Announce Type: new @@ -93,7 +94,7 @@ Abstract: Accurate prediction of radiation damage in YBa$_2$Cu$3$O${7-\delta}$ ( Abstract: We present quantum heat machines using a diatomic molecule modelled by a $q$-deformed potential as a working medium. We analyze the effect of the deformation parameter and other potential parameters on the work output and efficiency of the quantum Otto and quantum Carnot heat cycles. Furthermore, we derive the analytical expressions of work and efficiency as a function of these parameters. Interestingly, our system operates as a quantum heat engine across the range of parameters considered. In addition, the efficiency of the quantum Otto heat engine is seen to be tunable by the deformation parameter. Our findings provide useful insight for understanding the impact of anharmonicity on the design of quantum thermal machines.

[ChemRxiv] A Systematic Review of Prompt Engineering Paradigms in Organic Chemistry: Mining, Prediction, and Model Architectures

ChemRxiv — Tue, 06 Jan 2026 00:00:00 GMT

Large language models (LLMs) have emerged as transformative tools in scientific research, offering a powerful alternative to traditional, resource-intensive machine learning methods. By leveraging the vast knowledge encoded during pre-training, prompt engineering—the systematic design and optimization of input instructions—enables researchers to guide LLMs toward accurate and domain-specific outputs without updating model parameters. This review presents the first systematic examination of prompt engineering techniques within organic chemistry, focusing on two critical application areas: text mining and predictive tasks. We analyze the core paradigms of prompt engineering, including prompt design, prompt learning, and prompt tuning, and clarify terminological inconsistencies in the literature. The discussion is contextualized within the three principal LLM architectures (encoder-only, decoder-only, and encoder-decoder), with an evaluation of their respective performances on chemistry-related tasks. Furthermore, we explore practical workflows for extracting structured chemical data from texts and knowledge graphs, as well as advanced prompt strategies for reaction condition prediction, reaction optimization, and catalytic performance prediction. This review highlights the significant potential of LLM-driven prompt engineering to accelerate discovery in organic chemistry, from synthetic pathway optimization to automated literature analysis, while also addressing persistent challenges such as the limitations associated with various prompt engineering techniques and the constraints related to each related sub-task. We conclude by outlining future research directions aimed at deepening the integration of chemical knowledge with evolving AI methodologies.

[ChemRxiv] Ensemble Analyzer: An Open-Source Python Framework for Automated Conformer Ensemble Refinement

ChemRxiv — Tue, 06 Jan 2026 00:00:00 GMT

Accurate prediction of molecular properties often requires considering the full conformational ensemble rather than a single optimized structure. While modern sampling tools have revolutionized conformational sampling by enabling the rapid generation of ensembles, the subsequent refinement at higher levels of theory remains computationally demanding and technically complex. Existing workflows typically rely on ad hoc scripts and manual intervention, limiting reproducibility and accessibility. Here, we present Ensemble Analyzer (EnAn), an open-source Python framework designed to automate the refinement and analysis of conformational ensembles. Built on the Atomic Simulation Environment (ASE), EnAn integrates seamlessly with widely known quantum chemistry engines such as ORCA and Gaussian, providing a modular and extensible architecture that streamlines the entire pipeline. EnAn also supports automated generation and comparison of electronic and vibronic spectra, enabling rapid visualization and interpretation. By minimizing manual data handling and standardizing workflows, EnAn effectively manages reproducible exploration of complex conformational spaces.

[ChemRxiv] Electrostatic Patterning Controls Mineral Nucleation Inside Ferritin

ChemRxiv — Tue, 06 Jan 2026 00:00:00 GMT

Ferritin protein nanocages store iron across nearly all living organisms. In mammals, two subtypes of ferritin exist: heavy (H) chain and light (L) chain. They have very similar 3D structures, but each performs a slightly different role in iron mineral formation. How sequence differences between the two subtypes affect mineral formation within the nanocages is still unclear. Single-particle reconstruction of cryo-TEM images was used to build models of unmineralized and partially mineralized human L-chain and H-chain ferritin, which showed that subtle differences in protein structure led to changes in the location of mineral formation within ferritin. Explicit-solvent atomistic molecular dynamics (MD) simulations were used to explore how sequence-dependent electrostatics modulate ion transport, cluster formation, and mineral nucleation within the confined environment of human L- and H-chain apoferritin nanocages. Employing NaCl as a computational probe, we show that the internal charge distribution governs ion selectivity and nucleation pathways. Analysis of liquid and solid ionic clusters, combined with Markov State Models (MSMs), reveals that mineralization proceeds through a two-step mechanism involving dense liquid-like precursors that crystallize homogeneously within the cavity. These findings provide molecular insight into how ferritin sequence variability tunes confinement-driven nucleation and suggest general principles for designing biomimetic nanoreactors.

[ChemRxiv] Machine Learning Prediction of Henry Coefficients of Polar and Nonpolar Gases in Covalent Organic Frameworks: Effects of Interlayer Shifts and Functionalization

ChemRxiv — Tue, 06 Jan 2026 00:00:00 GMT

Covalent organic frameworks (COFs) are promising materials for gas separation and carbon capture. Computational techniques based on Monte Carlo simulation can be used to predict the gas adsorption properties of COFs with high accuracy, however they are too inefficient to be deployed in a high-throughput manner for screening large COF databases. In this paper, we systematically train and evaluate a range of machine learning models for predicting the Henry coefficients for CO2 and CH4 gas adsorption in COF materials. To account for COF structural variability, we train our models on datasets that include both chemically functionalized frameworks and interlayer displaced stacking configurations. By comparing predictive performance across descriptor–model architecture combinations, we demonstrate how different models capture the key physical factors governing gas adsorption, including electrostatics, local atomic environments, and van der Waals interactions. Our results therefore provide a framework for building machine learning models for scalable, high-throughput screening of COF materials with targeted gas adsorption properties.

[ChemRxiv] Bridging the Gap: Aqueous Phase Organic Synthesis as a Foundation for Advanced Chemical and Biological Discovery

ChemRxiv — Tue, 06 Jan 2026 00:00:00 GMT

For over a century, synthetic chemistry demanded the rigorous exclusion of water, relying on toxic, volatile organic solvents. This paradigm, while successful, is environmentally and economically costly. This review advocates for a fundamental shift: adopting water not just as a green solvent, but as a transformative medium that reshapes our understanding of reactivity and bridges chemistry with biology. Aqueous phase organic synthesis (APOS) has evolved from accidental observations to a deliberate discipline. Water’s unique properties—its high polarity, hydrogen-bonding capacity, and the hydrophobic effect—make it an active participant. This effect drives reactant aggregation and stabilizes transition states, leading to dramatic rate enhancements in pericyclic and condensation reactions. A broad range of reactions thrive in water, including classical carbon–carbon bond-forming reactions like the aldol and Diels–Alder, and modern cross-couplings (e.g., Suzuki–Miyaura) enabled by water-tolerant catalysts. Multicomponent and click chemistries are particularly powerful. Challenges like poor solubility are addressed with micellar catalysis, water-soluble ligands, and precise control of the reaction microenvironment. Beyond sustainability, APOS drives discovery, often yielding improved selectivities, new pathways, and streamlined syntheses of complex targets like pharmaceuticals. Its greatest promise lies in interfacing with biology. Bioorthogonal reactions, such as azide–alkyne cycloadditions, enable labeling and imaging in living organisms. Aqueous compatibility is essential for in situ therapeutic strategies, chemical biology, and advanced bioconjugation techniques for modifying biomolecules. The future converges with emerging technologies: machine learning to navigate complex aqueous systems, flow chemistry for scalability, and the integration of enzymatic with synthetic catalysis. This points toward a unified chemical-biological engineering paradigm. In conclusion, APOS is a mature, versatile field. It is a cornerstone of green chemistry and a critical bridge to biology, accelerating progress in medicine and materials science. Embracing water is both an environmental imperative and a strategic pathway to the next generation of scientific discovery. Introduction: The Solvent Problem in Organic Synthesis -For generations, organic synthesis has been defined by precise control over molecular structure carried out in rigorously dried environments. Since the emergence of modern organic chemistry in the nineteenth century, water – the very medium that sustains life – has been regarded as an obstacle to chemical transformation. This long-standing assumption has shaped laboratory routines, industrial manufacturing, and chemical education, reinforcing the idea that successful synthesis depends on strict exclusion of moisture. This introduction revisits that historical mindset, evaluates its environmental and economic consequences, and presents the case for a fundamental transition toward aqueous phase organic synthesis (APOS) as a more suitable platform for future chemical and biological innovation.

[RSC - Chem. Sci. latest articles] Reactive Fluorescent Probe for Covalent Membrane-Anchoring: Enabling Real-time Imaging of Protein Aggregation Dynamics in Live Cells

RSC - Chem. Sci. latest articles — Tue, 06 Jan 2026 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC07716H, Edge Article

Open Access

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

Hongbei Wei, Liren Xu, Ke Wei, Wenhai Bian, Yifan Wen, Wanyi Yu, Hui Zhang, Tony D. James, Xiaolong Sun
Aberrant aggregation of membrane proteins is a pathological hallmark of various diseases, including neurodegenerative disorders and cancer. The visualization of membrane protein aggregation has emerged as an important approach for...
The content of this RSS Feed (c) The Royal Society of Chemistry

[npj Computational Materials] Composition-dependent dislocation mobility in FeNiCrCoCu high-entropy alloys based on atomistic simulations and machine learning analysis

npj Computational Materials — Tue, 06 Jan 2026 00:00:00 GMT

npj Computational Materials, Published online: 06 January 2026; doi:10.1038/s41524-025-01942-6

Composition-dependent dislocation mobility in FeNiCrCoCu high-entropy alloys based on atomistic simulations and machine learning analysis

[npj Computational Materials] AI-assisted rapid crystal structure generation towards a target local environment

npj Computational Materials — Tue, 06 Jan 2026 00:00:00 GMT

npj Computational Materials, Published online: 06 January 2026; doi:10.1038/s41524-025-01931-9

AI-assisted rapid crystal structure generation towards a target local environment

[Applied Physics Letters Current Issue] Polarization-controlled multistate thermal conductivity in ferroelectric HfO 2 thin films

Applied Physics Letters Current Issue — Tue, 06 Jan 2026 00:00:00 GMT

While nanoscale electronic logic circuits are well established, the development of nanoscale thermal logic circuits has been slow, mainly due to the absence of efficient and controllable nonvolatile field-effect thermal transistors. In this study, we investigate polarization-dependent thermal conductivity in ferroelectric orthorhombic hafnium dioxide (o-HfO₂) thin films. Using molecular dynamics simulations with machine learning potentials, we show that a 24-nm-long o-HfO₂ film can exhibit four distinct and stable thermal conductivity states arising from different ferroelectric polarization configurations. Notably, these states achieve a maximum switching ratio of 160.8% under 2% tensile strain. Our results suggest a practical pathway toward nonvolatile field-effect thermal transistors.

[ScienceDirect Publication: Journal of Catalysis] Microkinetic modeling of methane activation in Mo/ZSM-5 with machine learning potentials

ScienceDirect Publication: Journal of Catalysis — Mon, 05 Jan 2026 18:32:11 GMT

Publication date: February 2026

Source: Journal of Catalysis, Volume 454

Author(s): Yanqi Huang, Xiang Ryan Zhou, Brandon C. Bukowski

[Wiley: Advanced Functional Materials: Table of Contents] Thermoplastic Elastomeric Bottlebrush Copolymer Glue Electrolyte Featuring Dual‐Ion Transport Channels and Strong Segregation: Experimental and Simulation Insights (Adv. Funct. Mater. 2/2026)

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Regulating Metallic Deposition Behavior by Gradient Alloy/Solid Electrolyte Interphase for Durable Na/K Anodes (Adv. Funct. Mater. 2/2026)

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Autonomous Liquid Metal Droplets Actuated by Ion Diffusion

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Microcrack‐Structured Visualizable Hydrogel Sensor for Machine Learning‐Assisted Handwriting Recognition

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Regulating Metallic Deposition Behavior by Gradient Alloy/Solid Electrolyte Interphase for Durable Na/K Anodes

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Thermoplastic Elastomeric Bottlebrush Copolymer Glue Electrolyte Featuring Dual‐Ion Transport Channels and Strong Segregation: Experimental and Simulation Insights

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Science: Table of Contents] CGRP Enhances the Regeneration of Bone Defects by Regulating Bone Marrow Mesenchymal Stem Cells Through Promoting ANGPTL4 Secretion by Bone Blood Vessels

Wiley: Advanced Science: Table of Contents — Mon, 05 Jan 2026 09:55:39 GMT

Advanced Science, EarlyView.

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Pd/Cu Dual Catalysis for Stereodivergent Allylic Alkylation of α-F-Substituted Azaaryl Acetates and Acetamides with Morita–Baylis–Hillman Carbonates

Chinese Chemical Society: CCS Chemistry: Table of Contents — Mon, 05 Jan 2026 07:20:22 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202506840

A palladium/copper dual catalytic system has been developed for the asymmetric allylic alkylation of Morita–Baylis–Hillman carbonates with α-fluoro-2-azaaryl acetates. This system delivers a series of chiral fluorinated compounds featuring an azaaryl ...

[Wiley: Carbon Energy: Table of Contents] Strategies to Enhance Ionic Conductivity of Na3Zr2Si2O12 Solid Electrolyte for Advanced Solid‐State Sodium Batteries

Wiley: Carbon Energy: Table of Contents — Mon, 05 Jan 2026 07:00:12 GMT

Carbon Energy, EarlyView.

[Wiley: Advanced Science: Table of Contents] Diffusion‐MRI‐Based Estimation of Cortical Architecture via Machine Learning (DECAM) in Primate Brains

Wiley: Advanced Science: Table of Contents — Mon, 05 Jan 2026 05:33:28 GMT

Advanced Science, EarlyView.

[cond-mat updates on arXiv.org] Automated electrostatic characterization of quantum dot devices in single- and bilayer heterostructures

ChemRxiv — Tue, 06 Jan 2026 00:00:00 GMT

arXiv:2601.00067v1 Announce Type: new +For generations, organic synthesis has been defined by precise control over molecular structure carried out in rigorously dried environments. Since the emergence of modern organic chemistry in the nineteenth century, water – the very medium that sustains life – has been regarded as an obstacle to chemical transformation. This long-standing assumption has shaped laboratory routines, industrial manufacturing, and chemical education, reinforcing the idea that successful synthesis depends on strict exclusion of moisture. This introduction revisits that historical mindset, evaluates its environmental and economic consequences, and presents the case for a fundamental transition toward aqueous phase organic synthesis (APOS) as a more suitable platform for future chemical and biological innovation.

[RSC - Chem. Sci. latest articles] Reactive Fluorescent Probe for Covalent Membrane-Anchoring: Enabling Real-time Imaging of Protein Aggregation Dynamics in Live Cells

RSC - Chem. Sci. latest articles — Tue, 06 Jan 2026 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC07716H, Edge Article

Open Access

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

[npj Computational Materials] Composition-dependent dislocation mobility in FeNiCrCoCu high-entropy alloys based on atomistic simulations and machine learning analysis

npj Computational Materials — Tue, 06 Jan 2026 00:00:00 GMT

npj Computational Materials, Published online: 06 January 2026; doi:10.1038/s41524-025-01942-6

Composition-dependent dislocation mobility in FeNiCrCoCu high-entropy alloys based on atomistic simulations and machine learning analysis

[npj Computational Materials] AI-assisted rapid crystal structure generation towards a target local environment

npj Computational Materials — Tue, 06 Jan 2026 00:00:00 GMT

npj Computational Materials, Published online: 06 January 2026; doi:10.1038/s41524-025-01931-9

AI-assisted rapid crystal structure generation towards a target local environment

[Applied Physics Letters Current Issue] Polarization-controlled multistate thermal conductivity in ferroelectric HfO 2 thin films

Applied Physics Letters Current Issue — Tue, 06 Jan 2026 00:00:00 GMT

[RSC - Chem. Sci. latest articles] Tailoring terminal groups in sulfonyl solvents to boost compatibility with lithium metal anodes

RSC - Chem. Sci. latest articles — Tue, 06 Jan 2026 00:00:00 GMT

Chem. Sci., 2026, Advance Article
DOI: 10.1039/D5SC09242F, Edge Article

Open Access

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

Jinmin Wang, Shuang Wei, Mingming Fang, Angye Li, Qian Zheng, Xubing Dong, Yuanmao Chen, Kang Yuan, Xinyang Yue, Zheng Liang
The synthesis of a multifunctional N,N-dimethylsulfamoyl fluoride (DMSF) solvent overcomes key limitations of conventional sulfonyl-based electrolytes including high viscosity, low ionic conductivity, and poor lithium metal compatibility.
To cite this article before page numbers are assigned, use the DOI form of citation above.
The content of this RSS Feed (c) The Royal Society of Chemistry

[ScienceDirect Publication: Journal of Catalysis] Microkinetic modeling of methane activation in Mo/ZSM-5 with machine learning potentials

ScienceDirect Publication: Journal of Catalysis — Mon, 05 Jan 2026 18:32:11 GMT

Publication date: February 2026

Source: Journal of Catalysis, Volume 454

Author(s): Yanqi Huang, Xiang Ryan Zhou, Brandon C. Bukowski

[Wiley: Advanced Functional Materials: Table of Contents] Thermoplastic Elastomeric Bottlebrush Copolymer Glue Electrolyte Featuring Dual‐Ion Transport Channels and Strong Segregation: Experimental and Simulation Insights (Adv. Funct. Mater. 2/2026)

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Regulating Metallic Deposition Behavior by Gradient Alloy/Solid Electrolyte Interphase for Durable Na/K Anodes (Adv. Funct. Mater. 2/2026)

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Autonomous Liquid Metal Droplets Actuated by Ion Diffusion

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Microcrack‐Structured Visualizable Hydrogel Sensor for Machine Learning‐Assisted Handwriting Recognition

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Regulating Metallic Deposition Behavior by Gradient Alloy/Solid Electrolyte Interphase for Durable Na/K Anodes

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Thermoplastic Elastomeric Bottlebrush Copolymer Glue Electrolyte Featuring Dual‐Ion Transport Channels and Strong Segregation: Experimental and Simulation Insights

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Science: Table of Contents] CGRP Enhances the Regeneration of Bone Defects by Regulating Bone Marrow Mesenchymal Stem Cells Through Promoting ANGPTL4 Secretion by Bone Blood Vessels

Wiley: Advanced Science: Table of Contents — Mon, 05 Jan 2026 09:55:39 GMT

Advanced Science, EarlyView.

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Pd/Cu Dual Catalysis for Stereodivergent Allylic Alkylation of α-F-Substituted Azaaryl Acetates and Acetamides with Morita–Baylis–Hillman Carbonates

Chinese Chemical Society: CCS Chemistry: Table of Contents — Mon, 05 Jan 2026 07:20:22 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202506840

[Wiley: Carbon Energy: Table of Contents] Strategies to Enhance Ionic Conductivity of Na3Zr2Si2O12 Solid Electrolyte for Advanced Solid‐State Sodium Batteries

Wiley: Carbon Energy: Table of Contents — Mon, 05 Jan 2026 07:00:12 GMT

Carbon Energy, EarlyView.

[Wiley: Advanced Science: Table of Contents] Diffusion‐MRI‐Based Estimation of Cortical Architecture via Machine Learning (DECAM) in Primate Brains

Wiley: Advanced Science: Table of Contents — Mon, 05 Jan 2026 05:33:28 GMT

Advanced Science, EarlyView.

[cond-mat updates on arXiv.org] Automated electrostatic characterization of quantum dot devices in single- and bilayer heterostructures

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

arXiv:2601.00067v1 Announce Type: new Abstract: As quantum dot (QD)-based spin qubits advance toward larger, more complex device architectures, rapid, automated device characterization and data analysis tools become critical. The orientation and spacing of transition lines in a charge stability diagram (CSD) contain a fingerprint of a QD device's capacitive environment, making these measurements useful tools for device characterization. However, manually interpreting these features is time-consuming, error-prone, and impractical at scale. Here, we present an automated protocol for extracting underlying capacitive properties from CSDs. Our method integrates machine learning, image processing, and object detection to identify and track charge transitions across large datasets without manual labeling. We demonstrate this method using experimentally measured data from a strained-germanium single-quantum-well (planar) and a strained-germanium double-quantum-well (bilayer) QD device. Unlike for planar QD devices, CSDs in bilayer germanium heterostructure exhibit a larger set of transitions, including interlayer tunneling and distinct loading lines for the vertically stacked QDs, making them a powerful testbed for automation methods. By analyzing the properties of many CSDs, we can statistically estimate physically relevant quantities, like relative lever arms and capacitive couplings. Thus, our protocol enables rapid extraction of useful, nontrivial information about QD devices.

[cond-mat updates on arXiv.org] Atomic-Scale Mechanisms of Li-Ion Transport Mediated by Li10GeP2S12 in Composite Solid Polyethylene Oxide Electrolytes

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

arXiv:2601.00112v1 Announce Type: new Abstract: Polymer electrolytes incorporating Li$_{10}$GeP$_{2}$S$_{12}$ (LGPS) nanoparticles show promise for solid-state lithium batteries owing to their enhanced ionic conductivity, though the governing mechanisms remain unclear. We combine molecular dynamics (MD) simulations, experimental ionic conductivity measurements, and density functional theory (DFT) calculations to elucidate the effect of LGPS loading on polyethylene oxide (PEO) structure and Li-ion transport. MD and experimental results agree up to 10\% LGPS, showing a volcano-shaped conductivity trend driven by polymer segmental dynamics and interfacial effects. Beyond 10\%, experiments reveal additional conductivity enhancement unexplained by MD, suggesting a distinct transport regime. DFT calculations indicate that Li-ion migration at the PEO|LGPS interface proceeds via vacancy-mediated hopping, with low barriers favored by S-rich interfacial sites and hindered by Ge. These findings link interfacial chemistry and microstructure to Li-ion dynamics, offering guidelines for designing high-performance composite polymer electrolytes.

[cond-mat updates on arXiv.org] Engineering Ideal 2D Type-II Nodal Line Semimetals via Stacking and Intercalation of van der Waals Layers

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

arXiv:2601.00407v1 Announce Type: new Abstract: Two-dimensional type-II topological semimetals (TSMs), characterized by strongly tilted Dirac cones, have attracted intense interest for their unconventional electronic properties and exotic transport behaviors. However, rational design remains challenging due to the sensitivity of band tilting to lattice geometry, atomic coordination, and symmetry constraints. Here, we present a bottom-up approach to engineer ideal type-II nodal line semimetals (NLSMs) in van der Waals bilayers via atomic intercalation. Using monolayer $h$-AlN as a prototype, we show that fluorine-intercalated bilayer AlN (F@BL-AlN) hosts a symmetry-protected type-II nodal loop precisely at the Fermi level, enabled by preserved mirror symmetry ($\mathcal{M}_z$) and tailored interlayer hybridization. First-principles calculations reveal that fluorine not only tunes interlayer coupling but also aligns the Fermi energy with the nodal line, stabilizing the type-II NLSM phase. The system exhibits tunable electronic properties under external electric and strain fields and features a van Hove singularity that induces spontaneous ferromagnetism, realizing a ferromagnetic topological semimetal state. This work provides a versatile platform for designing type-II NLSMs and offers practical guidance for their experimental realization.