My Customized Papers

[ScienceDirect Publication: Joule] A critical outlook for large-scale all-solid-state batteries

ScienceDirect Publication: Joule — Sun, 11 Jan 2026 01:50:45 GMT

Publication date: Available online 9 January 2026

Source: Joule

Author(s): Seongjae Ko, Makoto Ue, Atsuo Yamada

[Wiley: Advanced Functional Materials: Table of Contents] Recycling of Thermoplastics with Machine Learning: A Review

Wiley: Advanced Functional Materials: Table of Contents — Sat, 10 Jan 2026 15:14:36 GMT

Advanced Functional Materials, Volume 36, Issue 3, 8 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Electron Compensation Enhanced Triboelectric Sensor Assisted by Machine Learning for Tactile Perception Recognition

Wiley: Advanced Functional Materials: Table of Contents — Sat, 10 Jan 2026 15:14:36 GMT

Advanced Functional Materials, Volume 36, Issue 3, 8 January 2026.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Selective Ion Transport Regulation Enables High Current Density CO2‐to‐C2+ Conversion in Acid

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:07:04 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Triply Responsive Control of Ion Transport with an Artificial Channel Creates a Switchable AND to OR Logic Gate

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:04:51 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Coupled Engineering of Short‐/Long‐Range Disorder in Oxyhalides Unlocks Benchmark Sodium Superionic Conductor

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:04:51 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Atomistic Landscape of Pt Nanoparticles via Machine Learning: How Size Effect and Hydrogen Adsorption Govern Structural Ensembles and Catalytic Activity

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:04:51 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Empowering Chemistry Experts with Large Language Models for Literature Interpretation in Single‐Atom Catalysis Toward Advanced Oxidation

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:04:51 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Balancing Oxidative Stability and Ion Transport in Quasi‐Solid Polymer Electrolytes via Chlorine‐Driven Halogenation Engineering

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:04:51 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[Wiley: Advanced Energy Materials: Table of Contents] Machine Learning Driven High‐Throughput Screening of Asymmetric Dinuclear Cobalt for Nitrate‐to‐Ammonia Reduction with Near‐100% Selectivity

Wiley: Advanced Energy Materials: Table of Contents — Sat, 10 Jan 2026 14:09:28 GMT

Advanced Energy Materials, EarlyView.

[ChemRxiv] ConforFormer: representation for molecules through understanding of conformers

ChemRxiv — Sat, 10 Jan 2026 00:00:00 GMT

Recent years have seen a growing interest in machine learning approaches for chemical tasks. The best existing methods focus on building base models that combine molecular graphs (“2D structures”) with atomic coordinates in 3D to predict molecular properties, typically through pre-training followed by fine-tuning on benchmark datasets. However, current approaches require updating the weights of the entire model during the fine-tuning procedure for each prediction task. While this enables state-of-the-art performance, it limits practical deployment, as real-world datasets are often too small to support the stable retraining of large models. Importantly, the 3D geometry of a molecule holds crucial information for predicting its properties, but a single molecular graph usually corresponds to several 3D geometries, called conformers, introducing ambiguity into the inference process. Typical solutions rely on molecular graphs, but this approach is not easily generalizable beyond organic molecules. Here, we present ConforFormer, a method that explicitly accounts for the diversity of 3D conformations of a molecule to derive a task-agnostic and conformation-agnostic vector representation. This model serves as a foundational framework, producing embeddings that can be generated once and directly applied to downstream tasks, including property prediction and structural similarity, without extensive fine-tuning.

[ChemRxiv] Graph learning of sequence statistics for polymer representation

ChemRxiv — Sat, 10 Jan 2026 00:00:00 GMT

Polymers underpin critical technologies from medicine to energy, but their immense chemical and structural diversity makes rational design exceptionally difficult. Machine learning offers a way to navigate this space, yet prevailing approaches inherit small-molecule representations that fail to encode polymer-specific architecture; the distinction between random, block and other statistical copolymers is often collapsed into a categorical tag or ignored. Here, we introduce SCALE (Statistical Copolymer Architecture with Learning Edges), which recasts a copolymer as a Markovian sequence over a monomer alphabet and embeds the transition probabilities P(j/i) as edge features within a graph attention network. Message passing thus computes contextualized monomer states analogous to applying a transfer operator along the chain, while attention learns a data-driven kernel over paths that weights sequence heterogeneity versus block persistence. On a robotically synthesized, high-throughput fluorescence library, SCALE attained RMSE ≈228 and R² ≈0.84, surpassing polymer-adapted neural baselines and descriptor regressors (e.g., wDMPNN RMSE ≈326; XGBoost RMSE ≈254). The model is interpretable: edges dominate predictions for statistical (random) copolymers, whereas nodes prevail for block copolymers, consistent with NOESY 2D NMR. Beyond photophysics, SCALE generalized to antibacterial design across penta- and hexa-copolymer libraries with validation from <300 syntheses. By elevating sequence statistics to first-class learning variables, SCALE provides a generalizable, data-efficient route to closed-loop polymer discovery.

[npj Computational Materials] Machine learning for phase prediction of high entropy carbide ceramics from imbalanced data

npj Computational Materials — Sat, 10 Jan 2026 00:00:00 GMT

npj Computational Materials, Published online: 10 January 2026; doi:10.1038/s41524-025-01873-2

Machine learning for phase prediction of high entropy carbide ceramics from imbalanced data

[Wiley: Small Structures: Table of Contents] Dielectric Constant Guided Solvation Structure Design for Stable Solid Electrolyte Interphase in Lithium Metal Batteries

Wiley: Small Structures: Table of Contents — Fri, 09 Jan 2026 19:05:13 GMT

Small Structures, Volume 7, Issue 1, January 2026.

[ScienceDirect Publication: Journal of Energy Storage] External pressure's influence on lithium-ion transport within solid-state electrolytes

ScienceDirect Publication: Journal of Energy Storage — Fri, 09 Jan 2026 18:31:35 GMT

Publication date: 1 March 2026

Source: Journal of Energy Storage, Volume 149

Author(s): Yuan He, Xiongying Zhang, Dong Lu

[ScienceDirect Publication: Journal of Energy Storage] Alterative aqueous polymer anode binder enabling interfacial stabilization and improved lithium-ion transport

ScienceDirect Publication: Journal of Energy Storage — Fri, 09 Jan 2026 18:31:35 GMT

Publication date: 1 March 2026

Source: Journal of Energy Storage, Volume 149

Author(s): Yuan Peng, Huimin Chen, Xiaowen Qv, Ao Zeng, Jianfeng Xia, Jiangtao Xu, Kunkun Guo

[ScienceDirect Publication: Journal of Energy Storage] Interface engineering of Li_1.5Al_0.5Ge_1.5(PO₄)₃ electrolytes via in-situ polymer–cerium hybrid interlayers for high-performance all-solid-state lithium metal batteries

ScienceDirect Publication: Journal of Energy Storage — Fri, 09 Jan 2026 18:31:35 GMT

Publication date: 1 March 2026

Source: Journal of Energy Storage, Volume 149

Author(s): Kaiqi Wu, Chengjin Peng, Fanglin Wu, Liyuan Huang, Liang Lan, Liqiang Kang, Yecheng Liu, Xin Ao, Shan Fang

[ScienceDirect Publication: Computational Materials Science] A general LLM-powered text mining framework: Applied to extract high entropy alloys

ScienceDirect Publication: Computational Materials Science — Fri, 09 Jan 2026 18:31:33 GMT

Publication date: October 2026

Source: Computational Materials Science, Volume 264

Author(s): Haolun Yuan, Jun Zeng, Jie Zuo, Xin Wang, Dingguo Xu

[ScienceDirect Publication: Materials Today] Heteropolyanion regulation activating decoupled ion transition for Na superionic conductors

ScienceDirect Publication: Materials Today — Fri, 09 Jan 2026 18:31:29 GMT

Publication date: Available online 9 January 2026

Source: Materials Today

Author(s): Tian Jiang, Qi Fan, Wenshan Gou, Anyang Yu, Changhao Zhu, Ruirui Zhang, Youwei Dong, Shijun Yuan, Qingyu Xu

[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Correlating (Chemo-)Mechanical Coupling in TiS2 during Li+ Intercalation across Liquid and Solid Electrolytes Via Operando Analysis

ACS Energy Letters: Latest Articles (ACS Publications) — Fri, 09 Jan 2026 16:22:26 GMT

ACS Energy Letters

DOI: 10.1021/acsenergylett.5c02943

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Harnessing Entropic Effects from Interlayer Coupling to Modulate Ion Transport and Rectification in Multilayered Janus Graphene Nanopores

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Fri, 09 Jan 2026 12:51:38 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c17242

[Wiley: Advanced Science: Table of Contents] Uncertainty‐Quantified Primary Particle Size Prediction in Li‐Rich NCM Materials via Machine Learning and Chemistry‐Aware Imputation

Wiley: Advanced Science: Table of Contents — Fri, 09 Jan 2026 11:35:15 GMT

Advanced Science, Volume 13, Issue 2, 9 January 2026.

[Wiley: Advanced Science: Table of Contents] Highly Selective CO2 Reduction to Pure Formic Acid Using a Nafion‐TiO2 Composite Porous Solid Electrolyte

Wiley: Advanced Science: Table of Contents — Fri, 09 Jan 2026 11:35:15 GMT

Advanced Science, Volume 13, Issue 2, 9 January 2026.

[Wiley: Advanced Science: Table of Contents] Macroscopically Ordered Piezo‐Potential in All‐Polymetric Solid Electrolytes Responding to Li Anode Volume Changes for Dendrites Suppression

Wiley: Advanced Science: Table of Contents — Fri, 09 Jan 2026 11:35:15 GMT

Advanced Science, Volume 13, Issue 2, 9 January 2026.

[Recent Articles in Phys. Rev. B] Reinforcement fine-tuning for materials design

Recent Articles in Phys. Rev. B — Fri, 09 Jan 2026 10:00:00 GMT

Author(s): Zhendong Cao and Lei Wang

Reinforcement fine-tuning played an instrumental role in enhancing the instruction-following and reasoning abilities of large language models. In this work, we employ reinforcement fine-tuning for materials design, in which discriminative machine learning models are used to provide rewards to the au…

[Phys. Rev. B 113, 024106] Published Fri Jan 09, 2026

[cond-mat updates on arXiv.org] Fluctuation conductivity in ultraclean multicomponent superconductors

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 — Mon, 12 Jan 2026 05:00:00 GMT

arXiv:2601.05946v1 Announce Type: new +Abstract: Active learning (AL) plays a critical role in materials science, enabling applications such as the construction of machine-learning interatomic potentials for atomistic simulations and the operation of self-driving laboratories. Despite its widespread use, the reliability and effectiveness of AL workflows depend on implicit design assumptions that are rarely examined systematically. Here, we critically assess AL workflows deployed in materials science and investigate how key design choices, such as surrogate models, sampling strategies, uncertainty quantification and evaluation metrics, relate to their performance. By identifying common pitfalls and discussing practical mitigation strategies, we provide guidance to practitioners for the efficient design, assessment, and interpretation of AL workflows in materials science.

[cond-mat updates on arXiv.org] Autonomous Sampling and SHAP Interpretation of Deposition-Rates in Bipolar HiPIMS

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

arXiv:2601.05287v1 Announce Type: cross +Abstract: High-power impulse magnetron sputtering (HiPIMS) offers considerable control over ion energy and flux, making it invaluable for tailoring the microstructure and properties of advanced functional coatings. However, compared to conventional sputtering techniques, HiPIMS suffers from reduced deposition rates. Many groups have begun to evaluate complex pulsing schemes to improve upon this, leveraging multi-pulse schemes (e.g. pre-ionization or bipolar pulses). Unfortunately, the increased complexity of these pulsing schemes has led to high-dimensionality parameter spaces that are prohibitive to classic design of experi-ments. In this work we evaluate bipolar HiPIMS pulses for improving deposition rates of Al and Ti sputter tar-gets. Over 3000 process conditions were collected via autonomous Bayesian sampling over a 6-dimensional parameter space. These process conditions were then interpreted using Shapley Additive Explanations (SHAP), to deconvolute complex process influences on deposition rates. This allows us to link observed var-iations in deposition rate to physical mechanisms such as back-attraction and plasma ignition. Insights gained from this approach were then used to target specific processes where the positive pulse components were expected to have the highest impact on deposition rates. However, in practice, only minimal improve-ments in deposition rate were achieved. In most cases, the positive pulse appears to be detrimental when placed immediately after the neg. pulse which we hypothesize relates to quenching of the afterglow plasma. The proposed workflow combining autonomous experimentation and interpretable machine learning is broad-ly applicable to the discovery and optimization of complex plasma processes, paving the way for physics-informed, data-driven advancements in coating technologies.

[cond-mat updates on arXiv.org] VacHopPy: A Python package for vacancy hopping analysis based on molecular dynamics simulations

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

arXiv:2503.23467v2 Announce Type: replace +Abstract: Multiscale modeling, which integrates material properties from ab initio calculations into continuum-scale simulations, is a promising strategy for optimizing semiconductor devices. However, a key challenge remains: while ab initio methods provide diffusion parameters specific to individual migration paths, continuum equations require a single effective set of parameters that captures the overall diffusion behavior. To address this issue, we present VacHopPy, an open-source Python package for vacancy hopping analysis based on molecular dynamics (MD). VacHopPy extracts an effective set of hopping parameters, including hopping distance, hopping barrier, number of effective paths, correlation factor, and attempt frequency, by statistically integrating energetic, kinetic, and geometric contributions across all paths. It also includes tools for tracking vacancy trajectories and for detecting phase transitions during MD simulations. The applicability of VacHopPy is demonstrated in three representative materials: face-centered cubic Al, rutile TiO2, and monoclinic HfO2. The extracted effective parameters reproduce temperature-dependent diffusion behavior and are in good agreement with previous experimental data. Provided in a simplified form, these parameters are well suited for continuum-scale models and remain valid over a wide temperature range spanning several hundred kelvins. Furthermore, VacHopPy inherently accounts for anisotropy in thermal vibrations, a factor often overlooked, making it suitable for simulating diffusion in complex crystals. Overall, VacHopPy establishes a robust bridge between atomic- and continuum-scale models, enabling more reliable multiscale simulation

[cond-mat updates on arXiv.org] Machine Learning Framework for Characterizing Processing-Structure Relationship in Block Copolymer Thin Films

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

arXiv:2505.23064v2 Announce Type: replace +Abstract: The morphology of block copolymers (BCPs) critically influences material properties and applications. This work introduces a machine learning (ML)-enabled, high-throughput framework for analyzing grazing incidence small-angle X-ray scattering (GISAXS) data and atomic force microscopy (AFM) images to characterize BCP thin film morphology. A convolutional neural network was trained to classify AFM images by morphology type, achieving 97% testing accuracy. Classified images were then analyzed to extract 2D grain size measurements from the samples in a high-throughput manner. ML models were developed to predict morphological features based on processing parameters such as solvent ratio, additive type, and additive ratio. GISAXS-based properties were predicted with strong performances ($R^2$ > 0.75), while AFM-based property predictions were less accurate ($R^2$ < 0.60), likely due to the localized nature of AFM measurements compared to the bulk information captured by GISAXS. Beyond model performance, interpretability was addressed using Shapley Additive exPlanations (SHAP). SHAP analysis revealed that the additive ratio had the largest impact on morphological predictions, where additive provides the BCP chains with increased volume to rearrange into thermodynamically favorable morphologies. This interpretability helps validate model predictions and offers insight into parameter importance. Altogether, the presented framework combining high-throughput characterization and interpretable ML offers an approach to exploring and optimizing BCP thin film morphology across a broad processing landscape.

[cond-mat updates on arXiv.org] Efficient Band Structure Unfolding with Atom-centered Orbitals: General Theory and Application

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

arXiv:2506.21089v2 Announce Type: replace +Abstract: Band structure unfolding is a key technique for analyzing and simplifying the electronic band structure of large, internally distorted supercells that break the primitive cell's translational symmetry. In this work, we present an efficient band unfolding method for atomic orbital (AO) basis sets that explicitly accounts for both the non-orthogonality of atomic orbitals and their atom-centered nature. Unlike existing approaches that typically rely on a plane-wave representation of the (semi-)valence states, we here derive analytical expressions that recasts the primitive cell translational operator and the associated Bloch-functions in the supercell AO basis. In turn, this enables the accurate and efficient unfolding of conduction, valence, and core states in all-electron codes, as demonstrated by our implementation in the all-electron ab initio simulation package FHI-aims, which employs numeric atom-centered orbitals. We explicitly demonstrate the capability of running large-scale unfolding calculations for systems with thousands of atoms and showcase the importance of this technique for computing temperature-dependent spectral functions in strongly anharmonic materials using CuI as example.

[cond-mat updates on arXiv.org] Disordered Dynamics in High Dimensions: Connections to Random Matrices and Machine Learning

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

arXiv:2601.01010v2 Announce Type: replace +Abstract: We provide an overview of high dimensional dynamical systems driven by random matrices, focusing on applications to simple models of learning and generalization in machine learning theory. Using both cavity method arguments and path integrals, we review how the behavior of a coupled infinite dimensional system can be characterized as a stochastic process for each single site of the system. We provide a pedagogical treatment of dynamical mean field theory (DMFT), a framework that can be flexibly applied to these settings. The DMFT single site stochastic process is fully characterized by a set of (two-time) correlation and response functions. For linear time-invariant systems, we illustrate connections between random matrix resolvents and the DMFT response. We demonstrate applications of these ideas to machine learning models such as gradient flow, stochastic gradient descent on random feature models and deep linear networks in the feature learning regime trained on random data. We demonstrate how bias and variance decompositions (analysis of ensembling/bagging etc) can be computed by averaging over subsets of the DMFT noise variables. From our formalism we also investigate how linear systems driven with random non-Hermitian matrices (such as random feature models) can exhibit non-monotonic loss curves with training time, while Hermitian matrices with the matching spectra do not, highlighting a different mechanism for non-monotonicity than small eigenvalues causing instability to label noise. Lastly, we provide asymptotic descriptions of the training and test loss dynamics for randomly initialized deep linear neural networks trained in the feature learning regime with high-dimensional random data. In this case, the time translation invariance structure is lost and the hidden layer weights are characterized as spiked random matrices.

[cond-mat updates on arXiv.org] Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system

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

arXiv:2506.05999v2 Announce Type: replace-cross +Abstract: Self-driving labs (SDLs), employing automation and machine learning (ML) to accelerate experimental procedures, have enormous potential in the discovery of new materials. However, in thin film science, SDLs are mainly restricted to solution-based synthetic methods which are easier to automate but cannot access the broad chemical space of inorganic materials. This work presents an SDL based on magnetron co-sputtering. We are using combinatorial frameworks, obtaining accurate composition maps on multi-element, compositionally graded thin films. This normally requires time-consuming ex-situ analysis prone to systematic errors. We present a rapid and calibration-free in-situ, ML driven approach to produce composition maps for arbitrary source combinations and sputtering conditions. We develop a method to predict the composition distribution in a multi-element combinatorial thin film, using in-situ measurements from quartz-crystal microbalance sensors placed in a sputter chamber. For a given source, the sensor readings are learned as a function of the sputtering pressure and magnetron power, through active learning using Gaussian processes (GPs). The final GPs are combined with a geometric model of the deposition flux distribution in the chamber, which allows interpolation of the deposition rates from each source, at any position across the sample. We investigate several acquisition functions for the ML procedure. A fully Bayesian GP - BALM (Bayesian active learning MacKay) - achieved the best performance, learning the deposition rates for a single source in 10 experiments. Prediction accuracy for co-sputtering composition distributions was verified experimentally. Our framework dramatically increases throughput by avoiding the need for extensive characterisation or calibration, thus demonstrating the potential of ML-guided SDLs to accelerate materials exploration.

[ChemRxiv] ACCEL: Automated Closed-loop Co-Optimization and Experimentation Learning Enables Phase-Pure Identification in Formamidinium-based Dion–Jacobson Halide Perovskites

ChemRxiv — Mon, 12 Jan 2026 00:00:00 GMT

Self-driving laboratories (SDLs) are poised to transform materials discovery by integrating automation with machine learning (ML) to accelerate data-driven experimentation. However, most SDL frameworks remain limited by single-feedback optimization and lack the multi-modal diagnostics needed to resolve both optical and structural evolution in complex hybrid systems. Here, we introduce ACCEL, an automated, ML-guided closed-loop platform for identifying and synthesizing target pure phases within quasi-2D halide perovskite systems. Using a ternary 3D:2D compositional space comprising 3D FAPbI3 and two Dion–Jacobson (DJ) spacer–based components, ACCEL optimizes the 3D:2D ratios to identify compositions that converge toward a structurally and optically stable α-FAPbI3-like phase within a quasi-2D design space. Automated in-situ photoluminescence (PL) is used to monitor crystallization kinetics in real time, revealing how the relative fraction of co-spacers governs nucleation rate and phase evolution during film formation. These kinetic insights are integrated with high-throughput synthesis, automated PL analysis, and X-ray diffraction (XRD) similarity metrics within a Gaussian Process–Bayesian Optimization framework to suppress phase heterogeneity and stabilize the target phase. Rather than targeting a predefined DJ layer number, ACCEL enables optimization based on user-defined optical and structural signatures, providing a generalizable route for autonomous phase discovery in complex hybrid materials.

[ScienceDirect Publication: Joule] A critical outlook for large-scale all-solid-state batteries

ScienceDirect Publication: Joule — Sun, 11 Jan 2026 01:50:45 GMT

Publication date: Available online 9 January 2026

Source: Joule

Author(s): Seongjae Ko, Makoto Ue, Atsuo Yamada

[Wiley: Advanced Functional Materials: Table of Contents] Recycling of Thermoplastics with Machine Learning: A Review

Wiley: Advanced Functional Materials: Table of Contents — Sat, 10 Jan 2026 15:14:36 GMT

Advanced Functional Materials, Volume 36, Issue 3, 8 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Electron Compensation Enhanced Triboelectric Sensor Assisted by Machine Learning for Tactile Perception Recognition

Wiley: Advanced Functional Materials: Table of Contents — Sat, 10 Jan 2026 15:14:36 GMT

Advanced Functional Materials, Volume 36, Issue 3, 8 January 2026.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Selective Ion Transport Regulation Enables High Current Density CO2‐to‐C2+ Conversion in Acid

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:07:04 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Triply Responsive Control of Ion Transport with an Artificial Channel Creates a Switchable AND to OR Logic Gate

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:04:51 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Coupled Engineering of Short‐/Long‐Range Disorder in Oxyhalides Unlocks Benchmark Sodium Superionic Conductor

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:04:51 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Atomistic Landscape of Pt Nanoparticles via Machine Learning: How Size Effect and Hydrogen Adsorption Govern Structural Ensembles and Catalytic Activity

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:04:51 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Empowering Chemistry Experts with Large Language Models for Literature Interpretation in Single‐Atom Catalysis Toward Advanced Oxidation

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:04:51 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Balancing Oxidative Stability and Ion Transport in Quasi‐Solid Polymer Electrolytes via Chlorine‐Driven Halogenation Engineering

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:04:51 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[Wiley: Advanced Energy Materials: Table of Contents] Machine Learning Driven High‐Throughput Screening of Asymmetric Dinuclear Cobalt for Nitrate‐to‐Ammonia Reduction with Near‐100% Selectivity

Wiley: Advanced Energy Materials: Table of Contents — Sat, 10 Jan 2026 14:09:28 GMT

Advanced Energy Materials, EarlyView.

[ChemRxiv] ConforFormer: representation for molecules through understanding of conformers

ChemRxiv — Sat, 10 Jan 2026 00:00:00 GMT

[ChemRxiv] Graph learning of sequence statistics for polymer representation

ChemRxiv — Sat, 10 Jan 2026 00:00:00 GMT

[npj Computational Materials] Machine learning for phase prediction of high entropy carbide ceramics from imbalanced data

npj Computational Materials — Sat, 10 Jan 2026 00:00:00 GMT

npj Computational Materials, Published online: 10 January 2026; doi:10.1038/s41524-025-01873-2

Machine learning for phase prediction of high entropy carbide ceramics from imbalanced data

[Wiley: Small Structures: Table of Contents] Dielectric Constant Guided Solvation Structure Design for Stable Solid Electrolyte Interphase in Lithium Metal Batteries

Wiley: Small Structures: Table of Contents — Fri, 09 Jan 2026 19:05:13 GMT

Small Structures, Volume 7, Issue 1, January 2026.

[ScienceDirect Publication: Journal of Energy Storage] External pressure's influence on lithium-ion transport within solid-state electrolytes

ScienceDirect Publication: Journal of Energy Storage — Fri, 09 Jan 2026 18:31:35 GMT

Publication date: 1 March 2026

Source: Journal of Energy Storage, Volume 149

Author(s): Yuan He, Xiongying Zhang, Dong Lu

[ScienceDirect Publication: Journal of Energy Storage] Alterative aqueous polymer anode binder enabling interfacial stabilization and improved lithium-ion transport

ScienceDirect Publication: Journal of Energy Storage — Fri, 09 Jan 2026 18:31:35 GMT

Publication date: 1 March 2026

Source: Journal of Energy Storage, Volume 149

Author(s): Yuan Peng, Huimin Chen, Xiaowen Qv, Ao Zeng, Jianfeng Xia, Jiangtao Xu, Kunkun Guo

[ScienceDirect Publication: Journal of Energy Storage] Interface engineering of Li_1.5Al_0.5Ge_1.5(PO₄)₃ electrolytes via in-situ polymer–cerium hybrid interlayers for high-performance all-solid-state lithium metal batteries

ScienceDirect Publication: Journal of Energy Storage — Fri, 09 Jan 2026 18:31:35 GMT

Publication date: 1 March 2026

Source: Journal of Energy Storage, Volume 149

Author(s): Kaiqi Wu, Chengjin Peng, Fanglin Wu, Liyuan Huang, Liang Lan, Liqiang Kang, Yecheng Liu, Xin Ao, Shan Fang

[ScienceDirect Publication: Computational Materials Science] A general LLM-powered text mining framework: Applied to extract high entropy alloys

ScienceDirect Publication: Computational Materials Science — Fri, 09 Jan 2026 18:31:33 GMT

Publication date: October 2026

Source: Computational Materials Science, Volume 264

Author(s): Haolun Yuan, Jun Zeng, Jie Zuo, Xin Wang, Dingguo Xu

[ScienceDirect Publication: Materials Today] Heteropolyanion regulation activating decoupled ion transition for Na superionic conductors

ScienceDirect Publication: Materials Today — Fri, 09 Jan 2026 18:31:29 GMT

Publication date: Available online 9 January 2026

Source: Materials Today

Author(s): Tian Jiang, Qi Fan, Wenshan Gou, Anyang Yu, Changhao Zhu, Ruirui Zhang, Youwei Dong, Shijun Yuan, Qingyu Xu

[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Correlating (Chemo-)Mechanical Coupling in TiS2 during Li+ Intercalation across Liquid and Solid Electrolytes Via Operando Analysis

ACS Energy Letters: Latest Articles (ACS Publications) — Fri, 09 Jan 2026 16:22:26 GMT

ACS Energy Letters

DOI: 10.1021/acsenergylett.5c02943

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Harnessing Entropic Effects from Interlayer Coupling to Modulate Ion Transport and Rectification in Multilayered Janus Graphene Nanopores

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Fri, 09 Jan 2026 12:51:38 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c17242

[Wiley: Advanced Science: Table of Contents] Uncertainty‐Quantified Primary Particle Size Prediction in Li‐Rich NCM Materials via Machine Learning and Chemistry‐Aware Imputation

Wiley: Advanced Science: Table of Contents — Fri, 09 Jan 2026 11:35:15 GMT

Advanced Science, Volume 13, Issue 2, 9 January 2026.

[Wiley: Advanced Science: Table of Contents] Highly Selective CO2 Reduction to Pure Formic Acid Using a Nafion‐TiO2 Composite Porous Solid Electrolyte

Wiley: Advanced Science: Table of Contents — Fri, 09 Jan 2026 11:35:15 GMT

Advanced Science, Volume 13, Issue 2, 9 January 2026.

[Wiley: Advanced Science: Table of Contents] Macroscopically Ordered Piezo‐Potential in All‐Polymetric Solid Electrolytes Responding to Li Anode Volume Changes for Dendrites Suppression

Wiley: Advanced Science: Table of Contents — Fri, 09 Jan 2026 11:35:15 GMT

Advanced Science, Volume 13, Issue 2, 9 January 2026.

[Recent Articles in Phys. Rev. B] Reinforcement fine-tuning for materials design

Recent Articles in Phys. Rev. B — Fri, 09 Jan 2026 10:00:00 GMT

Author(s): Zhendong Cao and Lei Wang

[Phys. Rev. B 113, 024106] Published Fri Jan 09, 2026

[cond-mat updates on arXiv.org] Fluctuation conductivity in ultraclean multicomponent superconductors

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2601.04308v1 Announce Type: new Abstract: We consider the intrinsic fluctuation conductivity in metals with multiply sheeted Fermi surfaces approaching a superconducting critical point. Restricting our attention to extreme type-II multicomponent superconductors motivates focusing on the ultraclean limit. Using functional-integral techniques, we derive the Gaussian fluctuation action from which we obtain the gauge-invariant electromagnetic linear response kernel. This allows us to compute the optical conductivity tensor. We identify essential conditions required for a nonzero longitudinal conductivity at finite frequencies in a disorder-free and translationally invariant system. Specifically, this is neither related to impurity scattering nor electron-phonon interaction, but derives indirectly from the multicomponent character of the incipient superconducting order and the parent metallic state. Under these conditions, the enhancement of the DC conductivity due to fluctuations close to the critical point follows the same critical behaviour as in the diffusive limit.

[cond-mat updates on arXiv.org] Towards understanding the defect properties in the multivalent A-site Na$_{0.5}$Bi$_{0.5}$TiO$_3$-based perovskite ceramics

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2601.04725v1 Announce Type: new Abstract: A defect model involving cation and anion vacancies and anti-site defects is proposed that accounts for the non-stoichiometry of multi-valent $A$-site Na$_{0.5}$Bi$_{0.5}$TiO$_3$ based perovskite oxides with $ABO_3$ composition. A series of samples with varying $A$-site non-stoichiometry and $A$:$B$ ratios were prepared to investigate their electrical conductivity. The oxygen partial pressure and temperature dependent conductivities where studied with direct current (dc) and alternating current (ac) techniques, enabling to separate between ionic and electronic conduction. The Na-excess samples, regardless of the $A$:$B$ ratio, exhibit dominant ionic conductivity and $p$-type electronic conduction, with the highest total conductivity reaching $4 \times 10^{-4}$ S/cm at 450$^\circ$C. In contrast, the Bi-excess samples display more insulating characteristics and $n$-type electronic conductivity, with conductivity values within the 10$^{-8}$ S/cm range at 450$^\circ$C. These conductivity results strongly support the proposed defect model, which offers a straightforward description of defect chemistry in NBT-based ceramics and serves as a valuable guide for optimizing sample processing to achieve tailored properties.

[cond-mat updates on arXiv.org] Scalable Dielectric Tensor Predictions for Inorganic Materials using Equivariant Graph Neural Networks

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2601.04755v1 Announce Type: new Abstract: Accurate prediction of dielectric tensors is essential for accelerating the discovery of next-generation inorganic dielectric materials. Existing machine learning approaches, such as equivariant graph neural networks, typically rely on specially-designed network architectures to enforce O(3) equivariance. However, to preserve equivariance, these specially-designed models restrict the update of equivariant features during message passing to linear transformations or gated equivariant nonlinearities. The inability to implicitly characterize more complex nonlinear structures may reduce the predictive accuracy of the model. In this study, we introduce a frame-averaging-based approach to achieve equivariant dielectric tensor prediction. We propose GoeCTP, an O(3)-equivariant framework that predicts dielectric tensors without imposing any structural restrictions on the backbone network. We benchmark its performance against several state-of-the-art models and further employ it for large-scale virtual screening of thermodynamically stable materials from the Materials Project database. GoeCTP successfully identifies various promising candidates, such as Zr(InBr$_3$)$_2$ (band gap $E_g = 2.41$ eV, dielectric constant $\overline{\varepsilon} = 194.72$) and SeI$_2$ (anisotropy ratio $\alpha_r = 96.763$), demonstrating its accuracy and efficiency in accelerating the discovery of advanced inorganic dielectric materials.

My Customized Papers

[ScienceDirect Publication: Joule] A critical outlook for large-scale all-solid-state batteries

[Wiley: Advanced Functional Materials: Table of Contents] Recycling of Thermoplastics with Machine Learning: A Review

[Wiley: Advanced Functional Materials: Table of Contents] Electron Compensation Enhanced Triboelectric Sensor Assisted by Machine Learning for Tactile Perception Recognition

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[Wiley: Angewandte Chemie International Edition: Table of Contents] Triply Responsive Control of Ion Transport with an Artificial Channel Creates a Switchable AND to OR Logic Gate

[Wiley: Angewandte Chemie International Edition: Table of Contents] Coupled Engineering of Short‐/Long‐Range Disorder in Oxyhalides Unlocks Benchmark Sodium Superionic Conductor

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[Wiley: Advanced Energy Materials: Table of Contents] Machine Learning Driven High‐Throughput Screening of Asymmetric Dinuclear Cobalt for Nitrate‐to‐Ammonia Reduction with Near‐100% Selectivity

[ChemRxiv] ConforFormer: representation for molecules through understanding of conformers

[ChemRxiv] Graph learning of sequence statistics for polymer representation

[npj Computational Materials] Machine learning for phase prediction of high entropy carbide ceramics from imbalanced data

[Wiley: Small Structures: Table of Contents] Dielectric Constant Guided Solvation Structure Design for Stable Solid Electrolyte Interphase in Lithium Metal Batteries

[ScienceDirect Publication: Journal of Energy Storage] External pressure's influence on lithium-ion transport within solid-state electrolytes

[ScienceDirect Publication: Journal of Energy Storage] Alterative aqueous polymer anode binder enabling interfacial stabilization and improved lithium-ion transport

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[ScienceDirect Publication: Computational Materials Science] A general LLM-powered text mining framework: Applied to extract high entropy alloys

[ScienceDirect Publication: Materials Today] Heteropolyanion regulation activating decoupled ion transition for Na superionic conductors

[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Correlating (Chemo-)Mechanical Coupling in TiS2 during Li+ Intercalation across Liquid and Solid Electrolytes Via Operando Analysis

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Harnessing Entropic Effects from Interlayer Coupling to Modulate Ion Transport and Rectification in Multilayered Janus Graphene Nanopores

[Wiley: Advanced Science: Table of Contents] Uncertainty‐Quantified Primary Particle Size Prediction in Li‐Rich NCM Materials via Machine Learning and Chemistry‐Aware Imputation

[Wiley: Advanced Science: Table of Contents] Highly Selective CO2 Reduction to Pure Formic Acid Using a Nafion‐TiO2 Composite Porous Solid Electrolyte

[Wiley: Advanced Science: Table of Contents] Macroscopically Ordered Piezo‐Potential in All‐Polymetric Solid Electrolytes Responding to Li Anode Volume Changes for Dendrites Suppression

[Recent Articles in Phys. Rev. B] Reinforcement fine-tuning for materials design

[cond-mat updates on arXiv.org] Fluctuation conductivity in ultraclean multicomponent superconductors

[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

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

[cond-mat updates on arXiv.org] Autonomous Sampling and SHAP Interpretation of Deposition-Rates in Bipolar HiPIMS

[cond-mat updates on arXiv.org] VacHopPy: A Python package for vacancy hopping analysis based on molecular dynamics simulations

[cond-mat updates on arXiv.org] Machine Learning Framework for Characterizing Processing-Structure Relationship in Block Copolymer Thin Films

[cond-mat updates on arXiv.org] Efficient Band Structure Unfolding with Atom-centered Orbitals: General Theory and Application

[cond-mat updates on arXiv.org] Disordered Dynamics in High Dimensions: Connections to Random Matrices and Machine Learning

[cond-mat updates on arXiv.org] Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system

[ChemRxiv] ACCEL: Automated Closed-loop Co-Optimization and Experimentation Learning Enables Phase-Pure Identification in Formamidinium-based Dion–Jacobson Halide Perovskites

[ScienceDirect Publication: Joule] A critical outlook for large-scale all-solid-state batteries

[Wiley: Advanced Functional Materials: Table of Contents] Recycling of Thermoplastics with Machine Learning: A Review

[Wiley: Advanced Functional Materials: Table of Contents] Electron Compensation Enhanced Triboelectric Sensor Assisted by Machine Learning for Tactile Perception Recognition

[Wiley: Angewandte Chemie International Edition: Table of Contents] Selective Ion Transport Regulation Enables High Current Density CO2‐to‐C2+ Conversion in Acid

[Wiley: Angewandte Chemie International Edition: Table of Contents] Triply Responsive Control of Ion Transport with an Artificial Channel Creates a Switchable AND to OR Logic Gate

[Wiley: Angewandte Chemie International Edition: Table of Contents] Coupled Engineering of Short‐/Long‐Range Disorder in Oxyhalides Unlocks Benchmark Sodium Superionic Conductor

[Wiley: Angewandte Chemie International Edition: Table of Contents] Atomistic Landscape of Pt Nanoparticles via Machine Learning: How Size Effect and Hydrogen Adsorption Govern Structural Ensembles and Catalytic Activity

[Wiley: Angewandte Chemie International Edition: Table of Contents] Empowering Chemistry Experts with Large Language Models for Literature Interpretation in Single‐Atom Catalysis Toward Advanced Oxidation

[Wiley: Angewandte Chemie International Edition: Table of Contents] Balancing Oxidative Stability and Ion Transport in Quasi‐Solid Polymer Electrolytes via Chlorine‐Driven Halogenation Engineering

[Wiley: Advanced Energy Materials: Table of Contents] Machine Learning Driven High‐Throughput Screening of Asymmetric Dinuclear Cobalt for Nitrate‐to‐Ammonia Reduction with Near‐100% Selectivity

[ChemRxiv] ConforFormer: representation for molecules through understanding of conformers

[ChemRxiv] Graph learning of sequence statistics for polymer representation

[npj Computational Materials] Machine learning for phase prediction of high entropy carbide ceramics from imbalanced data

[Wiley: Small Structures: Table of Contents] Dielectric Constant Guided Solvation Structure Design for Stable Solid Electrolyte Interphase in Lithium Metal Batteries

[ScienceDirect Publication: Journal of Energy Storage] External pressure's influence on lithium-ion transport within solid-state electrolytes

[ScienceDirect Publication: Journal of Energy Storage] Alterative aqueous polymer anode binder enabling interfacial stabilization and improved lithium-ion transport

[ScienceDirect Publication: Journal of Energy Storage] Interface engineering of Li1.5Al0.5Ge1.5(PO4)3 electrolytes via in-situ polymer–cerium hybrid interlayers for high-performance all-solid-state lithium metal batteries

[ScienceDirect Publication: Computational Materials Science] A general LLM-powered text mining framework: Applied to extract high entropy alloys

[ScienceDirect Publication: Materials Today] Heteropolyanion regulation activating decoupled ion transition for Na superionic conductors

[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Correlating (Chemo-)Mechanical Coupling in TiS2 during Li+ Intercalation across Liquid and Solid Electrolytes Via Operando Analysis

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Harnessing Entropic Effects from Interlayer Coupling to Modulate Ion Transport and Rectification in Multilayered Janus Graphene Nanopores

[Wiley: Advanced Science: Table of Contents] Uncertainty‐Quantified Primary Particle Size Prediction in Li‐Rich NCM Materials via Machine Learning and Chemistry‐Aware Imputation

[Wiley: Advanced Science: Table of Contents] Highly Selective CO2 Reduction to Pure Formic Acid Using a Nafion‐TiO2 Composite Porous Solid Electrolyte

[Wiley: Advanced Science: Table of Contents] Macroscopically Ordered Piezo‐Potential in All‐Polymetric Solid Electrolytes Responding to Li Anode Volume Changes for Dendrites Suppression

[Recent Articles in Phys. Rev. B] Reinforcement fine-tuning for materials design

[cond-mat updates on arXiv.org] Fluctuation conductivity in ultraclean multicomponent superconductors

[cond-mat updates on arXiv.org] Towards understanding the defect properties in the multivalent A-site Na$_{0.5}$Bi$_{0.5}$TiO$_3$-based perovskite ceramics

[cond-mat updates on arXiv.org] Scalable Dielectric Tensor Predictions for Inorganic Materials using Equivariant Graph Neural Networks

[ScienceDirect Publication: Journal of Energy Storage] Interface engineering of Li_1.5Al_0.5Ge_1.5(PO₄)₃ electrolytes via in-situ polymer–cerium hybrid interlayers for high-performance all-solid-state lithium metal batteries

[ScienceDirect Publication: Journal of Energy Storage] Interface engineering of Li_1.5Al_0.5Ge_1.5(PO₄)₃ electrolytes via in-situ polymer–cerium hybrid interlayers for high-performance all-solid-state lithium metal batteries