Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials

Advanced Energy Materials, Год журнала: 2024, Номер unknown

Опубликована: Дек. 10, 2024

Abstract This review highlights recent advances in machine learning (ML)‐assisted design of energy materials. Initially, ML algorithms were successfully applied to screen materials databases by establishing complex relationships between atomic structures and their resulting properties, thus accelerating the identification candidates with desirable properties. Recently, development highly accurate interatomic potentials generative models has not only improved robust prediction physical but also significantly accelerated discovery In past couple years, methods have enabled high‐precision first‐principles predictions electronic optical properties for large systems, providing unprecedented opportunities science. Furthermore, ML‐assisted microstructure reconstruction physics‐informed solutions partial differential equations facilitated understanding microstructure–property relationships. Most recently, seamless integration various platforms led emergence autonomous laboratories that combine quantum mechanical calculations, language models, experimental validations, fundamentally transforming traditional approach novel synthesis. While highlighting aforementioned advances, existing challenges are discussed. Ultimately, is expected fully integrate atomic‐scale simulations, reverse engineering, process optimization, device fabrication, empowering system design. will drive transformative innovations conversion, storage, harvesting technologies.

Язык: Английский

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Applications of machine learning in surfaces and interfaces

Chemical Physics Reviews, Год журнала: 2025, Номер 6(1)

Опубликована: Март 1, 2025

Surfaces and interfaces play key roles in chemical material science. Understanding physical processes at complex surfaces is a challenging task. Machine learning provides powerful tool to help analyze accelerate simulations. This comprehensive review affords an overview of the applications machine study systems materials. We categorize into following broad categories: solid–solid interface, solid–liquid liquid–liquid surface solid, liquid, three-phase interfaces. High-throughput screening, combined first-principles calculations, force field accelerated molecular dynamics simulations are used rational design such as all-solid-state batteries, solar cells, heterogeneous catalysis. detailed information on for

Язык: Английский

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Tetrahedral Tilting and Lithium‐Ion Transport in Halide Argyrodites Prepared by Rapid, Microwave‐Assisted Synthesis

Advanced Functional Materials, Год журнала: 2025, Номер unknown

Опубликована: Фев. 12, 2025

Abstract This study demonstrates a rapid, dry, microwave‐assisted (MW) synthesis method that enables preparation of halide argyrodites ( , ) in less than 20 min. The structures and ion transport properties the resulting materials are compared with those synthesized by conventional solid‐state methods. leads to increased site disorder elevated Arrhenius prefactors (), which lead an order magnitude improvement 30 ionic conductivity MW‐. X‐ray pair distribution function analysis (XPDF) reveals significant rotational units, is impacted method, choice halide, presence / disorder. These displacements strongly correlated transport, specifically entropy migration (). Overall, this rapid route for preparing high‐quality argyrodite electrolytes min, further unravels atomistic insights into interplay structural disorder, dynamics, mechanisms.

Язык: Английский

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Artificial Intelligence in Rechargeable Battery: Advancements and Prospects

Energy storage materials, Год журнала: 2024, Номер unknown, С. 103860 - 103860

Опубликована: Окт. 1, 2024

Язык: Английский

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Lithium Localization by Anions in Argyrodite Solid Electrolytes from Machine‐Learning‐based Simulations

Advanced Energy Materials, Год журнала: 2024, Номер unknown

Опубликована: Окт. 11, 2024

Abstract The introduction of density functional theory (DFT) has improved the study material properties. This enabled significant breakthroughs in solid electrolytes, which have emerged as promising candidates for next‐generation energy storage systems. However, DFT faces limitations due to extremely high computational costs required large atomic cells and long simulation times. In current study, AI‐based simulations using neural network potentials (NNPs) are introduced extend capabilities explore effect anions on lithium diffusion Li argyrodite (Li 6 PS 5 X, X = Cl Br). investigation categorizes frameworks into two distinct cages, demonstrating that sulfur ions these cage centers bind surrounding ions. From results, a strategy is proposed enhance ion conductivity by minimizing occupation centers. research provides benchmark evaluating ionic based anion configuration advances understanding transport argyrodite, informing potential improvements energy‐storage technologies.

Язык: Английский

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Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials

Physical Review Materials, Год журнала: 2024, Номер 8(11)

Опубликована: Ноя. 21, 2024

Язык: Английский

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