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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Influence of manganese doping on the efficacy of SrCeO3 perovskite utilized as electrodes in supercapacitors

Journal of the Indian Chemical Society, Год журнала: 2025, Номер 102(3), С. 101582 - 101582

Опубликована: Янв. 18, 2025

Язык: Английский

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Computational understanding and multiscale simulation of secondary batteries

Energy storage materials, Год журнала: 2025, Номер unknown, С. 104009 - 104009

Опубликована: Янв. 1, 2025

Язык: Английский

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Elucidating the role of multi-scale microstructures in Li7La3Zr2O12 based all-solid-state lithium batteries

Energy storage materials, Год журнала: 2024, Номер 72, С. 103752 - 103752

Опубликована: Авг. 30, 2024

Язык: Английский

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Advancement of capacitive deionization propelled by machine learning approach

Separation and Purification Technology, Год журнала: 2024, Номер 354, С. 129423 - 129423

Опубликована: Авг. 30, 2024

Язык: Английский

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Prospects and Challenges of Energy Storage Materials: A Comprehensive Review

Chemical Engineering Journal Advances, Год журнала: 2024, Номер 20, С. 100657 - 100657

Опубликована: Окт. 10, 2024

Язык: Английский

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SOLID-STATE LITHIUM-ION BATTERY ELECTROLYTES: REVOLUTIONIZING ENERGY DENSITY AND SAFETY

Hybrid Advances, Год журнала: 2024, Номер unknown, С. 100339 - 100339

Опубликована: Ноя. 1, 2024

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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Toward unlocking the potential of aqueous Zn-CO2 batteries: What factors affect the electrochemical performance?

Chemical Engineering Journal, Год журнала: 2025, Номер unknown, С. 161736 - 161736

Опубликована: Март 1, 2025

Язык: Английский

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Data-driven discovery of vanadium-based anode materials for lithium-ion batteries

Journal of Energy Storage, Год журнала: 2025, Номер 118, С. 116290 - 116290

Опубликована: Март 20, 2025

Язык: Английский

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