Applications of machine learning in surfaces and interfaces DOI Open Access
Shaofeng Xu, Jing‐Yuan Wu, Ying Guo

et al.

Chemical Physics Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: March 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

Language: Английский

Recent Applications of Theoretical Calculations and Artificial Intelligence in Solid-State Electrolyte Research: A Review DOI Creative Commons
Ming-Wei Wu, Zheng Wei, Yan Zhao

et al.

Nanomaterials, Journal Year: 2025, Volume and Issue: 15(3), P. 225 - 225

Published: Jan. 30, 2025

Solid-state electrolytes (SSEs), as key materials for all-solid-state batteries (ASSBs), face challenges such low ionic conductivity and poor interfacial stability. With the rapid advancement of computational science artificial intelligence (AI) technologies, theoretical calculations AI methods are emerging efficient important virtual tools predicting screening high-performance SSEs. To further promote development SSEs, this review outlines recent applications in field. First, current calculation methods, density functional theory (DFT) molecular dynamics (MD), material structure optimization, electronic property analysis, transport introduced, along with an analysis their limitations. Second, innovative including machine learning (ML) deep (DL), properties, analyzing structural features, simulating behaviors elaborated. Subsequently, synergistic application strategies combining high-throughput (HTS), calculations, highlighted, demonstrating unique advantages integrating multiple methodologies discovery performance optimization. Finally, research progress is summarized, future trends forecasted. The integration expected to significantly accelerate SSE materials, thereby driving industrial ASSBs.

Language: Английский

Citations

1
Advances in the application of first principles calculations to phosphate-based NASICON battery materials DOI

Zhongyi Cui,

Shilong Sun,

Gang Ning

et al.

Journal of Materials Chemistry A, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

NASICON potential unlocked: first-principles calculations guide doping for sodium ion battery advancement.

Language: Английский

Citations

4
Uncovering the role of atmosphere on thermal stability of NASICON type solid electrolytes and oxide-based cathode materials via high temperature X-ray diffraction DOI Creative Commons
Wen Zhu, Andrea Paolella,

Sylvio Savoie

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown, P. 100045 - 100045

Published: Feb. 1, 2025

Language: Английский

Citations

0
Inorganic Solid‐State Electrolytes in Potassium Batteries: Advances, Challenges, and Future Prospects DOI Creative Commons
Titus Masese,

Godwill Mbiti Kanyolo

ChemElectroChem, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 14, 2025

Abstract Potassium‐ion batteries (KIBs) are increasingly attractive owing to their high voltage and projected low cost. However, the advancement of KIBs has been constrained by challenges related electrolyte stability interface compatibility. Traditional liquid electrolytes pose significant risks, including leakage flammability, prompting a shift towards solid‐state electrolytes, which offer improved energy density, safety thermal stability. This Perspective explores current state inorganic entailing oxides, chalcogenides, halides hydrides. We delve into recent advancements, identifying key future research opportunities, with aim advancing development high‐performance all‐solid‐state potassium batteries.

Language: Английский

Citations

0
Applications of machine learning in surfaces and interfaces DOI Open Access
Shaofeng Xu, Jing‐Yuan Wu, Ying Guo

et al.

Chemical Physics Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: March 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

Language: Английский

Citations

0