Applications of machine learning in surfaces and interfaces

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

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Machine learning-driven catalyst design, synthesis and performance prediction for CO2 hydrogenation

Journal of Industrial and Engineering Chemistry, Journal Year: 2024, Volume and Issue: 144, P. 32 - 47

Published: Sept. 21, 2024

Language: Английский

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High-throughput and explainable machine learning for lacquer formulations: Enhancing coating development by interpretable models

Progress in Organic Coatings, Journal Year: 2025, Volume and Issue: 205, P. 109265 - 109265

Published: April 14, 2025

Language: Английский

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Machine learning-based techno-econo-environmental analysis of CO2-to-olefins process for screening the optimal catalyst and hydrogen color

Energy, Journal Year: 2024, Volume and Issue: unknown, P. 133508 - 133508

Published: Oct. 1, 2024

Language: Английский

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