Machine learning prediction of materials properties from chemical composition: Status and prospects DOI Open Access
Mohammed Alghadeer, Nyimas Aisyah, Mahmoud Hezam

et al.

Chemical Physics Reviews, Journal Year: 2024, Volume and Issue: 5(4)

Published: Dec. 1, 2024

In materials science, machine learning (ML) has become an essential and indispensable tool. ML emerged as a powerful tool in particularly for predicting material properties based on chemical composition. This review provides comprehensive overview of the current status future prospects using this domain, with special focus physics-guided (PGML). By integrating physical principles into models, PGML ensures that predictions are not only accurate but also interpretable, addressing critical need sciences. We discuss foundational concepts statistical PGML, outline general framework informatics, explore key aspects such data analysis, feature reduction, composition representation. Additionally, we survey latest advancements prediction geometric structures, electronic properties, other characteristics from formulas. The resource tables listing databases, tools, predictors, offering valuable reference researchers. As field rapidly expands, aims to guide efforts harnessing discovery development.

Language: Английский

Elemental Augmentation of Machine Learning Interatomic Potentials DOI Creative Commons
Haibo Xue, Guanjian Cheng, Wan‐Jian Yin

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown, P. 100026 - 100026

Published: Feb. 1, 2025

Language: Английский

Citations

0
Recent Advances in the Large‐Scale Production of Photo/Electrocatalysts for Energy Conversion and beyond DOI
Jinhao Li,

Zixian Li,

Qiuhong Sun

et al.

Advanced Energy Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 17, 2024

Abstract Photocatalysis and electrocatalysis have emerged as promising technologies for addressing the energy crisis environmental issues. However, widespread application of these is hampered by challenge scaling up production photo/electrocatalysts that are not only highly active stable but also cost‐effective environmentally benign. This review delves into latest advancements in large‐scale synthesis photo/electrocatalysts. The factors to be considered catalysts discussed first. methods batch preparation then comprehensively introduced, with a thorough discussion their respective advantages limitations. Moreover, data analysis via machine learning techniques, which accelerates identification refinement potential new offers insights enhancing high‐throughput catalysts, introduced detail. Then representative examples presented illustrate applications field industrial‐level photo/electrocatalysis. Finally, challenges prospects development discussed. By bridging gap between laboratory research industrial application, this aims provide reference future sustainable conversion beyond.

Language: Английский

Citations

3
Editorial: special topic on computation-assisted materials screening and design DOI Open Access
Jinlan Wang, Chenghua Sun, Shaohua Dong

et al.

Science China Materials, Journal Year: 2024, Volume and Issue: 67(4), P. 1011 - 1013

Published: March 26, 2024

Language: Английский

Citations

2
Machine learning prediction of materials properties from chemical composition: Status and prospects DOI Open Access
Mohammed Alghadeer, Nyimas Aisyah, Mahmoud Hezam

et al.

Chemical Physics Reviews, Journal Year: 2024, Volume and Issue: 5(4)

Published: Dec. 1, 2024

In materials science, machine learning (ML) has become an essential and indispensable tool. ML emerged as a powerful tool in particularly for predicting material properties based on chemical composition. This review provides comprehensive overview of the current status future prospects using this domain, with special focus physics-guided (PGML). By integrating physical principles into models, PGML ensures that predictions are not only accurate but also interpretable, addressing critical need sciences. We discuss foundational concepts statistical PGML, outline general framework informatics, explore key aspects such data analysis, feature reduction, composition representation. Additionally, we survey latest advancements prediction geometric structures, electronic properties, other characteristics from formulas. The resource tables listing databases, tools, predictors, offering valuable reference researchers. As field rapidly expands, aims to guide efforts harnessing discovery development.

Language: Английский

Citations

1