Catalysis in the digital age: Unlocking the power of data with machine learning

Wiley Interdisciplinary Reviews Computational Molecular Science, Год журнала: 2024, Номер 14(5)

Опубликована: Сен. 1, 2024

Abstract The design and discovery of new improved catalysts are driving forces for accelerating scientific technological innovations in the fields energy conversion, environmental remediation, chemical industry. Recently, use machine learning (ML) combination with experimental and/or theoretical data has emerged as a powerful tool identifying optimal various applications. This review focuses on how ML algorithms can be used computational catalysis materials science to gain deeper understanding relationships between properties their stability, activity, selectivity. development repositories, mining techniques, tools that navigate structural optimization problems highlighted, leading highly efficient sustainable future. Several data‐driven models commonly research diverse applications reaction prediction discussed. key challenges limitations using presented, which arise from catalyst's intrinsic complex nature. Finally, we conclude by summarizing potential future directions area ML‐guided catalyst development. article is categorized under: Structure Mechanism > Reaction Mechanisms Catalysis Data Science Artificial Intelligence/Machine Learning Electronic Theory Density Functional

Язык: Английский

Application of graph neural network in computational heterogeneous catalysis

The Journal of Chemical Physics, Год журнала: 2024, Номер 161(17)

Опубликована: Ноя. 1, 2024

Heterogeneous catalysis, as a key technology in modern chemical industries, plays vital role social progress and economic development. However, its complex reaction process poses challenges to theoretical research. Graph neural networks (GNNs) are gradually becoming tool this field they can intrinsically learn atomic representation consider connection relationship, making them naturally applicable molecular systems. This article introduces the basic principles, current network architectures, datasets of GNNs reviews application GNN heterogeneous catalysis from accelerating materials screening exploring potential energy surface. In end, we summarize main prospects future research endeavors.

Язык: Английский

Revealing critical features determining the hydrogen adsorption behaviors in ferric oxide films via machine learning

Applied Surface Science, Год журнала: 2025, Номер unknown, С. 163408 - 163408

Опубликована: Май 1, 2025

Язык: Английский