The Future of Catalysis: Applying Graph Neural Networks for Intelligent Catalyst Design DOI

Zhihao Wang,

Wentao Li, Siying Wang

et al.

Wiley Interdisciplinary Reviews Computational Molecular Science, Journal Year: 2025, Volume and Issue: 15(2)

Published: March 1, 2025

ABSTRACT With the increasing global demand for energy transition and environmental sustainability, catalysts play a vital role in mitigating climate change, as they facilitate over 90% of chemical material conversions. It is important to investigate complex structures properties enhanced performance, which artificial intelligence (AI) methods, especially graph neural networks (GNNs) could be useful. In this article, we explore cutting‐edge applications future potential GNNs intelligent catalyst design. The fundamental theories their practical catalytic simulation inverse design are first reviewed. We analyze critical roles accelerating screening, performance prediction, reaction pathway analysis, mechanism modeling. By leveraging convolution techniques accurately represent molecular structures, integrating symmetry constraints ensure physical consistency, applying generative models efficiently space, these approaches work synergistically enhance efficiency accuracy Furthermore, highlight high‐quality databases crucial catalysis research innovative application thermocatalysis, electrocatalysis, photocatalysis, biocatalysis. end, key directions advancing catalysis: dynamic frameworks real‐time conditions, hierarchical linking atomic details features, multi‐task interpretability mechanisms reveal pathways. believe advancements will significantly broaden science, paving way more efficient, accurate, sustainable methodologies.

Language: Английский

The Future of Catalysis: Applying Graph Neural Networks for Intelligent Catalyst Design DOI

Zhihao Wang,

Wentao Li, Siying Wang

et al.

Wiley Interdisciplinary Reviews Computational Molecular Science, Journal Year: 2025, Volume and Issue: 15(2)

Published: March 1, 2025

ABSTRACT With the increasing global demand for energy transition and environmental sustainability, catalysts play a vital role in mitigating climate change, as they facilitate over 90% of chemical material conversions. It is important to investigate complex structures properties enhanced performance, which artificial intelligence (AI) methods, especially graph neural networks (GNNs) could be useful. In this article, we explore cutting‐edge applications future potential GNNs intelligent catalyst design. The fundamental theories their practical catalytic simulation inverse design are first reviewed. We analyze critical roles accelerating screening, performance prediction, reaction pathway analysis, mechanism modeling. By leveraging convolution techniques accurately represent molecular structures, integrating symmetry constraints ensure physical consistency, applying generative models efficiently space, these approaches work synergistically enhance efficiency accuracy Furthermore, highlight high‐quality databases crucial catalysis research innovative application thermocatalysis, electrocatalysis, photocatalysis, biocatalysis. end, key directions advancing catalysis: dynamic frameworks real‐time conditions, hierarchical linking atomic details features, multi‐task interpretability mechanisms reveal pathways. believe advancements will significantly broaden science, paving way more efficient, accurate, sustainable methodologies.

Language: Английский

Citations

0