The rational design of catalyst structures tailored to target performance is an ambitious and profoundly impactful goal.Key challenges include achieving refined representations of the three-dimensional structure of ac...The rational design of catalyst structures tailored to target performance is an ambitious and profoundly impactful goal.Key challenges include achieving refined representations of the three-dimensional structure of active sites and imbuing models with robust physical interpretability.Herein,we developed a topology-based variational autoencoder framework(PGH-VAEs)to enable the interpretable inverse design of catalytic active sites.Leveraging high-entropy alloys as a case,we demonstrate that persistent GLMY homology,an advanced topological algebraic analysis tool,enables the quantification of three-dimensional structural sensitivity and establishes correlations with adsorption properties.The multi-channel PGH-VAEs illustrate how coordination and ligand effects shape the latent space and influence the adsorption energies.Building on the inverse design results from PGH-VAEs,the strategies to optimize the composition and facet structures to maximize the proportion of optimal active sites are proposed.This interpretable inverse design framework can be extended to diverse systems,paving the way for AI-driven catalyst design.展开更多
基金supported by the Guangdong Basic and Applied Basic Research Foundation(2020A1515110843)Young S&T Talent Training Program of Guangdong Provincial Association for S&T(SKXRC202211)+3 种基金National Natural Science Foundation of China(22402163,22109003)the Major Science and Technology Infrastructure Project of Material Genome Big-science Facilities Platform supported by Municipal Development and Reform Commission of Shenzhen,Soft Science Research Project of Guangdong Province(No.2017B030301013)Natural Science Foundation of Xiamen,China(3502Z202472001)High-level Scientific Research Foundation of Hebei Province and Fundamental Research Funds for the Central Universities(20720240054).
文摘The rational design of catalyst structures tailored to target performance is an ambitious and profoundly impactful goal.Key challenges include achieving refined representations of the three-dimensional structure of active sites and imbuing models with robust physical interpretability.Herein,we developed a topology-based variational autoencoder framework(PGH-VAEs)to enable the interpretable inverse design of catalytic active sites.Leveraging high-entropy alloys as a case,we demonstrate that persistent GLMY homology,an advanced topological algebraic analysis tool,enables the quantification of three-dimensional structural sensitivity and establishes correlations with adsorption properties.The multi-channel PGH-VAEs illustrate how coordination and ligand effects shape the latent space and influence the adsorption energies.Building on the inverse design results from PGH-VAEs,the strategies to optimize the composition and facet structures to maximize the proportion of optimal active sites are proposed.This interpretable inverse design framework can be extended to diverse systems,paving the way for AI-driven catalyst design.
