Transforming CO_(2)into methanol represents a crucial step towards closing the carbon cycle,with thermoreduction technology nearing industrial application.However,obtaining high methanol yields and ensuring the stabil...Transforming CO_(2)into methanol represents a crucial step towards closing the carbon cycle,with thermoreduction technology nearing industrial application.However,obtaining high methanol yields and ensuring the stability of heterocatalysts remain significant challenges.Herein,we present a sophisticated computational framework to accelerate the discovery of thermal heterogeneous catalysts,using machine-learned force fields.We propose a new catalytic descriptor,termed adsorption energy distribution,that aggregates the binding energies for different catalyst facets,binding sites,and adsorbates.The descriptor is versatile and can be adjusted to a specific reaction through careful choice of the key-step reactants and reaction intermediates.By applying unsupervised machine learning and statistical analysis to a dataset comprising nearly 160 metallic alloys,we offer a powerful tool for catalyst discovery.We propose new promising candidates such as ZnRh and ZnPt_(3),which to our knowledge,have not yet been tested,and discuss their possible advantage in terms of stability.展开更多
基金funding from the European Union – NextGenerationEU instrument and the Research Council of Finland's AICon project (grant number no. 348179). The authors gratefully acknowledge CSC – IT Center for Science, Finland, and the Aalto Science-IT project for generous computational resources.
文摘Transforming CO_(2)into methanol represents a crucial step towards closing the carbon cycle,with thermoreduction technology nearing industrial application.However,obtaining high methanol yields and ensuring the stability of heterocatalysts remain significant challenges.Herein,we present a sophisticated computational framework to accelerate the discovery of thermal heterogeneous catalysts,using machine-learned force fields.We propose a new catalytic descriptor,termed adsorption energy distribution,that aggregates the binding energies for different catalyst facets,binding sites,and adsorbates.The descriptor is versatile and can be adjusted to a specific reaction through careful choice of the key-step reactants and reaction intermediates.By applying unsupervised machine learning and statistical analysis to a dataset comprising nearly 160 metallic alloys,we offer a powerful tool for catalyst discovery.We propose new promising candidates such as ZnRh and ZnPt_(3),which to our knowledge,have not yet been tested,and discuss their possible advantage in terms of stability.
