Heterogeneous catalysts constitute a crucial component of many industrial processes,and to gain an understanding of the atomicscale features of such catalysts,ab initio density functional theory is widely employed.Rec...Heterogeneous catalysts constitute a crucial component of many industrial processes,and to gain an understanding of the atomicscale features of such catalysts,ab initio density functional theory is widely employed.Recently,growing computational power has permitted the extension of such studies to complex reaction networks involving either high adsorbate coverages or multidentate adsorbates,which bind to the surface through multiple atoms.Describing all possible adsorbate configurations for such systems,however,is often not possible based on chemical intuition alone.To systematically treat such complexities,we present a generalized Python-based graph theory approach to convert atomic scale models into undirected graph representations.These representations,when combined with workflows such as evolutionary algorithms,can systematically generate high coverage adsorbate models and classify unique minimum energy multidentate adsorbate configurations for surfaces of low symmetry,including multi-elemental alloy surfaces,steps,and kinks.展开更多
基金J.G.,S.D.,and T.M.acknowledge the United States Department of Energy through the Office of Science,Office of Basic Energy Sciences(BES),Chemical,Biological,and Geosciences Division,Data Science Initiative,Grant DE-SC0020381.
文摘Heterogeneous catalysts constitute a crucial component of many industrial processes,and to gain an understanding of the atomicscale features of such catalysts,ab initio density functional theory is widely employed.Recently,growing computational power has permitted the extension of such studies to complex reaction networks involving either high adsorbate coverages or multidentate adsorbates,which bind to the surface through multiple atoms.Describing all possible adsorbate configurations for such systems,however,is often not possible based on chemical intuition alone.To systematically treat such complexities,we present a generalized Python-based graph theory approach to convert atomic scale models into undirected graph representations.These representations,when combined with workflows such as evolutionary algorithms,can systematically generate high coverage adsorbate models and classify unique minimum energy multidentate adsorbate configurations for surfaces of low symmetry,including multi-elemental alloy surfaces,steps,and kinks.
