Single-Atom Alloy Catalysts Designed by First-Principles Calculations and Artificial Intelligence

Abstract

Single-atom metal alloy catalysts (SAACs) have recently become a very active new frontier in catalysis research. The simultaneous optimization of both facile dissociation of reactants and a balanced strength of intermediates' binding make them highly efficient and selective for many industrially important reactions. However, discovery of new SAACs is hindered by the lack of fast yet reliable prediction of the catalytic properties of the sheer number of candidate materials. In this work, we address this problem by applying a compressed-sensing data-analytics approach parameterized with density-functional inputs. Our approach is faster and more accurate than the current state-of-the-art linear relationships. Besides consistently predicting high efficiency of the experimentally studied Pd/Cu, Pt/Cu, Pd/Ag, Pt/Au, Pd/Au, Pt/Ni, Au/Ru, and Ni/Zn SAACs (the first metal is the dispersed component), we identify more than two hundred yet unreported candidates. Some of these new candidates are predicted to exhibit even higher stability and efficiency than the reported ones. Our study demonstrates the importance of breaking linear relationships to avoid bias in catalysis design, as well as provides a recipe for selecting best candidate materials from hundreds of thousands of transition-metal SAACs for various applications.

References in corpus (6)

• SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates
• The steady-state of heterogeneous catalysis, studied by first-principles statistical mechanics
• Beyond scaling relations for the description of catalytic materials
• Ab initio atomistic thermodynamics and statistical mechanics of surface properties and functions
• Uncovering structure-property relationships of materials by subgroup discovery
• Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery

Cited by in corpus (7)

• AI-driven inverse design of materials: Past, present and future
• Hierarchical symbolic regression for identifying key physical parameters correlated with bulk properties of perovskites
• Material Hardness Descriptor Derived by Symbolic Regression
• Oxygen dissociation on the C3N monolayer: A first-principles study
• Three Dimensional Activity Volcano Plot under External Electric Field
• Transition States Energies from Machine Learning: An Application to Reverse Water-Gas Shift on Single-Atom Alloys
• Multiple Adsorption of CO Molecules on Transition Metal Substitutional Impurities in Copper Surfaces