Machine Learning-Aided Identification of Single Atom Alloy Catalysts

Abstract

In metal catalytic design, there is a well-established linear scaling relationship between reaction and adsorption energies. However, owing to the challenges of performing experimental and/or computational experiments, there is a paucity of empirical data regarding these systems. In particular, there is little experimental evidence suggesting how the linear scaling law might be overcome in order to discover catalysts with more desirable properties. In this paper, we employ machine-learning techniques in order to predict reaction and adsorption energies for 300 hypothetical binary compounds. We then apply outlier detection methods to identify which of these predicted compounds do not follow the known scaling law. These outlier compounds, which would not have been identified through traditional design rules, are the most likely to have unexpected and potentially transformative catalytic behavior. Thus, this paper proposes a data-driven screening methodology to identify those metallic compounds (as a function of gaseous environment) which are most likely to have targeted catalytic behavior.

Tracing the reactivity of single atom alloys for ethanol dehydrogenation using ab initio simulations

“…Recently, much emphasis has been placed on building a large dataset of descriptors (e.g., binding energies of key elements) for SAAs, 13 which in-turn provides a comprehensive evaluation of turnover rates using linear scaling. 28,29 Our group has demonstrated the application of an ML method in predicting turnover rates for the NODH reaction of ethanol over the bimetallic alloys of Cu. 11 On applying the same ML method, the binding energies of carbon and oxygen atoms for NiCu, PtCu and PdCu SAAs are estimated.…”

“…This thought resonates well with several studies which have pursued ML approaches for catalytic trends. 13,28,29 However, catalytic turnovers estimated from ML predicted descriptors and scaling relations may not provide an ideal platform, since departure from scaling relations is reported in the reactivity of SAAs. 14,15 Therefore, an attempt is made here to estimate reactivity trends at the reaction temperature from a complete ab initio parameterized MKM with the adsorption and transition state energies derived from density functional theory (DFT) calculations.…”

When More Is Less: Nonmonotonic Trends in Adsorption on Clusters in Alloy Surfaces

“…[9][10][11] Single-atom alloys have received particular attention, including development of models and correlations for predicting or understanding adsorption energies. [12][13][14] Single-atom alloys can have unusual properties, such as allowing spillover and breaking linear correlations between energies. [15][16][17] Further, they often feature localized, sharp electronic states.…”

Machine learning‐based analysis and prediction of the interfacial corrosion processes of copper cathode plates during the electrolytic production of copper powders

“…In addition to numerical methods, the continuous development of artificial intelligence technology in recent years has generated considerable interest in applying machine learning methods for conducting data processing in materials research, [21][22][23][24][25][26][27][28][29][30] the prediction of material performance, [31] and the development of graphical diagnosis systems. [32] Moreover, machine learning has been applied in the analysis of electrochemical corrosion processes for various materials.…”