Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence

TL;DR

This paper demonstrates how artificial intelligence, specifically symbolic regression with SISSO, can identify key material properties from clean experimental data to understand and accelerate heterogeneous catalyst design.

Contribution

It introduces a tailored AI approach using SISSO to model catalysis and identify materials genes from limited experimental data, revealing underlying physicochemical processes.

Findings

Identified correlations between material properties and reactivity.

Highlighted key physicochemical processes influencing catalysis.

Accelerated catalyst design through AI-driven analysis.

Abstract

Heterogeneous catalysis is an example of a complex materials function, governed by an intricate interplay of several processes, e.g., the different surface chemical reactions, and the dynamic re-structuring of the catalyst material at reaction conditions. Modelling the full catalytic progression via first-principles statistical mechanics is impractical, if not impossible. Instead, we show here how a tailored artificial-intelligence approach can be applied, even to a small number of materials, to model catalysis and determine the key descriptive parameters ("materials genes") reflecting the processes that trigger, facilitate, or hinder catalyst performance. We start from a consistent experimental set of "clean data", containing nine vanadium-based oxidation catalysts. These materials were synthesized, fully characterized, and tested according to standardized protocols. By applying the symbolic-regression SISSO approach, we identify correlations between the few most relevant materials properties and their reactivity. This approach highlights the underlying physicochemical processes, and accelerates catalyst design.