Interpretable Machine Learning for Catalytic Materials Design toward Sustainability

ConspectusFinding catalytic materials with optimal properties for sustainable chemical and energy transformations is one of the pressing challenges facing our society today. Traditionally, the discovery of catalysts or the philosopher’s stone of alchemists relies on a trial-and-error approach with physicochemical intuition. Decades-long advances in science and engineering, particularly in quantum chemistry and computing infrastructures, popularize a paradigm of computational science for materials discovery. However, the brute-force search through a vast chemical space is hampered by its formidable cost. In recent years, machine learning (ML) has emerged as a promising approach to streamline the design of active sites by learning from data. As ML is increasingly employed to make predictions in practical settings, the demand for domain interpretability is surging. Therefore, it is of great importance to provide an in-depth review of our efforts in tackling this challenging issue in computational heterogeneous catalysis.In this Account, we present an interpretable ML framework for accelerating catalytic materials design, particularly in driving sustainable carbon, nitrogen, and oxygen cycles. By leveraging the linear adsorption-energy scaling and Brønsted–Evans–Polanyi (BEP) relationships, catalytic outcomes (i.e., activity, selectivity, and stability) of a multistep reaction can often be mapped onto one or two kinetics-informed descriptors. One type of descriptor of great importance is the adsorption energies of representative species at active site motifs that can be computed from quantum-chemical simulations. To complement such a descriptor-based design strategy, we delineate our endeavors in incorporating domain knowledge into a data-driven ML workflow. We demonstrate that the major drawbacks of black-box ML algorithms, e.g., poor explainability, can be largely circumvented by employing (1) physics-inspired feature engineering, (2) Bayesian statistical learning, and (3) theory-infused deep neural networks. The framework drastically facilitates the design of heterogeneous metal-based catalysts, some of which have been experimentally verified for an array of sustainable chemistries. We offer some remarks on the existing challenges, opportunities, and future directions of interpretable ML in predicting catalytic materials and, more importantly, on advancing catalysis theory beyond conventional wisdom. We envision that this Account will attract more researchers’ attention to develop highly accurate, easily explainable, and trustworthy materials design strategies, facilitating the transition to the data science paradigm for sustainability through catalysis.