Machine learning methods for theoretical heterogeneous catalysis: current status and challenges

Catalysis, the science of accelerating chemical reactions and regulating the selectivity of reaction products, lies in the heart of modern chemical industry. Typically, catalysis can be categorized into heterogeneous catalysis and homogeneous catalysis. Over the past century, heterogeneous catalytic processes such as ammonia synthesis and Fischer-Tropsch synthesis have become an indispensable part of the chemical industry. In recent years, the rational design of catalysts has gained a lot of attention due to both economic and environmental concerns, which has raised new demands for a deep understanding of the mechanism of catalytic reactions. Machine learning methods as one of the most important areas of artificial intelligence (AI) are becoming more and more popular in practices of theoretical simulations. This review gives an overview on various machine learning methods and their applications in theoretical heterogeneous catalysis. Starting with statistical learning models on small datasets based on specific physical descriptors, which play an important role in early theoretical heterogeneous research, this review analyzes how they connect linear scaling relationship and catalytic experiments. However, due to the complexity of heterogeneous catalytic systems, sometimes nonlinear models can be essential. In this way, the exact form of equations should be obtained before fitting, which is extremely challenging. This problem can be solved by methods based on symbolic regression like SISSO to obtain a set of expressions, and the researchers can select one by their domain knowledge. This review also shows the applications of some popular machine learning methods (such as SVM, PCA, RF, etc.) in research, most of which are based on specific descriptors, simple but have difficulty transferring to other systems. First-principle calculations like DFT are crucial in catalysis rational design by computational simulations for surface structure determination and reaction channel exploration, but they are impractical for simulations on large and complex catalytic systems due to high computational costs. In recent years, machine learning potentials (MLPs), as tools to bridge the gap between first-principle accuracy and computational efficiency, have pushed forward the frontier of theoretical heterogeneous catalysis. Popular MLPs (LASP, EANN, DP, GemNet-OC, MACE, DPA, etc.) have numerous applications in this field, from global optimization and molecular dynamics of active surfaces under reaction conditions to automatic reaction network exploration. Recently, universal machine learning potentials have gained much attention and are very promising in heterogeneous catalysis simulations, for their promising capability to encapsulate huge chemical space of the whole periodic table in one pre-trained model, which is easy to fine-tune on a specific chemical system, efficiently deploying a specific MLP with high accuracy and transferability. Recent advances in generative models have shown considerable promise for heterogeneous catalysis research, and the dramatic emergence of large language models like ChatGPT is especially important in this case. LLMs can incorporate information from massive natural language data, having a huge ability to push forward the understanding of topics in catalytic science. Furthermore, the concept of generative models themselves has much potential in generating novel catalytic information directly without natural language as the intermediate, but their application in heterogeneous catalysis is under limitation due to the lack of large datasets with high quality. In a nutshell, machine learning methods play a key role in theoretical understanding of heterogeneous catalytic systems. Challenges remain due to the complexity of heterogeneous catalytic systems and lack of high-quality dataset, but the era of AI-driven rational design of surface catalysts is truly coming.