Chapter 6: Practical Application of Machine Learning in Catalysis

In this chapter, we will cover practical tips for translating computational catalysis problems into artificial intelligence/machine learning (AI/ML) questions and approaches, classes of AI/ML methods, and common pitfalls in implementation, statistics, and analysis. Most projects will also require additional data generation and retraining, and active learning (AL) approaches to do this efficiently are nearly as important as the models themselves. The target audience of this chapter is a first-year PhD student who knows they will be working in ML-applications in catalysis or a mid-PhD computational catalysis researcher interested in applying AI/ML methods to their own work.