Using Machine Learning to Accelerate and Extend Catalyst Theory

Many catalytic phenomena can be prohibitively expensive to study with DFT. We show how machine learning can be used to both accelerate – that is, study more systems faster – and extend – that is, study large and complex systems – catalysis theory, and how these techniques can be improved.