An Atomistic Machine Learning Package for Surface Science and Catalysis
This provides a tool for researchers in catalysis to streamline model building and reduce reliance on chemical intuition, though it is incremental as it builds on existing methods.
The authors developed a software package with workflows for machine learning model building in surface science and catalysis, including fingerprinting, descriptor selection, and active learning frameworks to automate and enhance exploration of atomic structures.
We present work flows and a software module for machine learning model building in surface science and heterogeneous catalysis. This includes fingerprinting atomic structures from 3D structure and/or connectivity information, it includes descriptor selection methods and benchmarks, and it includes active learning frameworks for atomic structure optimization, acceleration of screening studies and for exploration of the structure space of nano particles, which are all atomic structure problems relevant for surface science and heterogeneous catalysis. Our overall goal is to provide a repository to ease machine learning model building for catalysis, to advance the models beyond the chemical intuition of the user and to increase autonomy for exploration of chemical space.