An Atomistic Machine Learning Package for Surface Science and Catalysis

TL;DR

This paper introduces a comprehensive software package that facilitates machine learning model development for surface science and catalysis, focusing on atomic structure fingerprinting, descriptor selection, and active learning for structure optimization.

Contribution

It provides an integrated workflow and tools for atomic structure fingerprinting, descriptor selection, and active learning, advancing autonomous exploration in catalysis research.

Findings

Enhanced atomic structure fingerprinting methods

Benchmarking of descriptor selection techniques

Active learning frameworks for structure optimization

Abstract

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.