CURATOR: Autonomous Batch Active-Learning Workflow for Catalysts

Xin Yang, Renata Sechi, Martin Hoffmann Petersen, Arghya Bhowmik, Heine Anton Hansen

Submission Track: Tutorials

Submission Category: Automated Material Characterization

Keywords: catalysis, batch active learning, workflow, molecular simulations

TL;DR: We present how to use CURATOR, an autonomous batch active learning workflow for molecular simulations of catalysts.

Abstract:

Machine learning interatomic potentials (MLIPs) enable molecular simulations at longer time scales without compromising accuracy and at lower computational costs compared to electronic structure methods such as density functional theory (DFT). Application of MLIPs to complex functional-materials development can help to create new scientific insights,
however, MLIPs need ad-hoc training for each new system. Reaching sufficient accuracy through large-scale training is data-intensive, and requires a high level of technical proficiency from the user. Reliable MLIP construction requires an appropriate selection of representative structures and calibrated model uncertainty while avoiding undersampling of the state space. Currently, there is a lack of end-to-end automated software to take this complexity away from the end user. In this tutorial, we show how to use CURATOR, an open-source software-based autonomous batch active learning workflow. CURATOR trains message-passing graph neural networks and enables management of model training, production testing, data selection based on uncertainty estimation, optimal batch choice, labeling via DFT-based simulations, and retraining in a user-friendly way.

Submission Number: 23