CURATOR: Building Robust Machine Learning Potentials for Atomistic Simulations Autonomously with Batch Active Learning

16 February 2024, Version 1


To enable fast, resource efficient development and broad scale deployment of of high accuracy Machine-Learned Interatomic Potentials (MLIPs) with minimum expert involvement, we introduce CURATOR, an autonomous batch active learning workflow for constructing MLIPs. CURATOR integrates state of the art models, uncertainty quantification techniques, batch selection algorithms with user defined labeling and chemical-structure space exploration methods for data and compute efficient active learning. We also developed a novel efficient gradient computation method that calculates forces and stress based on the energy derivative with respect to accelerate CURATOR. Our evaluation across different chemical systems demonstrates that CURATOR considerably reduces the computational resources and time required to develop reliable MLIPs. In practical applications in novel complex materials and interfaces, CURATOR shows promising results, underscoring its potential in accelerating materials discovery. The flexibility and efficiency of CURATOR mark a significant advancement in the field of computational materials science, paving the way for more efficient and larger time-length scale atomistic simulations.


Graph neural networks
Batch active learning
Machine learning potential
Uncertainty quantification

Supplementary materials

Supporting Information
Additional analysis and results

Supplementary weblinks


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