Data Curation for Machine Learning Interatomic Potentials by Determinantal Point Processes
The development of machine learning interatomic potentials faces a critical computational bottleneck with the generation and labeling of useful training datasets. We present a novel application of determinantal point processes (DPPs) to the task of selecting informative subsets of atomic configurations to label with reference energies and forces from costly quantum mechanical methods. Through experiments with hafnium oxide data, we show that DPPs are competitive with existing approaches to constructing compact but diverse training sets by utilizing kernels of molecular descriptors, leading to improved accuracy and robustness in machine learning representations of molecular systems. Our wo...
| Authors | Joanna Zou, Youssef Marzouk |
|---|---|
| Primary category | stat.AP |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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