Role of Structural and Conformational Diversity for Machine Learning Potentials. (arXiv:2311.00862v1 [physics.chem-ph])

In the field of Machine Learning Interatomic Potentials (MLIPs),
understanding the intricate relationship between data biases, specifically
conformational and structural diversity, and model generalization is critical
in improving the quality of Quantum Mechanics (QM) data generation efforts. We
investigate these dynamics through two distinct experiments: a fixed budget
one, where the dataset size remains constant, and a fixed molecular set one,
which focuses on fixed structural diversity while varying conformational
diversity. Our results reveal nuanced patterns in generalization metrics.
Notably, …

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