  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, for optimal structural and conformational generalization, a careful
balance between structural and conformational diversity is required, but
existing QM datasets do not meet that trade-off. Additionally, our results
highlight the limitation of the MLIP models at generalizing beyond their
training distribution, emphasizing the importance of defining applicability
domain during model deployment. These findings provide valuable insights and
guidelines for QM data generation efforts.
