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arXiv Machine Learning

Truncated automatic sparse differentiation for machine learning interatomic potentials

The paper introduces truncated automatic sparse differentiation (ASD) to efficiently compute higher‑order derivatives, such as Hessians, for machine learning interatomic potentials (MLIPs). By exploiting the locality of atomic interactions, ASD identifies a sparsity pattern that allows exact Hessian calculation for large porous materials, while truncated ASD discards distant, small Hessian entries to achieve order‑of‑magnitude speedups with minimal loss in predictive accuracy. The authors demonstrate these methods on several foundational MLIPs, showing modest speedups for full ASD and significant gains for the truncated approach.

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