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arXiv Machine Learning By Marcel F. Langer, Adrian Hill, Michele Ceriotti

Truncated automatic sparse differentiation for machine learning interatomic potentials

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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