Short-range $\Delta$-Machine Learning: A cost-efficient strategy to transfer chemical accuracy to condensed phase systems

Bence Balázs Mészáros; András Szabó; János Daru

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arXiv · 2502.16930

Short-range $\Delta$-Machine Learning: A cost-efficient strategy to transfer chemical accuracy to condensed phase systems

Bence Balázs Mészáros·András Szabó·János Daru

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Abstract

DFT-based machine-learning potentials (MLPs) are now routinely trained for condensed-phase systems, but surpassing DFT accuracy remains challenging due to the cost or unavailability of periodic reference calculations. Our previous work (PRL 2022, 129, 226001) demonstrated that high-accuracy periodic MLPs can be trained within the CCMD framework using extended yet finite reference calculations. Here, we introduce short-range $\Delta$-Machine Learning (sr$\Delta$ML), which builds on periodic MLPs while accurately reproducing the observables of the high-level method.

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Bence Balázs Mészáros, András Szabó, János Daru. 2025-02-24. Short-range $\Delta$-Machine Learning: A cost-efficient strategy to transfer chemical accuracy to condensed phase systems. https://arxiv.org/abs/2502.16930

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