Short-Range Δ-Machine Learning: A Cost-Efficient Strategy to Transfer Chemical Accuracy to Condensed Phase Systems DOI
Bence Balázs Mészáros,

András Szabó,

János Daru

и другие.

Journal of Chemical Theory and Computation, Год журнала: 2025, Номер unknown

Опубликована: Май 28, 2025

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 ( Phys. Rev. Lett. 2022, 129, 226001) demonstrated that high-accuracy MLPs can be within CCMD framework using extended yet finite Here, we introduce short-range Δ-Machine Learning (srΔML), a method starts from baseline MLP on low-level data and adds Δ-MLP correction based high-level cluster calculations at CC level. Applied liquid water, srΔML reduces required size (H2O)64 (H2O)15 significantly lowers number clusters needed, resulting in 50-200× reduction computational cost. The potential closely reproduces target accurately captures both two- three-body structural descriptors.

Язык: Английский

Short-Range Δ-Machine Learning: A Cost-Efficient Strategy to Transfer Chemical Accuracy to Condensed Phase Systems DOI
Bence Balázs Mészáros,

András Szabó,

János Daru

и другие.

Journal of Chemical Theory and Computation, Год журнала: 2025, Номер unknown

Опубликована: Май 28, 2025

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 ( Phys. Rev. Lett. 2022, 129, 226001) demonstrated that high-accuracy MLPs can be within CCMD framework using extended yet finite Here, we introduce short-range Δ-Machine Learning (srΔML), a method starts from baseline MLP on low-level data and adds Δ-MLP correction based high-level cluster calculations at CC level. Applied liquid water, srΔML reduces required size (H2O)64 (H2O)15 significantly lowers number clusters needed, resulting in 50-200× reduction computational cost. The potential closely reproduces target accurately captures both two- three-body structural descriptors.

Язык: Английский

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