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

MBX V1.2: Accelerating Data-Driven Many-Body Molecular Dynamics Simulations DOI
Shreya Gupta, Ethan F. Bull-Vulpe, Henry Agnew

и другие.

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

Опубликована: Фев. 14, 2025

The MBX software provides an advanced platform for molecular dynamics simulations, leveraging state-of-the-art MB-pol and MB-nrg data-driven many-body potential energy functions. Developed over the past decade, these functions integrate physics-based machine-learned terms trained on electronic structure data calculated at "gold standard" coupled-cluster level of theory. Recent advancements in have focused optimizing its performance, resulting release v1.2. While inherently nature ensures high accuracy, it poses computational challenges. v1.2 addresses challenges with significant performance improvements, including enhanced parallelism that fully harnesses power modern multicore CPUs. These enable simulations nanosecond time scales condensed-phase systems, significantly expanding scope high-accuracy, predictive complex systems powered by

Язык: Английский

Процитировано

1
Nuclear Quantum Effects in Neutral Water Clusters at Finite Temperature: Structural Evolution from Two to Three Dimensions DOI

Mengxu Li,

J. A. Kong, Pengju Wang

и другие.

The Journal of Physical Chemistry Letters, Год журнала: 2025, Номер unknown, С. 3004 - 3011

Опубликована: Март 17, 2025

Understanding the structure of bulk water presents a significant challenge due to its intricate hydrogen bond network and dynamic properties. Neutral clusters, serving as fundamental building blocks, provide key insights into configurations intermolecular interactions, thereby establishing critical foundation for elucidating behavior liquid water. In this study, state-of-the-art quantum simulations utilizing many-body potential are employed investigate influence nuclear effects (NQEs) on structural evolution neutral clusters (H2O)n (n = 2–10). For pentamer at finite temperature, demonstrate that NQEs substantially facilitate transition from two-dimensional (2D) three-dimensional (3D) configurations. The population 3D isomers is governed by synergistic interplay among thermal fluctuates NQEs. hexamers with fully structures, uncover lower-energy pathway prism book via cage-like intermediate―a not observed in classical simulations. These findings highlight crucial role theoretical framework explore properties condensed-phase

Язык: Английский

Процитировано

0
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

Процитировано

0