Accurate Simulations of Water and Aqueous Solutions through Fine-Tuned Dispersion-Corrected Density Functional Theory and Machine-Learning Interatomic Potentials.

ABSTRACT: Dispersion-corrected density functional theory (DFT-D) is widely employed to model large molecular systems at an affordable computational cost and to develop machine-learning interatomic potentials (MLIPs), enabling reliable molecular dynamics (MD) simulations of condensed-phase systems. Yet, given a molecular system, the choice of a specific DFT-D model that can achieve the necessary accuracy over an extended range of physicochemical properties and conditions is generally not trivial. Here, we report an effective computational strategy for enhancing the accuracy of standard DFT-D models toward high-level quantum mechanical data and for developing MLIPs preserving the same high fidelity. Taking water as a paradigmatic example, we derive a novel MLIP and demonstrate that its use allows us t

SUBMITTER: Ferretti A 

PROVIDER: S-EPMC12648658 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature