Addressing pathological deficiencies in the simulation of complex charged fluids using neural network force fields.

Hadrián Montes-Campos, Martín Otero-Lema, Trinidad Méndez-Morales

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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3 authors.

Hadrián Montes-CamposGrupo de Nanomateriais, Fotónica e Materia Branda, Departamento de Física de Partículas, Facultade de Física, University of Santiago de Compostela, Campus Vida s/n, 15782, Santiago de Compostela, Galicia, Spain. hadrian.montes@usc.es.

Martín Otero-LemaGrupo de Nanomateriais, Fotónica e Materia Branda, Departamento de Física de Partículas, Facultade de Física, University of Santiago de Compostela, Campus Vida s/n, 15782, Santiago de Compostela, Galicia, Spain.

Trinidad Méndez-MoralesGrupo de Nanomateriais, Fotónica e Materia Branda, Departamento de Física de Partículas, Facultade de Física, University of Santiago de Compostela, Campus Vida s/n, 15782, Santiago de Compostela, Galicia, Spain.

Funding

European Regional Development Fund 0160 HI_MOV_1_EMinisterio de Ciencia e Innovación PID2021-126148NA-I00Universidade de Santiago de Compostela Convocatoria de Recualificaci´on do Sistema Universitario Español-Margarita Salas

6 · The paper itself

Abstract

In this work, we evaluate the capability of Neural Network-based force fields, particularly NeuralIL (J Chem. Inf. Model. 62, 88-101, 2021), to simulate complex charged fluids. We focus on how this novel force field can address several pathological deficiencies of classical force fields for such systems. First, we review the capability of NeuralIL to replicate the molecular structures of the system. Then, we analyze the structural and dynamic properties, showing that weak hydrogen bonds are significantly better predicted and that their dynamics are not hindered by the absence of polarization of the electronic densities as seen in classical force fields. Finally, we analyze the capability of NeuralIL to model systems with proton transfer reactions, demonstrating its ability to find and reproduce the reactions that take place within the system. Moreover, we validate our results by comparing them with previous predictions of the equilibrium coefficient for the same system, finding a strong agreement.

Indexed as

Complex charged fluidsDensity functional theoryNeural network force fieldsProton transfer

Identifiers

PMID40592901

DOI10.1038/s41598-025-04482-7

PMCPMC12215625

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