Addressing pathological deficiencies in the simulation of complex charged fluids using neural network force fields
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...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-04482-7 |
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| author | Hadrián Montes-Campos Martín Otero-Lema Trinidad Méndez-Morales |
| author_facet | Hadrián Montes-Campos Martín Otero-Lema Trinidad Méndez-Morales |
| author_sort | Hadrián Montes-Campos |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e01cc3b66a2c4c0ca26767e80950c618 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e01cc3b66a2c4c0ca26767e80950c6182025-08-20T03:45:19ZengNature PortfolioScientific Reports2045-23222025-07-0115111210.1038/s41598-025-04482-7Addressing pathological deficiencies in the simulation of complex charged fluids using neural network force fieldsHadrián Montes-Campos0Martín Otero-Lema1Trinidad Méndez-Morales2Grupo de Nanomateriais, Fotónica e Materia Branda, Departamento de Física de Partículas, Facultade de Física, University of Santiago de CompostelaGrupo de Nanomateriais, Fotónica e Materia Branda, Departamento de Física de Partículas, Facultade de Física, University of Santiago de CompostelaGrupo de Nanomateriais, Fotónica e Materia Branda, Departamento de Física de Partículas, Facultade de Física, University of Santiago de CompostelaAbstract 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.https://doi.org/10.1038/s41598-025-04482-7Neural network force fieldsComplex charged fluidsProton transferDensity functional theory |
| spellingShingle | Hadrián Montes-Campos Martín Otero-Lema Trinidad Méndez-Morales Addressing pathological deficiencies in the simulation of complex charged fluids using neural network force fields Scientific Reports Neural network force fields Complex charged fluids Proton transfer Density functional theory |
| title | Addressing pathological deficiencies in the simulation of complex charged fluids using neural network force fields |
| title_full | Addressing pathological deficiencies in the simulation of complex charged fluids using neural network force fields |
| title_fullStr | Addressing pathological deficiencies in the simulation of complex charged fluids using neural network force fields |
| title_full_unstemmed | Addressing pathological deficiencies in the simulation of complex charged fluids using neural network force fields |
| title_short | Addressing pathological deficiencies in the simulation of complex charged fluids using neural network force fields |
| title_sort | addressing pathological deficiencies in the simulation of complex charged fluids using neural network force fields |
| topic | Neural network force fields Complex charged fluids Proton transfer Density functional theory |
| url | https://doi.org/10.1038/s41598-025-04482-7 |
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