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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-04482-7 |
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| Summary: | 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. |
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| ISSN: | 2045-2322 |