Montes-Campos Hadrián, Otero-Lema Martín, Méndez-Morales Trinidad
Grupo 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.
Instituto de Materiais (iMATUS), University of Santiago de Compostela, Campus Vida s/n, 15782, Santiago de Compostela, Galicia, Spain.
Sci Rep. 2025 Jul 1;15(1):22365. doi: 10.1038/s41598-025-04482-7.
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.
在这项工作中，我们评估了基于神经网络的力场，特别是NeuralIL（《化学信息与建模杂志》62卷，88 - 101页，2021年）模拟复杂带电流体的能力。我们关注这种新型力场如何解决此类系统中经典力场的几个病理学缺陷。首先，我们回顾NeuralIL复制系统分子结构的能力。然后，我们分析结构和动力学性质，结果表明弱氢键得到了显著更好的预测，并且其动力学不会像经典力场那样因电子密度缺乏极化而受到阻碍。最后，我们分析NeuralIL对具有质子转移反应的系统进行建模的能力，证明其能够找到并重现系统内发生的反应。此外，我们通过将结果与同一系统平衡系数的先前预测进行比较来验证我们的结果，发现两者高度一致。
