Wang Yanxing, Inizan Théo Jaffrelot, Liu Chengwen, Piquemal Jean-Philip, Ren Pengyu
Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States.
Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, Paris 75005, France.
J Phys Chem B. 2024 Mar 14;128(10):2381-2388. doi: 10.1021/acs.jpcb.3c08166. Epub 2024 Mar 6.
Neural network potentials (NNPs) offer significant promise to bridge the gap between the accuracy of quantum mechanics and the efficiency of molecular mechanics in molecular simulation. Most NNPs rely on the locality assumption that ensures the model's transferability and scalability and thus lack the treatment of long-range interactions, which are essential for molecular systems in the condensed phase. Here we present an integrated hybrid model, AMOEBA+NN, which combines the AMOEBA potential for the short- and long-range noncovalent atomic interactions and an NNP to capture the remaining local covalent contributions. The AMOEBA+NN model was trained on the conformational energy of the ANI-1x data set and tested on several external data sets ranging from small molecules to tetrapeptides. The hybrid model demonstrated substantial improvements over the baseline models in term of accuracy as the molecule size increased, suggesting its potential as a next-generation approach for chemically accurate molecular simulations.
神经网络势（NNP）有望弥合分子模拟中量子力学精度与分子力学效率之间的差距。大多数神经网络势依赖局部性假设，该假设确保了模型的可转移性和可扩展性，因此缺乏对长程相互作用的处理，而长程相互作用对于凝聚相中的分子系统至关重要。在此，我们提出了一种集成混合模型AMOEBA+NN，它结合了用于短程和长程非共价原子相互作用的AMOEBA势以及一个神经网络势来捕捉其余的局部共价贡献。AMOEBA+NN模型在ANI-1x数据集的构象能量上进行了训练，并在从小分子到四肽的几个外部数据集上进行了测试。随着分子尺寸的增加，该混合模型在准确性方面相对于基线模型有显著改进，表明其作为下一代化学精确分子模拟方法的潜力。
