用机器学习直接预测分子材料的范德华作用关键参数。
MBD-ML: Many-body dispersion from machine learning for molecules and materials
- 用神经网络从原子结构直接预测C6系数和极化率。
- 无需电子结构计算即可快速获得高精度范德华能量与力。
- 适合需要精准范德华作用的分子动力学与力场研究者。
范德华（vdW）相互作用对药物设计、催化及电池应用中的分子与材料描述至关重要，也必须准确纳入机器学习势能场。多体色散（MBD）方法是目前最精确且可迁移的vdW相互作用描述方法，仅需原子C6系数和极化率作为输入。我们提出MBD-ML，一种预训练的消息传递神经网络，可直接从原子结构预测这些原子属性。通过与libMBD无缝集成，该方法可立即计算包含MBD的总能量、原子力与应力张量。无需中间电子结构计算，MBD-ML提供了一种实用且高效的工具，可将最先进的vdW相互作用轻松引入任何电子结构代码及经验或机器学习力场中。
原文摘要 · Abstract (English)
Van der Waals (vdW) interactions are essential for describing molecules and materials, from drug design and catalysis to battery applications. These omnipresent interactions must also be accurately included in machine-learned force fields. The many-body dispersion (MBD) method stands out as one of the most accurate and transferable approaches to capture vdW interactions, requiring only atomic $C_6$ coefficients and polarizabilities as input. We present MBD-ML, a pretrained message passing neural network that predicts these atomic properties directly from atomic structures. Through seamless integration with libMBD, our method enables the immediate calculation of MBD-inclusive total energies, forces, and stress tensors. By eliminating the need for intermediate electronic structure calculations, MBD-ML offers a practical and streamlined tool that simplifies the incorporation of state-of-the-art vdW interactions into any electronic structure code, as well as empirical and machine-learned force fields.
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