计算机科学
分子动力学
图形
星团(航天器)
分子图
理论计算机科学
人工神经网络
人工智能
计算化学
化学
程序设计语言
作者
Alfonso Gijón,Miguel Molina-Solana,Juan Gómez‐Romero
标识
DOI:10.1007/978-3-031-36027-5_25
摘要
Regression of potential energy functions is one of the most popular applications of machine learning within the field of materials simulation since it would allow accelerating molecular dynamics simulations. Recently, graph-based architectures have been proven to be especially suitable for molecular systems. However, the construction of robust and transferable potentials, resulting in stable dynamical trajectories, still needs to be researched. In this work, we design and compare several neural architectures with different graph convolutional layers to predict the energy of water cluster anions, a system of fundamental interest in chemistry and biology. After identifying the best aggregation procedures for this problem, we have obtained accurate, fast-evaluated and easy-to-implement graph neural network models which could be employed in dynamical simulations in the future.
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