加速水团阴离子蒙特卡罗模拟的图神经网络势能面

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Computational Science Pub Date : 2024-07-22 DOI:10.1016/j.jocs.2024.102383
Alfonso Gijón , Miguel Molina-Solana , Juan Gómez-Romero
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引用次数: 0

摘要

势能函数回归是机器学习在材料模拟领域最普遍的应用之一,有望将模拟速度提高几个数量级。最近,基于图的架构已成为分子系统建模的最佳选择。然而,如何开发稳健且可转移的势能,从而针对不同尺寸和物理条件进行稳定的模拟,仍然是一个需要持续研究的领域。在本研究中,我们比较了几种图神经网络在预测水簇阴离子能量方面的性能,水簇阴离子是化学和生物学中的一个重要系统。在确定图注意网络是这项任务的最佳聚合程序后,我们获得了一个高效、准确的能量模型。然后,我们利用该模型对不同大小的簇进行蒙特卡罗模拟,结果显示了稳定的行为。值得注意的是,预测的表面到内部状态转换点和系统的主体能量与其他研究结果一致,而计算成本却低了三个数量级。
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Graph-neural-network potential energy surface to speed up Monte Carlo simulations of water cluster anions

Regression of potential energy functions stands as one of the most prevalent applications of machine learning in the realm of materials simulation, offering the prospect of accelerating simulations by several orders of magnitude. Recently, graph-based architectures have emerged as particularly adept for modeling molecular systems. However, the development of robust and transferable potentials, leading to stable simulations for different sizes and physical conditions, remains an ongoing area of investigation. In this study, we compare the performance of several graph neural networks for predicting the energy of water cluster anions, a system of fundamental interest in Chemistry and Biology. Following the identification of the graph attention network as the optimal aggregation procedure for this task, we obtained an efficient and accurate energy model. This model is then employed to conduct Monte Carlo simulations of clusters across different sizes, demonstrating stable behavior. Notably, the predicted surface-to-interior state transition point and the bulk energy of the system are consistent with findings from other investigations, at a computational cost three-orders of magnitude lower.

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来源期刊
Journal of Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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