优化器对神经网络力场性能的影响与分析  

Impact and Analysis of Optimizers on the Performance of Neural Network Force Fields

作　　者：李恩吉 胡思宇 谭光明[1,2] 贾伟乐 LI Enji;HU Siyu;TAN Guangming;JIA Weile(State Key Lab of Processors,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100190,China)

机构地区：[1]中国科学院计算技术研究所处理器芯片全国重点实验室,北京100190 [2]中国科学院大学,北京100190

出　　处：《计算机科学》2025年第5期50-57,共8页Computer Science

基　　金：中国科学院战略性先导科技专项资助(XDB0500102);国家自然科学基金(92270206,T2125013,62372435,62032023,61972377,61972380,T2293702);中国科学院青年基础研究项目(YSBR-005);国家创新人才博士后计划(BX20240383)。

摘　　要：分子动力学模拟是一种广泛应用于多个学科(如材料科学、计算化学等)的关键研究方法。近年来,随着计算能力的提升、神经网络模型的发展以及第一性原理数据的增加,神经网络力场模型已经展现出高精度的预测能力。目前存在多种神经网络力场模型的训练算法,而神经网络力场模型处于一个快速迭代的阶段,当前仍然缺乏神经网络力场模型及与之适配的优化器的指导建议。选取3种有代表性的神经网络力场模型和目前3种用于神经网络力场模型上的优化算法,在4个真实数据集上进行测试和评估,分析影响其收敛性的原因。设计实验对其进行全方位的评估,包括模型参数量对优化器的影响,神经网络宽度对收敛性的影响,以及模型训练时间与优化器的关联等。文中工作可以针对神经网络力场模型,给出优化器算法的建议。Molecular dynamics(MD)simulation is widely used in various fields,such as materials science and computational chemistry.In recent years,with the improvement in computational power,the development of neural network models,and the accumulation in first-principle data,neural network force field(NNFF)models have demonstrated high predictive accuracy.Curren-tly,there are multiple training algorithms available for NNFF models,and these models are undergoing rapid iteration.However,there remains a lack of guidance on NNFF models and their compatible optimizers.This paper selects three representative NNFF models and the three most commonly used optimization algorithms for these models,testing and evaluating them on four real-world datasets to analyze factors affecting their convergence.We have designed numerous experiments for a comprehensive evaluation,including the impact of model parameter size on the optimizer,the influence of model depth and width on convergence,and the relationship between model training time and the optimizer.Our work provides recommendations for optimizer algorithms specific to NNFF models.

关 键 词：分子动力学模拟 神经网络 力场训练 优化器 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]