Extended DeepILST for Various Thermodynamic States and Applications in Coarse-Graining
Extended DeepILST for Various Thermodynamic States and Applications in Coarse-Graining
复制标题
适用于各种热力学状态和粗粒应用的扩展 DeepILST
DOI：
10.1021/acs.jpca.1c10865
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Aluru, N. R.
中科院分区：
文献类型：
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作者：
Jeong, J.;Moradzadeh, A.;Aluru, N. R.
分子动力学（MD）模拟被广泛用于在已知原子间势或粗粒势的情况下获得原子系统的微观性质。然而，在许多实际情况下，需要预测原子间或粗粒度的势，这是一个巨大的挑战。基于各种技术，已经开发了许多方法来预测势参数，包括相对熵方法、积分方程理论等，但是这些方法缺乏可转移性并且限于特定范围的热力学状态。最近，已经开发了数据驱动和机器学习方法来克服这些限制。在这项研究中，我们扩展了用于训练深度逆液态理论（DeepILST）1的热力学状态范围，DeepILST 1是一种用于解决液态理论逆问题的深度学习框架。我们还评估了DeepILST在粗粒化各种多原子分子中的性能，并确定了影响DeepILST粗粒化性能的分子特征。
Molecular dynamics (MD) simulations are widely used to obtain the microscopic properties of atomistic systems when the interatomic potential or the coarse-grained potential is known. In many practical situations, however, it is necessary to predict the interatomic or coarse-grained potential, which is a tremendous challenge. Many approaches have been developed to predict the potential parameters based on various techniques, including the relative entropy method, integral equation theory, etc., but these methods lack transferability and are limited to a specific range of thermodynamic states. Recently, data-driven and machine learning approaches have been developed to overcome such limitations. In this study, we expand the range of thermodynamic states used to train deep inverse liquid-state theory (DeepILST)1, a deep learning framework for solving the inverse problem of liquid-state theory. We also assess the performance of DeepILST in coarse-graining various multiatom molecules and identify the molecular characteristics that affect the coarse-graining performance of DeepILST.