Neural networks to approach potential energy surfaces: Application to a molecular dynamics simulation
Neural networks to approach potential energy surfaces: Application to a molecular dynamics simulation
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接近势能面的神经网络：在分子动力学模拟中的应用
DOI：
10.1002/qua.21398
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发表时间：
2007
影响因子：
2.2
通讯作者：
F. M. Fernandes
中科院分区：
文献类型：
--
作者：
Diogo Latino;F. Freitas;J. Aires;F. M. Fernandes
势能面 (PES) 对于通过蒙特卡罗 (MC) 或分子动力学 (MD) 模拟研究反应性和非反应性化学系统至关重要。理想情况下，PES 应具有从头计算提供的精度，并尽可能快地设置。最近，神经网络 (NN) 被证明是一种从头算/DFT 能量数据集估计 PES 的合适方法。然而，据我们所知，MC 和 MD 模拟方法从神经网络表面确定的属性的准确性尚未在训练所需的最小能量点数量和不同神经网络类型的使用方面进行系统分析。这项工作的目标是训练神经网络再现由众所周知的分析势函数表示的 PES，然后通过比较从神经网络和分析 PES 获得的模拟结果来评估该方法的准确性。前馈神经网络 (EnsFFNN) 和关联神经网络 (ASNN) 的集成用于估计全能量表面。使用来自 15 个不同参数化 Lennard-Jones (LJ) 势的不同点数的训练集，并使用氩气来测试网络。 MD 模拟是使用神经网络预测的表格势能进行的，用于计算热、结构和动态特性，并将这些特性与从解析函数获得的值进行比较。
Potential energy surfaces (PES) are crucial to the study of reactive and nonreactive chemical systems by Monte Carlo (MC) or molecular dynamics (MD) simulations. Ideally, PES should have the accuracy provided by ab initio calculations and be set up as fast as possible. Recently, neural networks (NNs) turned out as a suitable approach for estimating PES from ab initio/DFT energy datasets. However, the accuracy of the properties determined by MC and MD simulation methods from NNs surfaces has not yet, to our knowledge, been systematically analyzed in terms of the minimum number of energy points required for training and the usage of different NN- types. The goal of this work is to train NNs for reproducing PES represented by well- known analytical potential functions, and then to assess the accuracy of the method by comparing the simulation results obtained from NNs and analytical PES. Ensembles of feed-forward neural networks (EnsFFNNs) and associative neural networks (ASNNs) are used to estimate the full energy surface. Training sets with different number of points, from 15 differently parameterized Lennard-Jones (LJ) potentials, are used and argon is taken to test the network. MD simulations have been performed using the tabular potential energies, predicted by NNs, for working out thermal, structural, and dynamic properties which are compared with the values obtained from the analytical function.