Machine learning potentials for tobermorite minerals
Machine learning potentials for tobermorite minerals
复制标题
DOI：
10.1016/j.commatsci.2020.110173
复制
发表时间：
2021-02-15
影响因子：
3.3
通讯作者：
Okumura, Masahiko
中科院分区：
文献类型：
--
作者：
Kobayashi, Keita;Nakamura, Hiroki;Okumura, Masahiko
分子动力学（MD）模拟是在原子水平上理解水泥水化物物理化学性质的重要工具。具有机器学习潜力（MLP）的MD被认为是一种有前途的方法，用于准确预测材料性能。然而，迄今为止，机器学习MD在具有液-固界面的多组分体系中的应用受到限制。在这项工作中，我们使用人工神经网络（ANN）来构建雪硅钙石矿物的MLP。通过使用不同的目标函数优化产生两个MLP：一个MLP针对密度泛函理论（DFT）结果的力和能量进行优化（MLP-FE），另一个MLP仅拟合能量（MLP-E）。的MLPs的精度进行评估的晶格参数，弹性常数，和体积，剪切模量，和振动态密度。评估结果表明，电位的准确性在很大程度上取决于人工神经网络训练中使用的目标函数，即，MLP-FE与DFT的势能面（PES）相当，尽管MLP-E未能再现DFT给出的几个物理量。为了评估MLP-FE对超出DFT计算限制的扩展系统的适用性，使用MLP-FE对纳米多孔雪硅钙石模型进行了大规模MD模拟。研究了原子在多孔模型液体部分的输运和分布特性。检测到限制在纳米孔中的水的缓慢扩散，并且结果与使用经典力场的实验数据和先前的工作一致。本研究的结果表明，分子动力学与MLP是一种实用的方法，大规模的分子模拟水泥水合物的DFT精度。
Molecular dynamics (MD) simulation is an important tool to understand the physical and chemical properties of cement hydrates at the atomic level. MD with the machine learning potential (MLP) is considered a promising approach for accurate prediction of material properties. However, the applications of machine learning MD for multicomponent systems with a liquid-solid interface have been limited so far. In this work, we used artificial neural networks (ANNs) to construct MLPs for tobermorite minerals. Two MLPs were produced by optimization using different objective functions: one MLP was optimized for both the forces and energies of density functional theory (DFT) results (MLP-FE), and the other MLP was fitted to only the energies (MLP-E). Accuracy assessments of the MLPs were performed for lattice parameters, elastic constants, and bulk, shear moduli, and vibrational density of states. The results of the assessments showed that the accuracy of the potentials largely depended on the objective functions used in the training of the ANNs, i.e., MLP-FE was comparable to the potential energy surface (PES) of DFT, although MLP-E failed to reproduce the several physical quantities given by DFT. To evaluate the applicability of MLP-FE to extended systems beyond the limits of DFT calculations, a large scale MD simulation of a nanoporous tobermorite model was demonstrated using MLP-FE. The transport and distribution properties of atoms in the liquid part of the porous model were investigated. Slow diffusion of water confined in a nanopore was detected, and the results were consistent with experimental data and previous works using classical force fields. The results obtained in this study suggested that MD with MLP is a practical method for large scale molecular simulations of cement hydrates with DFT accuracy.