Machine-learning accelerated geometry optimization in molecular simulation
Machine-learning accelerated geometry optimization in molecular simulation
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DOI：
10.1063/5.0049665
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发表时间：
2021-06-21
影响因子：
4.4
通讯作者：
Kitchin, John R.
中科院分区：
文献类型：
--
作者：
Yang, Yilin;Jimenez-Negron, Omar A.;Kitchin, John R.
几何优化是计算材料和表面科学的重要组成部分，因为它是寻找基态原子结构和反应途径的途径。这些性质用于分子和晶体结构的热力学和动力学性质的估计。这个过程在理论的量子水平上是缓慢的，因为它涉及使用量子化学代码（诸如密度泛函理论（DFT））的力的迭代计算，这在计算上是昂贵的并且限制了优化算法的速度。加速这一过程是非常有利的，因为这样一来，人们可以在更短的时间内完成同样数量的工作，也可以在相同的时间内完成更多的工作。在这项工作中，我们提供了一个神经网络（NN）集成为基础的主动学习方法，以加速多个配置的局部几何优化同时进行。我们说明了几个案例研究，包括裸露的金属表面，表面吸附，和轻推弹性带两个反应的加速度。在所有情况下，加速方法比标准方法需要更少的DFT计算。此外，我们提供了一个原子仿真环境（ASE）优化器Python包，使使用NN集成主动学习几何优化更容易。
Geometry optimization is an important part of both computational materials and surface science because it is the path to finding ground state atomic structures and reaction pathways. These properties are used in the estimation of thermodynamic and kinetic properties of molecular and crystal structures. This process is slow at the quantum level of theory because it involves an iterative calculation of forces using quantum chemical codes such as density functional theory (DFT), which are computationally expensive and which limit the speed of the optimization algorithms. It would be highly advantageous to accelerate this process because then one could do either the same amount of work in less time or more work in the same time. In this work, we provide a neural network (NN) ensemble based active learning method to accelerate the local geometry optimization for multiple configurations simultaneously. We illustrate the acceleration on several case studies including bare metal surfaces, surfaces with adsorbates, and nudged elastic band for two reactions. In all cases, the accelerated method requires fewer DFT calculations than the standard method. In addition, we provide an Atomic Simulation Environment (ASE)-optimizer Python package to make the usage of the NN ensemble active learning for geometry optimization easier.