The potential energy surface (PES) calculation is the bottleneck for modern material simulation. The high‐dimensional neural network (HDNN) technique emerged recently appears to be a problem solver for fast and accurate PES computation. The major cost of the HDNN lies at the computation of the structural descriptors that capture the geometrical environment of atoms. Here, we introduce a massive parallelization strategy optimized for our recently developed power‐type structural descriptor. The method involves three‐levels: from the top to the bottom the parallelization is over atoms first, then, over structural descriptors and finally over the n‐body functions. We illustrate the parallelization method in a boron crystal system and show that the parallelization efficiency is maximally 100%, 58%, and 34% at each level. © 2018 Wiley Periodicals, Inc.

中文翻译：

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使用高维神经网络势进行材料模拟的大规模并行化策略

势能面 (PES) 计算是现代材料模拟的瓶颈。最近出现的高维神经网络 (HDNN) 技术似乎是快速准确的 PES 计算的问题解决者。HDNN 的主要成本在于计算捕获原子几何环境的结构描述符。在这里，我们引入了针对我们最近开发的功率型结构描述符优化的大规模并行化策略。该方法涉及三个层次：从上到下并行化首先是原子，然后是结构描述符，最后是 n 体函数。我们举例说明了硼晶体系统中的并行化方法，并表明每个级别的并行化效率最大为 100%、58% 和 34%。© 2018 Wiley Periodicals, Inc.