Artificial intelligence model for efficient simulation of monatomic phase change material antimony
Artificial intelligence model for efficient simulation of monatomic phase change material antimony
复制标题
单原子相变材料锑高效模拟的人工智能模型
DOI：
10.1016/j.mssp.2021.106146
复制
发表时间：
2021
影响因子：
4.1
通讯作者：
Jie Liu
中科院分区：
文献类型：
--
作者：
Mengchao Shi;Junhua Li;Ming Tao;Xin Zhang;Jie Liu
本文提出了一种高效准确的人工智能（AI）模型，通过利用主动学习（AL）和深度学习（DL）等尖端AI技术来计算新兴的单原子相变材料锑。通过使用最先进的AL技术，AI模型训练所需的数据集大小减少了约一个数量级，从而实现了高效的AI模型训练。结果表明，该模型计算的势能面、声子色散、配位数、径向分布函数、角分布函数和相变过程与DFT和基于DFT的分子动力学计算结果吻合较好，表明该模型具有较高的精度.众所周知，所提出的AI模型的模拟时间阿索（n），这比广泛使用的DFT和基于DFT的MD的O（n3）缩放场景要有利得多，为未来一代无有限尺寸效应的全原子器件模拟铺平了道路。
This paper presents an efficient and accurate artificial intelligence (AI) model, to calculate the emerging monatomic phase change material antimony, by leveraging the cutting-edge AI technologies like active learning (AL) and deep learning (DL). The required dataset size of the AI model training is reduced by about one order of magnitude, by using the state-of-the-art AL technology, leading to efficient AI model training. It is shown that the potential energy surface, phonon dispersion, coordination number, radial distribution function, angular distribution function, and phase transition processes calculated by using the proposed AI model could agree well with those calculated by using DFT and DFT-based MD, indicating decent accuracy of the proposed AI model. It is well known that the simulation time of the proposed AI model scales asO(n), which is much more favorable than theO(n3) scaling scenario of the widely-used DFT and DFT-based MD, paving a way to future-generation fully-atomistic device simulations free of finite-size effects.