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arXiv 1912.05044cond-mat.mtrl-sciphysics.comp-ph
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一种能够预测不同相硅热导率的统一深度神经网络势

A Unified Deep Neural Network Potential Capable of Predicting Thermal Conductivity of Silicon in Different Phases

Ruiyang Li, Eungkyu Lee, Tengfei Luo

摘要:

分子动力学模拟已被广泛用于预测热性质,但由于缺乏精确的和可转移的原子间势场,使用统一的力场以相似的精度模拟不同的相通常是困难的。因此,这个问题已经成为一个主要的障碍,预测材料的相变及其输运性质的原子级建模技术。最近,基于机器学习的算法已经成为一种很有前途的工具,以开发精确的分子动力学模拟的潜力。在这项工作中,我们的方法预测硅在不同阶段的热导率进行分子动力学模拟与深神经网络潜力的问题。这种神经网络的潜力是训练与从头计算数据的硅在结晶,液体和非晶相。我们的潜力的准确性首先验证通过再现原子结构在相变,在其他经验势通常失败。计算了不同相的热导率,与实验结果和从头算结果吻合较好。我们的工作表明,一个统一的神经网络为基础的潜力可以是一个很有前途的工具,研究相变和材料的热输运的高精度。

英文摘要:

Molecular dynamics simulations have been extensively used to predict thermal properties, but simulating different phases with similar precision using a unified force field is often difficult, due to the lack of accurate and transferrable interatomistic potential fields. As a result, this issue has become a major barrier to predicting the phase change of materials and their transport properties with atomistic-level modeling techniques. Recently, machine learning based algorithms have emerged as promising tools to develop accurate potentials for molecular dynamics simulations. In this work, we approach the problem of predicting the thermal conductivity of silicon in different phases by performing molecular dynamics simulations with a deep neural network potential. This neural network potential is trained with ab-initio data of silicon in the crystalline, liquid and amorphous phases. The accuracy of our potential is first validated through reproducing the atomistic structures during the phase transition, where other empirical potentials usually fail. The thermal conductivity of different phases is then calculated, showing a good agreement with the experimental results and ab-initio calculation results. Our work shows that a unified neural network-based potential can be a promising tool for studying phase change and thermal transport of materials with high accuracy.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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