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arXiv 2012.11137cond-mat.mtrl-sciphysics.comp-ph
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Structural phase transition of two-dimensional monolayer SnTe from artificial neural network

Jiale Zhang, Danni Wei, Feng Zhang, Xi Chen, Dawei Wang

英文摘要:

As machine learning becomes increasingly important in engineering and science, it is inevitable that machine learning techniques will be applied to the investigation of materials, and in particular the structural phase transitions common in ferroelectric materials. Here, we build and train an artificial neural network to accurately predict the energy change associated with atom displacements and use the trained artificial neural network in Monte-Carlo simulations on ferroelectric materials to investigate their phase transitions. We apply this approach to two-dimensional monolayer SnTe and show that it can indeed be used to simulate the phase transitions and predict the transition temperature. The artificial neural network, when viewed as a universal mathematical structure, can be readily transferred to the investigation of other ferroelectric materials when training data generated with ab initio methods are available.

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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