npj: 机器学习原子间势—预测锆的相变

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原子模拟为理解结构相变的物理机制提供了有效手段。但因经典的原子间作用势很难同时描述多型键合，因此对于某些具有同素异构转变的相变金属而言，揭示其结构相变的物理过程仍然面临挑战。

来自中国西安交通大学丁向东教授（长江学者）课题组的宗洪祥博士和美国洛斯阿拉莫斯国家实验室的Turab Lookman教授共同领导的团队，采用高斯型机器学习方法开发了一种机器学习与相变理论相结合的方法（ML-AIMD），该方法使得描述相变的原子间作用势能够从头算分子动力学模拟（AIMD）中高保真地学习得到。他们以锆作为模型系统，证明了该方法的可行性和有效性。具体来说，他们基于ML-AIMD方法开发的原子间作用势所预测的声子、弹性常数和堆垛层错能等关键材料属性与实验或者第一性原理的计算结果相一致，并能够正确的描述金属锆中不同相结构之间的转变机制和预测压力-温度相图。可见，使用机器学习开发描述相变系统的原子间势，可以帮助我们更好地模拟复杂系统。

该文近期发表于npj Computational Materials 4: 48 (2018)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。

Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning 

Hongxiang Zong (宗洪祥), Ghanshyam Pilania, Xiangdong Ding(丁向东教授), Graeme J. Ackland & Turab Lookman 

Atomic simulations provide an effective means to understand the underlying physics of structural phase transformations. However, this remains a challenge for certain allotropic metals due to the failure of classical interatomic potentials to represent the multitude of bonding. Based on machine-learning (ML) techniques, we develop a hybrid method in which interatomic potentials describing martensitic transformations can be learned with a high degree of fidelity from ab initio molecular dynamics simulations (AIMD). Using zirconium as a model system, for which an adequate semiempirical potential describing the phase transformation process is lacking, we demonstrate the feasibility and effectiveness of our approach. Specifically, the ML-AIMD interatomic potential correctly captures the energetics and structural transformation properties of zirconium as compared to experimental and density-functional data for phonons, elastic constants, as well as stacking fault energies. Molecular dynamics simulations successfully reproduce the transformation mechanisms and reasonably map out the pressure–temperature phase diagram of zirconium.

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