论文标题
使用无序结构的训练机学习潜力进行晶体结构预测
Training machine-learning potentials for crystal structure prediction using disordered structures
论文作者
论文摘要
对具有未探索组合物的多元(三元或更高)化合物的稳定晶体结构的预测,需要快速,准确地评估自由能,以探索庞大的配置空间。机器学习的潜力(例如神经网络电位(NNP))有望满足这一要求,但缺乏有关晶体结构的信息在选择训练集方面构成了挑战。本文中,我们建议从基于密度功能理论(DFT)的液体和淬灭无定形相的动力学轨迹构建训练集,该轨迹不需要任何先前有关材料结构的信息,除了化学组成外。为了证明训练有素的NNP在晶体结构预测中的适用性,我们比较了BA2AGSI3,MG2SIO4,LIALCL4和Inte2O5F的NNP和DFT能量,并比较实验阶段以及低能晶体结构的NNP和DFT能量。对于每种材料,我们都会发现DFT和NNP能量之间的强相关性,以确保NNP可以正确对低能晶体结构中的能量进行正确排除。我们还发现,使用NNP的进化搜索可以比基于DFTB的方法更有效地识别低能稳态阶段。通过提出一种为晶体结构预测开发可靠的机器学习势的方法,这项工作将为有效识别未开发的多个阶段铺平道路。
Prediction of the stable crystal structure for multinary (ternary or higher) compounds with unexplored compositions demands fast and accurate evaluation of free energies in exploring the vast configurational space. The machine-learning potential such as the neural network potential (NNP) is poised to meet this requirement but a dearth of information on the crystal structure poses a challenge in choosing training sets. Herein we propose constructing the training set from densityfunctional-theory (DFT) based dynamical trajectories of liquid and quenched amorphous phases, which does not require any preceding information on material structures except for the chemical composition. To demonstrate suitability of the trained NNP in the crystal structure prediction, we compare NNP and DFT energies for Ba2AgSi3, Mg2SiO4, LiAlCl4, and InTe2O5F over experimental phases as well as low-energy crystal structures that are generated theoretically. For every material, we find strong correlations between DFT and NNP energies, ensuring that the NNPs can properly rank energies among low-energy crystalline structures. We also find that the evolutionary search using the NNPs can identify low-energy metastable phases more efficiently than the DFTbased approach. By proposing a way to developing reliable machine-learning potentials for the crystal structure prediction, this work will pave the way to identifying unexplored multinary phases efficiently.