基于机器学习和第一性原理计算的Janus材料预测

PAPERS Prediction of magnetic Janus materials based on machine learning and first-principles calculations

寻找尺寸小、稳定性高和易操控的纳米磁结构——磁斯格明子(magnetic skyrmion),是发展下一代高密度、高速度和低能耗非易失性信息存储器件核心存储单元的关键.磁性斯格明子根据其拓扑产生机制,可以由非中心对称结构诱导的DMI(Dzyaloshinskii–Moriya interaction)作用项产生.二维Janus结构具有两个不同面的原子层,可以形成垂直内建电场,打破中心空间反演对称性.因此寻找具有本征磁性的二维Janus材料是研究新型磁存储的基础.本文基于晶体材料数据库Materials Project中的1179种六角晶系ABC型Janus材料数据,以其元素组分信息为特征描述符,构建了随机森林、梯度提升决策树、极端梯度提升和极端随机树等四种机器学习模型,基于上述模型对晶格常数、形成能和磁矩分类进行了预测,并采用十折交叉验证法对模型进行了评估.梯度提升决策树在磁矩分类预测显示出最高的精度和泛化能力.最后,基于上述模型对尚未发现的82018种二维Janus材料进行了预测,筛选得到4024种具有热稳定性的高磁矩结构,并基于第一性原理的方法对其中随机抽样的13种高磁矩结构进行了计算验证.本研究为二维Janus材料磁矩分类和高通量筛选训练了有效的机器学习模型,加速了二维Janus结构磁性的探索.本文数据集可在https://doi.org/10.57760/sciencedb.j00213.00072中访问获取.

Discovering compact,stable,and easily controllable nanoscale non-trivial topological magnetic structures,such as magnetic skyrmions,is the key to developing next-generation high-density,high-speed,and low-energy non-volatile information storage devices.Based on the topological generation mechanism,magnetic skyrmions can be generated through the Dzyaloshinskii–Moriya interaction(DMI)caused by breaking space-reversal symmetry.Two-dimensional(2D)non-centrosymmetric Janus structurecan generate vertical built-in electric fields to break spatial inversion symmetry.Therefore,seeking for 2D Janus material with intrinsic magnetism is fundamental to develop the novel chiral magnetic storage technologies.In this work,we combine detailed machine learning techniques and first-principle calculations to investigate the magnetism of the unexplored 2D Janus material.We first collect 11792D hexagonal ABC-type Janus materials based on the Materials Project database,and use elemental composition as feature descriptors to construct four machine learning models:random forest(RF),gradient boosting decision trees(GBDT),extreme gradient boosting(XGB),and extra trees(ET).These algorithms and models are constructed to predict lattice constants,formation energy,and magnetic moment,via hyperparameter optimization and ten-fold cross-validation.The GBDT exhibits the highest accuracy and best prediction performance for magnetic moment classification.Subsequently,the collected data of 82018 yet-undiscovered 2D Janus materials,are input into the trained models to generate 4024 high magnetic moment 2D Janus materials with thermal stability.First-principles calculations are employed to validate random sample of 13 Janus materials with high magnetic moment.This study provides an effective machine learning framework for classifying the magnetic moments and screening highthroughput 2D Janus structures,thereby accelerating the exploration of their magnetic properties.