Learning from data to design functional materials without inversion symmetry.
Learning from data to design functional materials without inversion symmetry.
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DOI：
10.1038/ncomms14282
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发表时间：
2017-02-17
影响因子：
16.6
通讯作者：
Rondinelli JM
中科院分区：
文献类型：
--
作者：
Balachandran PV;Young J;Lookman T;Rondinelli JM
加速功能材料的研究是一个具有挑战性的问题。在这里，我们开发了一个信息引导的从头算方法，以加速非中心对称材料的设计和发现。该工作流程集成了群论，信息学和密度泛函理论，以揭示预测非中心对称化合物的设计准则，我们将其应用于层状Ruddlesden-Popper氧化物。群论确定了氧八面体旋转模式，有序阳离子排列及其相互作用如何打破反转对称性，而信息学工具从现有数据中学习，以选择满足群论假设的候选组合物。我们的主要成果是在筛选了13，200种表现出非中心对称结构潜力的组合物后，鉴定出242种组合物，这是已知非中心对称Ruddlesden-Popper氧化物预计数量的25倍。我们用声子计算验证了我们对19个化合物的预测，其中17个具有非中心对称基态，包括两个潜在的多铁性。我们的方法能够合理设计具有目标晶体对称性和功能的材料。反演对称性破缺功能材料的计算设计是一项复杂的任务。在这里，作者展示了一种集成对称性分析、数据科学方法和密度泛函理论的方法，以加速复杂氧化物的选择和识别过程。
Accelerating the search for functional materials is a challenging problem. Here we develop an informatics-guided ab initio approach to accelerate the design and discovery of noncentrosymmetric materials. The workflow integrates group theory, informatics and density-functional theory to uncover design guidelines for predicting noncentrosymmetric compounds, which we apply to layered Ruddlesden-Popper oxides. Group theory identifies how configurations of oxygen octahedral rotation patterns, ordered cation arrangements and their interplay break inversion symmetry, while informatics tools learn from available data to select candidate compositions that fulfil the group-theoretical postulates. Our key outcome is the identification of 242 compositions after screening ∼3,200 that show potential for noncentrosymmetric structures, a 25-fold increase in the projected number of known noncentrosymmetric Ruddlesden-Popper oxides. We validate our predictions for 19 compounds using phonon calculations, among which 17 have noncentrosymmetric ground states including two potential multiferroics. Our approach enables rational design of materials with targeted crystal symmetries and functionalities. Computational design of functional materials with broken inversion symmetry is a complex task. Here, the authors demonstrate an approach that integrates symmetry analysis, data science methods, and density functional theory to accelerate the selection and identification process in complex oxides.