Learning from data to design functional materials without inversion symmetry

Volume: 8, Issue: 1
Published: Feb 17, 2017
Abstract
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...
Paper Details
Title
Learning from data to design functional materials without inversion symmetry
Published Date
Feb 17, 2017
Volume
8
Issue
1
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