arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2201.05668cond-mat.mtrl-sci
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加速寻找新的铁电材料

Accelerated search for new ferroelectric materials

Ramon Frey, Bastien F. Grosso, Pascal Fandré, Benjamin Mächler, Nicola A. Spaldin, Aria Mansouri Tehrani

摘要:

我们报告了一个结合机器学习和高通量密度泛函理论(DFT)框架的发展,以加速寻找新的铁电材料。该框架可以预测潜在的铁电化合物,仅使用元素组成作为输入。一系列机器学习算法最初预测在给定的化学成分空间内,具有铁电性所必需的极性晶体结构的可能的稳定和绝缘化学计量。然后,分类模型预测这些化学计量的点群。随后的一系列高通量DFT计算发现点群中能量最低的晶体结构。作为最后一步,使用群论考虑确定非极性母体结构,并使用DFT计算自发极化的值。通过预测晶体结构和极化值,该方法提供了一个强有力的工具,以探索新的铁电材料超出了现有的数据库。

英文摘要:

We report the development of a combined machine-learning and high-throughput density functional theory (DFT) framework to accelerate the search for new ferroelectric materials. The framework can predict potential ferroelectric compounds using only elemental composition as input. A series of machine-learning algorithms initially predict the possible stable and insulating stoichiometries with a polar crystal structure, necessary for ferroelectricity, within a given chemical composition space. A classification model then predicts the point groups of these stoichiometries. A subsequent series of high-throughput DFT calculations finds the lowest energy crystal structure within the point group. As a final step, non-polar parent structures are identified using group theory considerations, and the values of the spontaneous polarization are calculated using DFT. By predicting the crystal structures and the polarization values, this method provides a powerful tool to explore new ferroelectric materials beyond those in existing databases.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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