Balachandran Prasanna V, Shearman Toby, Theiler James, Lookman Turab
Theoretical Divison, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
Program in Applied Mathematics, University of Arizona, Tucson, AZ 85721, USA.
Acta Crystallogr B Struct Sci Cryst Eng Mater. 2017 Oct 1;73(Pt 5):962-967. doi: 10.1107/S2052520617011945. Epub 2017 Sep 29.
In ferroelectric perovskites, displacements of cations from the high-symmetry lattice positions in the paraelectric phase break the spatial inversion symmetry. Furthermore, the relative magnitude of ionic displacements correlate strongly with ferroelectric properties such as the Curie temperature. As a result, there is interest in predicting the relative displacements of cations prior to experiments. Here, machine learning is used to predict the average displacement of octahedral cations from its high-symmetry position in ferroelectric perovskites. Published octahedral cation displacements data from density functional theory (DFT) calculations are used to train machine learning models, where each cation is represented by features such as Pauling electronegativity, Martynov-Batsanov electronegativity and the ratio of valence electron number to nominal charge. Average displacements for ten new octahedral cations for which DFT data do not exist are predicted. Predictions are validated by comparing them with new DFT calculations and existing experimental data. The outcome of this work has implications in the design and discovery of novel ferroelectric perovskites.
在铁电钙钛矿中，顺电相里阳离子从高对称晶格位置的位移会打破空间反演对称性。此外，离子位移的相对大小与诸如居里温度等铁电性能密切相关。因此，人们有兴趣在实验之前预测阳离子的相对位移。在此，机器学习被用于预测铁电钙钛矿中八面体阳离子从其高对称位置的平均位移。已发表的来自密度泛函理论（DFT）计算的八面体阳离子位移数据被用于训练机器学习模型，其中每个阳离子由诸如鲍林电负性、马尔季诺夫 - 巴萨诺夫电负性以及价电子数与标称电荷之比等特征来表示。预测了十个不存在DFT数据的新八面体阳离子的平均位移。通过将预测结果与新的DFT计算以及现有的实验数据进行比较来验证预测。这项工作的成果对新型铁电钙钛矿的设计和发现具有重要意义。
