Pilania G, Balachandran P V, Gubernatis J E, Lookman T
Materials Science and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.
Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.
Acta Crystallogr B Struct Sci Cryst Eng Mater. 2015 Oct;71(Pt 5):507-13. doi: 10.1107/S2052520615013979. Epub 2015 Sep 19.
We explored the use of machine learning methods for classifying whether a particular ABO3 chemistry forms a perovskite or non-perovskite structured solid. Starting with three sets of feature pairs (the tolerance and octahedral factors, the A and B ionic radii relative to the radius of O, and the bond valence distances between the A and B ions from the O atoms), we used machine learning to create a hyper-dimensional partial dependency structure plot using all three feature pairs or any two of them. Doing so increased the accuracy of our predictions by 2-3 percentage points over using any one pair. We also included the Mendeleev numbers of the A and B atoms to this set of feature pairs. Doing this and using the capabilities of our machine learning algorithm, the gradient tree boosting classifier, enabled us to generate a new type of structure plot that has the simplicity of one based on using just the Mendeleev numbers, but with the added advantages of having a higher accuracy and providing a measure of likelihood of the predicted structure.
我们探索了使用机器学习方法来分类特定的ABO3化学物质是否形成钙钛矿结构或非钙钛矿结构的固体。从三组特征对（容忍度和八面体因子、A和B离子相对于O半径的离子半径以及A和B离子与O原子之间的键价距离）开始，我们使用机器学习来创建一个超维部分依赖结构图，使用所有三组特征对或其中任意两组。这样做比使用任何一对特征对将我们预测的准确率提高了2-3个百分点。我们还将A和B原子的门捷列夫数纳入了这组特征对。这样做并利用我们的机器学习算法梯度树提升分类器的能力，使我们能够生成一种新型的结构图，它具有仅基于门捷列夫数的结构图的简单性，但具有更高的准确性和提供预测结构可能性度量的额外优点。
