论文标题
ABO3 Perovskites使用机器学习的表现性预测和晶体结构分类
ABO3 Perovskites' Formability Prediction and Crystal Structure Classification using Machine Learning
论文作者
论文摘要
可再生能源对打击全球变暖具有极大的兴趣，但是像光伏（PV）细胞这样有希望的来源效率不高，便宜，无法作为传统能源的替代品。钙钛矿作为PV材料具有很高的潜力，但是为特定应用设计正确的材料通常是一个漫长的过程。在本文中，可以预测ABO3型Perovskites的表现性，并使用机器学习精确地将其晶体结构分类，从而提供快速筛选过程。尽管研究是在太阳能电池应用中进行的，但预测框架的通用框架足以用于其他目的。预测钙钛矿的形成性，其晶体结构的精度分别为98.57％和90.53％，分别在5倍交叉验证后使用随机森林。我们的机器学习模型可以通过提供快速的机制来洞悉材料的特性，从而有助于加速所需的钙钛矿结构。
Renewable energy sources are of great interest to combat global warming, yet promising sources like photovoltaic (PV) cells are not efficient and cheap enough to act as an alternative to traditional energy sources. Perovskite has high potential as a PV material but engineering the right material for a specific application is often a lengthy process. In this paper, ABO3 type perovskites' formability is predicted and its crystal structure is classified using machine learning with high accuracy, which provides a fast screening process. Although the study was done with solar-cell application in mind, the prediction framework is generic enough to be used for other purposes. Formability of perovskite is predicted and its crystal structure is classified with an accuracy of 98.57% and 90.53% respectively using Random Forest after 5-fold cross-validation. Our machine learning model may aid in the accelerated development of a desired perovskite structure by providing a quick mechanism to get insight into the material's properties in advance.
