Global instability index as a crystallographic stability descriptor of halide and chalcogenide perovskites

Crystallographic stability is an important factor that affects the stability of perovskites.The stability dictates the commercial applications of lead-based organometal halide perovskites.The tolerance factor(t)and octahedral factor(μ)form the state-of-the-art criteria used to evaluate the perovskite crystallographic stability.We studied the crystallographic stabilities of halide and chalcogenide perovskites by exploring an effective alternative descriptor,the global instability index(GII)that was used as an indicator of the stability of perovskite oxides.We particularly focused on determining crystallographic reliability by calculating GII.We analyzed the bond valence models of the 243 halide and chalcogenide perovskites that occupied the lowest-energy cubic-phase structures determined by conducting the first-principles-based total energy minimization calculations.The decomposition energy(ΔHD)reflects the thermodynamic stability of the system and is considered as the benchmark that helps assess the effectiveness of GII in evaluating the crystallographic stability of the systems under study.The results indicated that the accuracy of predicting thermodynamic stability was significantly higher when GII(73.6%)was analyzed compared to the cases when t(55%)andμ(39.1%)were analyzed to determine the stability.The results obtained from the machine learning-based data mining method further indicate that GII is an important descriptor of the stability of the perovskite family.

Evaluation of performance of machine learning methods in mining structure-property data of halide perovskite materials

With the rapid development of artificial intelligence and machine learning(ML)methods,materials science is rapidly entering the era of data-driven materials informatics.ML models serve as the most crucial component,closely bridging material structure and material properties.There is a considerable difference in the prediction performance of different ML methods for material systems.Herein,we evaluated three categories(linear,kernel,and nonlinear methods)of models,with twelve ML algorithms commonly used in the materials field.In addition,halide perovskite was chosen as an example to evaluate the fitting performance of different models.We constructed a total dataset of 540 halide perovskites and 72 features,with formation energy and bandgap as target properties.We found that different categories of ML models show similar trends for different target properties.Among them,the difference between the models is enormous for the formation energy,with the coefficient of determination(R2)range 0.69-0.953.The fitting performance between the models is closer for bandgap,with the R^(2)range 0.941-0.997.The nonlinear-ensemble model shows the best fitting performance for both the formation energy and the bandgap.It shows that the nonlinear-ensemble model,constructed by combining multiple weak learners,effectively describes the nonlinear relationship between material features and target property.In addition,the extreme gradient boosting decision tree model shows the most superior results among all the models and searches for two new descriptors that are crucial for formation energy and bandgap.Our work provides useful guidance for the selection of effective machine learning methods in the data-mining studies of specific material systems.

JAMIP:面向材料基因工程研究的功能材料设计开源软件

材料信息学或材料基因工程作为新兴的材料研究与设计范式,通过深度结合材料大数据与人工智能机器学习算法,正在加速新材料、新功能和新原则的创新发现.如何高效产生、收集、管理、学习和挖掘大规模材料数据是开展材料信息学或材料基因工程研究的关键.JAMIP(Jilin Artificial-intelligence-aided Materials-design Integrated Package)材料设计软件为满足这方面的研究需求而设计,涵盖半导体材料、介电材料、金属材料等材料体系,为基于功能材料大数据与机器学习算法结合的新材料发现和设计提供工具支撑.软件基于Python语言开发,代码开源,既可以基于结构原型数据库高效开展大规模高通量材料计算,也可以实现对计算任务更精细的控制及新任务流程的灵活定制.软件包主体框架包含以高通量材料计算为核心的数据产生、数据收集、管理工具及数据存储、机器学习/数据挖掘等功能模块.机器学习模块集成了数据预处理、数据特征工程,以及常用机器学习算法的模型构建和性能评估子模块.软件各模块之间高度融合,能够高效产生、分析、管理和学习计算材料大数据,为开展材料信息学或材料基因工程研究、实现新材料设计提供专业化的操作软件平台.