Abstract
Perovskite materials are central to the fields of energy conversion and storage, especially for fuel cells. However, they are challenged by overcomplexity, coupled with a strong desire for new materials discovery at high speed and high precision. Herein, we propose a new approach involving a combination of extreme feature engineering and automated machine learning to adaptively learn the structure-composition-property relationships of perovskite oxide materials for energy conversion and storage. Structure-composition-property relationships between stability and other features of perovskites are investigated. Extreme feature engineering is used to construct a great quantity of fresh descriptors, and a crucial subset of 23 descriptors is acquired by sequential forward selection algorithm. The best descriptor for stability of perovskites is determined with linear regression. The results demonstrate a high-efficient and non-priori-knowledge investigation of structure-composition-property relationships for perovskite materials, providing a new road to discover advanced energy materials.
Highlights
To discover materials, the investigation of structure-composition-property relationship of inorganic materials is essential, and a huge number of material composition pose a big challenge to investigate the www.energymaterj.com Page 2RIDeng et al
The model here usually refers to a machine learning algorithm, called an objective function; Step 2: Evaluate the model with a validation dataset; Step 3: Perform the above operations on different feature subsets based on some search algorithm; Step 4: Based on the evaluation results, the best feature subset is selected
We discovered the quantitative relationships between various variables based on a collection of data, which resulted in the construction of a mathematical model and the estimation of unknown parameters
Summary
The investigation of structure-composition-property relationship of inorganic materials is essential, and a huge number of material composition pose a big challenge to investigate the www.energymaterj.com Page 2RIDeng et al. Several approaches for discovering important descriptors have been published, such as the symbolic regressionr algorithm[12], the least absolute shrinkage and selection operator algorithm algorithm[13], and the sure independence screening and sparsifying operator (SISSO) algorithm[14] The purpose of these approaches is to find some vital descriptors describing the target variables or some hidden mathematical formulas from the given feature space so that these vital descriptors can be used to predict the target variables[4,9,10]. These algorithms are extremely low efficient[14,15,16]
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