Abstract
As one of the most studied materials, perovskites exhibit a wealth of superior properties that lead to diverse applications. Computational prediction of novel stable perovskite structures has big potential in the discovery of new materials for solar panels, superconductors, thermal electric, and catalytic materials, etc. By addressing one of the key obstacles of machine learning based materials discovery, the lack of sufficient training data, this paper proposes a transfer learning based approach that exploits the high accuracy of the machine learning model trained with physics-informed structural and elemental descriptors. This gradient boosting regressor model (the transfer learning model) allows us to predict the formation energy with sufficient precision of a large number of materials of which only the structural information is available. The enlarged training set is then used to train a convolutional neural network model (the screening model) with the generic Magpie elemental features with high prediction power. Extensive experiments demonstrate the superior performance of our transfer learning model and screening model compared to the baseline models. We then applied the screening model to filter out promising new perovskite materials out of 21,316 hypothetical perovskite structures with a large portion of them confirmed by existing literature.
Highlights
The perovskite structure is one of the most common and widely studied structures in materials science
We developed a gradient boosting regressor (GBR) machine learning (ML) model trained with structural and elemental features for perovskite formation energy prediction, which outperforms the state-of-the-art artificial neural network (ANN) based model trained with two elemental descriptors
To evaluate the performance of GBR, we evaluated several mainstream machine learning algorithms as the baselines, including random forest regression (RFR), support vector regression (SVR), and least absolute shrinkage and selection operator (Lasso) with the same dataset using the same set of features
Summary
The perovskite structure is one of the most common and widely studied structures in materials science. We proposed a transfer learning strategy to convert formation energy related structural features/insights into training data for a perovskite screening model using only elemental Magpie features This enables us to address the small dataset issue in typical ML based materials discovery. We developed a gradient boosting regressor (GBR) ML model trained with structural and elemental features for perovskite formation energy prediction, which outperforms the state-of-the-art artificial neural network (ANN) based model trained with two elemental descriptors This highly accurate model allows us to annotate the large number of material samples with structural information but no formation energy.
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