No abstract

In computational material sciences, Machine Learning (ML) techniques are now competitive alternatives that can be used in determining target properties conventionally resolved by ab initio quantum mechanical simulations or experimental synthesization. The successes realized with ML-based techniques often rely on the quality of the design architecture, in addition to the descriptors used in representing a chemical compound with good target mapping property. With the perovskite crystal structure at the forefront of modern energy materials discovery, accurately estimating related target properties is even of high importance due to the role such properties may have in defining the functionalization. As a result, the present study proposes a new feature engineering approach that takes advantage of both the direct ionic features and the periodic Fourier transformed reciprocal features of a three-dimensional perovskite polyhedral. The study is conducted on about 27,000 ABX3 perovskite structures with the stability energy, the formation energy, and the energy bandgap as targets. For accurate modeling, a feature-extracting two-dimensional convolutional neural network (Conv2D) is coupled with a prediction-enhancing Support Vector Machine (SVM) to form a hybridized Conv2D-SVM architecture. A comparison with previous benchmark evaluations reveals appreciable improvements in modeling accuracy for all target properties, particularly for the energy bandgap, for which the feature extraction approach yields 0.105 eV MAE, 0.301 eV RMSE, and 93.48% R2. Besides, the proposed design is further demonstrated to out-perform other similar periodic feature engineering approaches in the Coulomb matrix, Ewald-sum matrix, and Sine matrix, all in their absolute eigenvalue forms. All preprocessed data, source codes, and relevant sample calculations are openly available at: github.com/chenebuah/high_dim_descriptor

At the forefront of discoverable materials are perovskites that stand out as some of the most chemically diverse and multifunctional energy materials. Theoretically, the estimated number of ternary perovskites exceeds a hundred thousand distinct compounds, notwithstanding that only a small fraction of this estimate has been reported in existing crystal databases. Therefore, the study takes advantage of the reliable, inexpensive and rapid opportunity offered by deep generative modeling for accelerating the search for unknown perovskites. In the process of making such findings, an inverse design-modeling scheme is resolved, which aims at assimilating deterministic target properties with their corresponding perovskite structure. The inverse design pipeline is architectured by combining a generative Variational AutoEncoder (VAE) model with Target-Learning (TL) feed-forward neural networks to form the TL-VAE perovskite generator, thereby making the complete modeling process semi-supervisory. The TL feed-forward neural network model serves the purpose of organizing the non-linear latent space of the VAE model and further assists in isolating deterministic target properties that are of interest to the core objective of the study. The property to be target-learned in the latent space is the formation energy, which is a crucial indicator for calibrating perovskite stability. The results report the discovery of promising new perovskite candidates, which are unique and polymorphic material variants. Upon conducting Density Functional Theory (DFT) validation on the newly identified perovskites, candidates that undergo full geometrical relaxation are recommended for further investigation and/or synthesization. In conclusion, the study demonstrates the efficacy of the inverse design TL-VAE model for the generation of stable ternary perovskites.