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Abstract Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventional approaches based on experiments and simulations are labor-intensive or costly with success heavily depending on experts’ heuristic knowledge. Here, we propose a deep learning based Physics Guided Crystal Generative Model (PGCGM) for efficient crystal material design with high structural diversity and symmetry. Our model increases the generation validity by more than 700% compared to FTCP, one of the latest structure generators and by more than 45% compared to our previous CubicGAN model. Density Functional Theory (DFT) calculations are used to validate the generated structures with 1869 materials out of 2000 are successfully optimized and deposited into the Carolina Materials Databasewww.carolinamatdb.org, of which 39.6% have negative formation energy and 5.3% have energy-above-hull less than 0.25 eV/atom, indicating their thermodynamic stability and potential synthesizability. 

Abstract Semiconductor device technology has greatly developed in complexity since discovering the bipolar transistor. In this work, we developed a computational pipeline to discover stable semiconductors by combining generative adversarial networks (GAN), classifiers, and high-throughput first-principles calculations. We used CubicGAN, a GAN-based algorithm for generating cubic materials and developed a classifier to screen the semiconductors and studied their stability using first principles. We found 12 stable AA$${}^{\prime}$$ ${}^{\prime }$MH₆semiconductors in the F-43m space group including BaNaRhH₆, BaSrZnH₆, BaCsAlH₆, SrTlIrH₆, KNaNiH₆, NaYRuH₆, CsKSiH₆, CaScMnH₆, YZnMnH₆, NaZrMnH₆, AgZrMnH₆, and ScZnMnH₆. Previous research reported that five AA$${}^{\prime}$$ ${}^{\prime }$IrH6 semiconductors with the same space group were synthesized. Our research shows that AA$${}^{\prime}$$ ${}^{\prime }$MnH₆and NaYRuH₆semiconductors have considerably different properties compared to the rest of the AA$${}^{\prime}$$ ${}^{\prime }$MH₆semiconductors. Based on the accurate hybrid functional calculations, AA$${}^{\prime}$$ ${}^{\prime }$MH₆semiconductors are found to be wide-bandgap semiconductors. Moreover, BaSrZnH₆and KNaNiH₆are direct-bandgap semiconductors, whereas others exhibit indirect bandgaps.