Abstract
High-throughput virtual screening for crystals aims to discover new materials by evaluating the property of every virtual candidate in the database exhaustively. During this process, the major computational bottleneck is the costly structural relaxation of each hypothetical material on the large-scale dataset using density functional theory (DFT) calculations. Here, we present a generative domain translation framework that maps the unrelaxed structural domains to the relaxed domains, enabling data-driven structural translations. The model predicts the materials formation energy with a small mean absolute error without DFT relaxations, and furthermore can produce the atomic coordinates consistent with the DFT relaxed structures. The utility of the proposed concept is not restricted to the structural domains, and we expect that it can be extended to translate the domain of easy-to-compute properties into the domain of more difficult properties.
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