Shiyin Ma

Inverse design of semiconductor materials with deep generative models In the realm of materials design, effectively navigating the expansive chemical design space to discover materials with specific desired properties remains a formidable challenge. Here, we introduce an inverse design framework to generate thermodynamically stable semiconductor materials, utilizing existing data on decomposition enthalpies, synthesizability information, and band gaps.

Inverse design of experimentally synthesizable crystal structures by leveraging computational and experimental data Crystal structure prediction (CSP) drives the discovery and design of innovative materials. However, existing CSP methods rely heavily on formation enthalpies calculated by density functional theory (DFT) and ignore the differences between DFT and experimental values, resulting in predicted structures that may be limited in experimental synthesis.