Generative Inverse Design of Cold Metals for Low-Power Electronics
Cold metals are a class of metals with an intrinsic energy gap located close to the Fermi level, which enables cold-carrier injection for steep-slope transistors and is therefore promising for low-power electronic applications. High-throughput screening has revealed 252 three-dimensional (3D) cold metals in the Materials Project database, but database searches are inherently limited to known compounds. Here we present an inverse-design workflow that generates 3D cold metals using MatterGPT, a conditional autoregressive Transformer trained on SLICES, an invertible and symmetry-invariant crystal string representation. We curate a training set of 26,309 metallic structures labeled with energ...
| Authors | Kedeng Wu, Yucheng Zhu, Yan Chen et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 14 March 2026 |
| Last updated at source | 14 March 2026 |
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