Generative Inverse Design of Cold Metals for Low-Power Electronics

TL;DR

This paper introduces a generative inverse-design workflow using MatterGPT to discover novel 3D cold metals with potential for low-power electronics, expanding beyond known compounds through property-conditioned generation and high-throughput validation.

Contribution

The work presents a new inverse-design approach employing MatterGPT and SLICES representation to generate and validate novel cold metals with desired electronic properties.

Findings

Identified 257 novel cold metals with suitable Fermi level gaps.

Generated over 148,000 candidate structures with high reconstruction success.

Validated stability and work functions through first-principles calculations.

Abstract

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 energy above hull and a unified band-edge distance descriptor that merges p-type and n-type cold-metal characteristics to address severe label imbalance. Property-conditioned generation targeting thermodynamic stability and 50-500 meV band-edge distances produces 148,506 unique candidates; 92.1% are successfully reconstructed to 3D structures and down-selected by symmetry, uniqueness and novelty filters, followed by high-throughput DFT validation. We identify 257 cold metals verified as novel with respect to the Materials Project database, with gaps around the Fermi level spanning 50-500 meV. First-principles phonon, electronic-structure, and work-function calculations for representative candidates confirm dynamical stability and contact-relevant work functions. Our results demonstrate that SLICES-enabled generative transformers can expand the chemical space of cold metals beyond high-throughput screening, providing a route to low-power electronic materials discovery.