Electronic Structure Guided Inverse Design Using Generative Models

TL;DR

This paper presents DOSMatGen, a novel machine learning framework that generates crystal structures with specific electronic density of states, enabling targeted design of functional materials more efficiently than traditional methods.

Contribution

Introduces DOSMatGen, the first E(3)-equivariant diffusion model for inverse design of materials based on electronic density of states, with high flexibility and control.

Findings

Successfully generates stable materials matching desired electronic states

Achieves accurate conditioning on electronic density of states

Enables targeted and site-specific material design

Abstract

The electronic structure of a material fundamentally determines its underlying physical, and by extension, its functional properties. Consequently, the ability to identify or generate materials with desired electronic properties would enable the design of tailored functional materials. Traditional approaches relying on human intuition or exhaustive computational screening of known materials remain inefficient and resource-prohibitive for this task. Here, we introduce DOSMatGen, the first instance of a machine learning method which generates crystal structures that match a given desired electronic density of states. DOSMatGen is an E(3)-equivariant joint diffusion framework, and utilizes classifier-free guidance to accurately condition the generated materials on the density of states. Our experiments find this approach can successfully yield materials which are both stable and match closely with the desired density of states. Furthermore, this method is highly flexible and allows for finely controlled generation which can target specific templates or even individual sites within a material. This method enables a more physics-driven approach to designing new materials for applications including catalysts, photovoltaics, and superconductors.