Title: Shortcuts in Material Design: Efficient Generative Modeling of Amorphous Materials
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: amorphous materials, inverse design, generative model

Abstract: Amorphous materials, such as glasses, are solids that lack long-range atomic order but possess complex short- and medium-range order. Inverse design of amorphous materials with probabilistic generative models aims to generate the atomic positions and elements of amorphous materials given desired properties. It has emerged as a promising approach for facilitating the application of amorphous materials in domains such as energy storage and thermal management. In this paper, we introduce MDShortcut, an inference- and training-efficient probabilistic generative model for amorphous materials. MDShortcut enables accurate inference of diverse short- and medium-range structures in amorphous materials with only a few sampling steps, mitigating the need for an excessive number of sampling steps that hinders inference efficiency. MDShortcut can be trained once with all relevant properties and perform inference conditioned on arbitrary combinations of desired properties, mitigating the need for training one model for each combination. Experiments on two amorphous materials datasets with diverse structures and properties demonstrate that MDShortcut achieves its design goals.