AMShortcut: An Inference- and Training-Efficient Inverse Design Model for Amorphous Materials

Abstract

Amorphous materials are solids that lack long-range atomic order but possess complex short- and medium-range order. Unlike crystalline materials that can be described by unit cells containing few up to hundreds of atoms, amorphous materials require larger simulation cells with at least hundreds or often thousands of atoms. Inverse design of amorphous materials with probabilistic generative models aims to generate the atomic positions and elements of amorphous materials given a set of 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 AMShortcut, an inference- and training-efficient probabilistic generative model for amorphous materials. AMShortcut 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. AMShortcut 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 three amorphous materials datasets with diverse structures and properties demonstrate that AMShortcut achieves its design goals.

Figures (6)

• Figure 1: AMShortcut generates structurally accurate amorphous material samples with one or few sampling steps, enabling high-throughput inverse design of amorphous materials.
• Figure 2: Distribution comparison of properties in the training samples versus the samples generated by AMShortcut with 100 steps. The target properties extend beyond the training distribution, demonstrating the model's extrapolation capability.
• Figure 3: Structural accuracy evaluation for (a) RDF and (b) ADF of different models on the a-Si dataset with different numbers of sampling steps. ODE, SDE, and Shortcut corresponds to Material ODE, Material SDE, and AMShortcut, respectively.
• Figure 4: RMSD of (a) RDF and (b) ADF versus generation time per 10,000 a-Si samples across different models and step counts. Labels indicate the number of sampling steps for each run.
• Figure 5: Property MAPE versus generation time per 2,000 a-SiO2 samples across different models and step counts for (a) shear modulus and (b) ring size distribution. Labels indicate the number of sampling steps for each run.

Theorems & Definitions (2)

• Definition 1: Amorphous material sample
• Definition 2: Ghost atoms