Vector Field Oriented Diffusion Model for Crystal Material Generation

TL;DR

This paper introduces a novel probabilistic diffusion model with a geometrically equivariant GNN for generating crystal structures, improving upon existing methods by jointly considering atomic positions and lattices.

Contribution

The paper presents a new diffusion model that incorporates a geometrically equivariant GNN and a novel evaluation metric for crystal material generation.

Findings

Model effectively generates plausible crystal structures.

New metric provides comprehensive evaluation of model performance.

Experiments demonstrate the model's ability to learn meaningful representations.

Abstract

Discovering crystal structures with specific chemical properties has become an increasingly important focus in material science. However, current models are limited in their ability to generate new crystal lattices, as they only consider atomic positions or chemical composition. To address this issue, we propose a probabilistic diffusion model that utilizes a geometrically equivariant GNN to consider atomic positions and crystal lattices jointly. To evaluate the effectiveness of our model, we introduce a new generation metric inspired by Frechet Inception Distance, but based on GNN energy prediction rather than InceptionV3 used in computer vision. In addition to commonly used metrics like validity, which assesses the plausibility of a structure, this new metric offers a more comprehensive evaluation of our model's capabilities. Our experiments on existing benchmarks show the significance of our diffusion model. We also show that our method can effectively learn meaningful representations.