AI-Assisted Rapid Crystal Structure Generation Towards a Target Local Environment

TL;DR

This paper introduces LEGO-xtal, an AI-based method that rapidly generates and optimizes crystal structures by incorporating symmetry and local environment information, significantly expanding known low-energy carbon allotropes.

Contribution

The paper presents a novel symmetry-informed AI generative model for crystal structures that overcomes previous limitations in handling periodicity and size, enabling rapid exploration of diverse materials.

Findings

Expanded from 25 to over 1,700 low-energy carbon allotropes

Generated structures within 0.5 eV/atom of graphite

Demonstrated applicability to modular material design

Abstract

In the field of material design, traditional crystal structure prediction approaches require extensive structural sampling through computationally expensive energy minimization methods using either force fields or quantum mechanical simulations. While emerging artificial intelligence (AI) generative models have shown great promise in generating realistic crystal structures more rapidly, most existing models fail to account for the unique symmetries and periodicity of crystalline materials, and they are limited to handling structures with only a few tens of atoms per unit cell. Here, we present a symmetry-informed AI generative approach called Local Environment Geometry-Oriented Crystal Generator (LEGO-xtal) that overcomes these limitations. Our method generates initial structures using AI models trained on an augmented small dataset, and then optimizes them using machine learning structure descriptors rather than traditional energy-based optimization. We demonstrate the effectiveness of LEGO-xtal by expanding from 25 known low-energy sp2 carbon allotropes to over 1,700, all within 0.5 eV/atom of the ground-state energy of graphite. This framework offers a generalizable strategy for the targeted design of materials with modular building blocks, such as metal-organic frameworks and next-generation battery materials.