Year: 2024
Type: preprint
Abstract: Crystal structures are characterized by atomic bases within a primitive unit cell that repeats along a regular lattice throughout 3D space. The periodic and infinite nature of crystals poses unique challenges for geometric graph representation learning. Specifically, constructing graphs that effectively capture the complete geometric information of crystals and handle chiral crystals remains an unsolved and challenging problem. In this paper, we introduce a novel approach that utilizes the periodic patterns of unit cells to establish the lattice-based representation for each atom, enabling efficient and expressive graph representations of crystals. Furthermore, we propose ComFormer, a SE(3) transformer designed specifically for crystalline materials. ComFormer includes two variants; namely, iComFormer that employs invariant geometric descriptors of Euclidean distances and angles, and eComFormer that utilizes equivariant vector representations. Experimental results demonstrate the state-of-the-art predictive accuracy of ComFormer variants on various tasks across three widely-used crystal benchmarks. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS).
Source: arXiv (Cornell University)
Institution: Texas A&M University
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
Awards: III: Small: 3D Graph Neural Networks: Completeness, Efficiency, and Applications, G2113400439, DMREF: AI-Guided Accelerated Discovery of Multi-Principal Element Multi-Functional Alloys, RECONFIGURABLE ELECTRONIC MATERIALS INSPIRED BY NONLINEAR NEURON DYNAMICS (REMIND), G3813305316, CAREER: Knowledge-driven Analytics, Model Uncertainty, and Experiment Design, FMRG: Cyber: Manufacturing USA: Material-on-demand manufacturing through convergence of manufacturing, AI and materials science, Collaborative Research: III: Medium: Conditional Transport: Theory, Methods, Computation, and Applications
OpenAlex ID: W4392971574