Uni2D: A Universal Machine Learning Interatomic Potential for Two-Dimensional Materials

TL;DR

Uni2D is a comprehensive machine learning interatomic potential specifically designed for 2D materials, enabling accurate, efficient, and high-throughput simulations across a wide chemical space.

Contribution

The paper introduces Uni2D, a novel ML-based interatomic potential trained on extensive 2D material data, with integrated LLM-powered automation for materials discovery.

Findings

Reliable predictions for energies, forces, and stresses in 2D materials.

Accurate structural relaxation and molecular dynamics simulations.

Effective high-throughput screening and property trend analysis.

Abstract

Accurate interatomic potentials (IAPs) are essential for modeling the potential energy surfaces (PES) that govern atomic interactions in materials. However, most existing IAPs are developed for bulk materials and often struggle to accurately and efficiently capture the diverse chemical environments of two-dimensional (2D) materials, which limits large-scale simulation and design of emerging 2D systems. To address this challenge, we develop Uni2D, an interatomic potential tailored for 2D materials. The Uni2D model is trained on a dataset comprising approximately 327,000 structure-energy-force-stress mappings derived from about 20,000 distinct 2D materials, covering 89 chemical elements. The model demonstrates reliable predictive performance for energies, forces, and stresses, and demonstrates quantitatively robust accuracy in tasks such as structural relaxation, equation-of-state calculations, and molecular dynamics simulations, making the model suitable for high-throughput screening of 2D materials. For derived properties, including elastic properties, lattice dynamics, and other screening-related metrics, the model provides qualitative to semi-quantitative predictions that remain useful for trend analysis and preliminary evaluation. To enhance usability, we further introduce an intelligent agent powered by a large language model (LLM), enabling automated workflows and natural language interaction for 2D materials simulations. Our work provides an efficient and accessible framework for high-throughput screening and computational exploration of 2D materials.