UniMatSim: A High-Throughput Materials Simulation Automation Framework Based on Universal Machine Learning Potentials

TL;DR

UniMatSim is a modular Python framework that unifies various UMLIPs, automates high-throughput material simulations, and enhances efficiency and reproducibility in materials discovery workflows.

Contribution

It introduces a standardized, flexible platform for integrating UMLIPs and automating complex simulation workflows, addressing ecosystem fragmentation.

Findings

Successfully screened 1,176 candidates to identify 59 Lieb-lattice structures.

Automated workflow reduces computational time and improves reproducibility.

Demonstrated effective integration of multiple UMLIPs in high-throughput screening.

Abstract

Universal machine learning interatomic potentials (UMLIPs) offer accuracy close to first-principles calculations at a fraction of the cost, showing significant potential for large-scale material simulations. However, the fragmented UMLIPs ecosystem lacks unified interface standards and integration frameworks, hindering their automated deployment in high-throughput workflows. To address this, we present UniMatSim, a modular Python framework. It systematically integrates various UMLIPs (e.g., CHGNet, M3GNet, MACE) and automates workflows from structural optimization to stability verification. The framework enables seamless model switching via abstracted interfaces, incorporates task orchestration, and provides standardized modules for key properties (elasticity, phonons, molecular dynamics), including automated handling for low-dimensional materials. As a test case, using the 2D Lieb lattice system, we constructed a multi-stage high-throughput screening workflow covering structural optimization, elastic stability, and phonon spectrum calculations. Starting from 1,176 candidate compositions, a four-model consensus pipeline yields 393 stable structures. These are refined by magnetic-state screening and DFT band-structure calculations to 59 Lieb-lattice candidates with staggered-magnetic-band characteristics. Results show UniMatSim significantly improves computational efficiency and reproducibility, providing a reliable infrastructure for data-driven materials discovery and design.