CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties

Abstract

In this work, we introduce CHIPS-FF (Computational High-Performance Infrastructure for Predictive Simulation-based Force Fields), a universal, open-source benchmarking platform for machine learning force fields (MLFFs). This platform provides robust evaluation beyond conventional metrics such as energy, focusing on complex properties including elastic constants, phonon spectra, defect formation energies, surface energies, and interfacial and amorphous phase properties. Utilizing 16 graph-based MLFF models including ALIGNN-FF, CHGNet, MatGL, MACE, SevenNet, ORB, MatterSim and OMat24, the CHIPS-FF workflow integrates the Atomic Simulation Environment (ASE) with JARVIS-Tools to facilitate automated high-throughput simulations. Our framework is tested on a set of 104 materials, including metals, semiconductors and insulators representative of those used in semiconductor components, with each MLFF evaluated for convergence, accuracy, and computational cost. Additionally, we evaluate the force-prediction accuracy of these models for close to 2 million atomic structures. By offering a streamlined, flexible benchmarking infrastructure, CHIPS-FF aims to guide the development and deployment of MLFFs for real-world semiconductor applications, bridging the gap between quantum mechanical simulations and large-scale device modeling.