LAMBench: A Benchmark for Large Atomistic Models

TL;DR

LAMBench is a new benchmarking system that evaluates large atomistic models (LAMs) on their generalizability and applicability, revealing gaps in current models and guiding future improvements for scientific discovery.

Contribution

This paper introduces LAMBench, the first comprehensive benchmark for assessing the performance and universality of large atomistic models across diverse scientific contexts.

Findings

Current LAMs show significant gaps compared to the ideal universal potential energy surface.

Incorporating cross-domain training data improves model generalizability.

Supporting multi-fidelity modeling and ensuring model conservativeness enhances robustness.

Abstract

Large Atomistic Models (LAMs) have undergone remarkable progress recently, emerging as universal or fundamental representations of the potential energy surface defined by the first-principles calculations of atomistic systems. However, our understanding of the extent to which these models achieve true universality, as well as their comparative performance across different models, remains limited. This gap is largely due to the lack of comprehensive benchmarks capable of evaluating the effectiveness of LAMs as approximations to the universal potential energy surface. In this study, we introduce LAMBench, a benchmarking system designed to evaluate LAMs in terms of their generalizability, adaptability, and applicability. These attributes are crucial for deploying LAMs as ready-to-use tools across a diverse array of scientific discovery contexts. We benchmark ten state-of-the-art LAMs released prior to August 1, 2025, using LAMBench. Our findings reveal a significant gap between the current LAMs and the ideal universal potential energy surface. They also highlight the need for incorporating cross-domain training data, supporting multi-fidelity modeling, and ensuring the models' conservativeness and differentiability. As a dynamic and extensible platform, LAMBench is intended to continuously evolve, thereby facilitating the development of robust and generalizable LAMs capable of significantly advancing scientific research. The LAMBench code is open-sourced at https://github.com/deepmodeling/lambench, and an interactive leaderboard is available at https://www.aissquare.com/openlam?tab=Benchmark.