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Molecular dynamics (MD) simulation techniques are widely used for variousnatural science applications. Increasingly, machine learning (ML) force field(FF) models begin to replace ab-initio simulations by predicting forcesdirectly from atomic structures. Despite significant progress in this area,such techniques are primarily benchmarked by their force/energy predictionerrors, even though the practical use case would be to produce realistic MDtrajectories. We aim to fill this gap by introducing a novel benchmark suitefor learned MD simulation. We curate representative MD systems, includingwater, organic molecules, a peptide, and materials, and design evaluationmetrics corresponding to the scientific objectives of respective systems. Webenchmark a collection of state-of-the-art (SOTA) ML FF models and illustrate,in particular, how the commonly benchmarked force accuracy is not well alignedwith relevant simulation metrics. We demonstrate when and how selected SOTAmethods fail, along with offering directions for further improvement.Specifically, we identify stability as a key metric for ML models to improve.Our benchmark suite comes with a comprehensive open-source codebase fortraining and simulation with ML FFs to facilitate future work.

Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations