Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations

• URL: http://arxiv.org/abs/2506.14850v1
• Date: Tue, 17 Jun 2025 00:58:56 GMT
• Title: Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations
• Authors: Shagun Maheshwari, Janghoon Ock, Adeesh Kolluru, Amir Barati Farimani, John R. Kitchin,
• Abstract summary: Machine-learning force fields (MLFFs) have emerged as a promising solution for speeding up ab initio molecular dynamics (MD) simulations.<n>In this work, we employ GemNet-T, a graph neural network model, as an MLFF and investigate two training strategies.<n>We find that lower force errors do not necessarily guarantee stable MD simulations.
• Score: 5.913538953257869
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine-learning force fields (MLFFs) have emerged as a promising solution for speeding up ab initio molecular dynamics (MD) simulations, where accurate force predictions are critical but often computationally expensive. In this work, we employ GemNet-T, a graph neural network model, as an MLFF and investigate two training strategies: (1) direct training on MD17 (10K samples) without pre-training, and (2) pre-training on the large-scale OC20 dataset followed by fine-tuning on MD17 (10K). While both approaches achieve low force mean absolute errors (MAEs), reaching 5 meV/A per atom, we find that lower force errors do not necessarily guarantee stable MD simulations. Notably, the pre-trained GemNet-T model yields significantly improved simulation stability, sustaining trajectories up to three times longer than the model trained from scratch. These findings underscore the value of pre-training on large, diverse datasets to capture complex molecular interactions and highlight that force MAE alone is not always a sufficient metric of MD simulation stability.

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