用可微分模拟优化预训练势能模型，显著提升结构弛豫精度。
On The Finetuning of MLIPs Through the Lens of Iterated Maps With BPTT
- 构建端到端可微模拟回路，直接优化最终结构预测。
- 平均预测误差降低约32%，在多个模型上均有效。
- 对弛豫参数变化不敏感，鲁棒性强，适合实际应用。
精确的结构弛豫对先进材料设计至关重要。传统基于物理第一性原理的计算方法计算成本高昂，推动了机器学习原子间势能（MLIPs）的发展，旨在准确复现第一性原理计算的受力。本文提出一种针对预训练MLIP的微调方法，构建一个全可微的端到端模拟回路，直接优化预测的最终结构。通过展开轨迹并追踪整个弛豫过程的梯度，我们发现该方法在所有评估的预训练模型上均持续提升性能，平均预测误差减少约32%。有趣的是，该过程对弛豫设置的大幅变化具有鲁棒性，在不同超参数和流程修改下结果差异可忽略。此方法适用于多种材料体系与模型架构。
原文摘要 · Abstract (English)
Accurate structural relaxation is critical for advanced materials design. Traditional approaches built on physics-derived first-principles calculations are computationally expensive, motivating the creation of machine-learning interatomic potentials (MLIPs), which strive to faithfully reproduce first-principles computed forces. We propose a fine-tuning method to be used on a pretrained MLIP in which we create a fully-differentiable end-to-end simulation loop that optimizes the predicted final structures directly. Trajectories are unrolled and gradients are tracked through the entire relaxation. We show that this method consistently improves performance across all evaluated pretrained models; resulting in an average of roughly 32% reduction in prediction error. Interestingly, we show the process is robust to substantial variation in the relaxation setup, achieving negligibly different results across varied hyperparameter and procedural modifications.
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