Vital to the creation of advanced materials is performing structural relaxations. Traditional approaches built on physics-derived first-principles calculations are computationally expensive, motivating the creation of machine-learning interatomic potentials (MLIPs). Traditional approaches to training MLIPs for structural relaxations involves training models 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 achieves substantial performance gains when applied to pretrained models, leading to a nearly $50\%$ reduction in test error across the sample datasets. Interestingly, we show the process is robust to substantial variation in the relaxation setup, achieving negligibly different results across varied hyperparameter and procedural modifications. Experimental results indicate this is due to a ``preference'' of BPTT to modify the MLIP rather than the other trainable parameters. Of particular interest to practitioners is that this approach lowers the data requirements for producing an effective domain-specific MLIP, addressing a common bottleneck in practical deployment.

翻译：结构弛豫计算是先进材料研发的关键环节。传统基于物理第一性原理计算的方法计算成本高昂，这推动了机器学习原子间势能函数（MLIPs）的发展。传统MLIPs结构弛豫训练方法主要聚焦于精确复现第一性原理计算得到的原子力。本文提出一种针对预训练MLIPs的微调方法：构建完全可微分的端到端模拟循环，直接优化预测的最终结构。该方法通过展开弛豫轨迹并全程追踪梯度，在预训练模型上实现了显著的性能提升，在样本数据集上测试误差降低近50%。值得注意的是，该方法对弛豫设置的显著变化具有鲁棒性，在不同超参数和流程调整下结果差异可忽略。实验结果表明，反向传播通过时间（BPTT）算法更倾向于修正MLIPs而非其他可训练参数。对于实际应用者而言，该方法降低了构建有效领域专用MLIPs的数据需求，解决了实际部署中的常见瓶颈问题。