通过声子微调提升机器学习势能的二阶导数精度，改善振动性质预测。
PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials
- 用密度泛函计算的力常数监督机器学习势能的海森矩阵，直接优化二阶导数。
- 在MDR声子基准上，对热力学性质平均提升55%，超越现有模型。
- 适用于需高阶导数的材料性质预测，如热导率，适合材料模拟研究者。
许多材料性质依赖于势能面的高阶导数，但以能量、力和应力误差为标准损失训练的机器学习原子间势（MLIPs）在曲率预测上存在误差，影响振动性质的准确性。本文提出声子微调（PFT），通过匹配MLIP能量海森矩阵与基于有限位移声子计算的密度泛函理论（DFT）力常数，直接监督材料的二阶力常数。为适应大超胞计算，PFT随机采样海森矩阵列，并通过单次海森-向量乘积计算损失。同时采用简单共训练策略引入上游数据，缓解灾难性遗忘。在MDR声子基准测试中，PFT使Nequix MP平均提升55%的声子热力学性质预测性能，且在仅使用Materials Project轨迹训练的模型中达到当前最优水平。此外，PFT具有泛化能力，可提升依赖三阶导数的热导率预测精度。
原文摘要 · Abstract (English)
Many materials properties depend on higher-order derivatives of the potential energy surface, yet machine learned interatomic potentials (MLIPs) trained with a standard loss on energy, force, and stress errors can exhibit error in curvature, degrading the prediction of vibrational properties. We introduce phonon fine-tuning (PFT), which directly supervises second-order force constants of materials by matching MLIP energy Hessians to DFT-computed force constants from finite displacement phonon calculations. To scale to large supercells, PFT stochastically samples Hessian columns and computes the loss with a single Hessian-vector product. We also use a simple co-training scheme to incorporate upstream data to mitigate catastrophic forgetting. On the MDR Phonon benchmark, PFT improves Nequix MP by 55% on average across phonon thermodynamic properties and achieves state-of-the-art accuracy among models trained on Materials Project trajectories. PFT also generalizes to improve properties beyond second-derivatives, improving thermal conductivity predictions that rely on third-order derivatives of the potential energy.
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