Foundational Machine Learning Potentials can resolve the accuracy and transferability limitations of classical force fields. They enable microscopic insights into material behavior through Molecular Dynamics simulations, which can crucially expedite material design and discovery. However, insufficiently broad and systematically biased reference data affect the predictive quality of the learned models. Often, these models exhibit significant deviations from experimentally observed phase transition temperatures, in the order of several hundred kelvins. Thus, fine-tuning is necessary to achieve adequate accuracy in many practical problems. This work proposes a fine-tuning strategy via top-down learning, directly correcting the wrongly predicted transition temperatures to match the experimental reference data. Our approach leverages the Differentiable Trajectory Reweighting algorithm to minimize the free energy differences between phases at the experimental target pressures and temperatures. We demonstrate that our approach can accurately correct the phase diagram of pure Titanium in a pressure range of up to 5 GPa, matching the experimental reference within tenths of kelvins and improving the liquid-state diffusion constant. Our approach is model-agnostic, applicable to multi-component systems with solid-solid and solid-liquid transitions, and compliant with top-down training on other experimental properties. Therefore, our approach can serve as an essential step towards highly accurate application-specific and foundational machine learning potentials.

翻译：基础性机器学习势函数能够克服经典力场在精度与可迁移性方面的局限。它们通过分子动力学模拟实现对材料行为的微观洞察，从而显著加速材料设计与发现进程。然而，参考数据覆盖范围不足及系统性偏差会影响学习模型的预测质量。这类模型常出现与实验观测相变温度高达数百开尔文的显著偏差。因此，在许多实际应用中需通过微调以获得足够的精度。本研究提出一种基于自上而下学习的微调策略，直接修正错误预测的相变温度以匹配实验参考数据。该方法利用可微分轨迹重加权算法，在实验目标压力与温度下最小化各相之间的自由能差。我们证明该方法能在高达5 GPa的压力范围内精确修正纯钛的相图，与实验参考值的偏差控制在十分之几开尔文内，并改善了液态扩散常数。本方法具有模型无关性，适用于存在固-固及固-液相变的多组分体系，且兼容其他实验性质的自上而下训练。因此，该方法可作为实现高精度专用型与基础性机器学习势函数的关键步骤。