标题： 建模复杂的质子传输现象——探索基础机器学习力场的微调和可迁移性极限 显示英文标题

标题： Modelling complex proton transport phenomena -- Exploring the limits of fine-tuning and transferability of foundational machine-learned force fields

摘要： 固态酸 CsH$_2$PO$_4$和 Cs$_7$(H$_4$PO$_4$)(H$_2$PO$_4$)$_8$在质子传输现象的模拟中面临重大挑战。 在本工作中，我们使用最近开发的机器学习力场（MLFF）MACE，在纳秒时间尺度上对这些系统进行质子动力学建模，并将其性能与长期从头算分子动力学（AIMD）模拟进行比较。 MACE-MP-0 基础模型在所有从分子动力学（MD）模拟中得出的可观测量上表现出色，但与 AIMD 参考数据相比仍存在微小的定量差异。 然而，我们表明，只需最小的微调——仅拟合最多 1 ps 的 AIMD 数据——就能使 MACE 力场的径向分布函数与 AIMD 模拟达到完全的定量一致。 此外，我们表明传统的长期 AIMD 模拟由于可访问的时间尺度有限，无法捕捉这些固体酸中扩散系数和活化能的正确定性趋势。 相反，通过使用机器学习力场进行多纳秒长时间的 MD 模拟，可以可靠地获得准确且收敛的扩散系数。 现在，所获得的收敛扩散系数和活化能的定性和定量行为与两种固体酸的实验趋势相匹配，这与之前 AIMD 模拟得出的定性错误图像形成对比。 显示英文摘要

摘要： The solid acids CsH$_2$PO$_4$ and Cs$_7$(H$_4$PO$_4$)(H$_2$PO$_4$)$_8$ pose significant challenges for the simulation of proton transport phenomena. In this work, we use the recently developed machine-learned force field (MLFF) MACE to model the proton dynamics on nanosecond time scales for these systems and compare its performance with long-term ab initio molecular dynamics (AIMD) simulations. The MACE-MP-0 foundation model shows remarkable performance for all observables derived from molecular dynamics (MD) simulations, but minor quantitative discrepancies remain compared to the AIMD reference data. However, we show that minimal fine-tuning -- fitting to as little as 1 ps of AIMD data -- leads to full quantitative agreement between the radial distribution functions of MACE force field and AIMD simulations. In addition, we show that traditional long-term AIMD simulations fail to capture the correct qualitative trends in diffusion coefficients and activation energies for these solid acids due to the limited accessible time scale. In contrast, accurate and convergent diffusion coefficients can be reliably obtained through multi-nanosecond long MD simulations using machine-learned force fields. The obtained qualitative and quantitative behavior of the converged diffusion coefficients and activation energies now matches the experimental trends for both solid acids, in contrast to previous AIMD simulations that yielded a qualitatively wrong picture.