复杂质子传输现象建模——探索基础机器学习力场的微调和可迁移性极限
Modelling complex proton transport phenomena -- Exploring the limits of fine-tuning and transferability of foundational machine-learned force fields
AI总结：
本研究利用MACE力场模拟固体酸的质子传输，通过微调和多纳秒级MD模拟，实现了与实验数据一致的扩散系数和活化能结果。
摘要：
CsH₂PO₄和Cs₇(H₄PO₄)(H₂PO₄)₈这两种固体酸在模拟质子传输现象方面提出了重大挑战。在本工作中，我们使用最近开发的机器学习力场（MLFF）MACE来模拟这些系统的质子动力学，时间尺度达到纳秒级，并将其性能与长期的从头计算分子动力学（AIMD）模拟进行比较。MACE-MP-0基础模型在所有从分子动力学（MD）模拟中导出的可观测性上表现出显著的性能，但与AIMD参考数据相比仍存在微小的定量差异。然而，我们证明了最小的微调——仅拟合1皮秒的AIMD数据——就能使MACE力场的径向分布函数与AIMD模拟完全一致。此外，我们还证明传统长期AIMD模拟由于可访问的时间尺度有限，无法正确捕捉这些固体酸的扩散系数和活化能的定性趋势。相反，通过使用机器学习力场进行多纳秒级的MD模拟，可以可靠地获得准确且收敛的扩散系数。所获得的收敛扩散系数和活化能的定性和定量行为现在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。
英文摘要：
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.
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