发表机构
National University of Singapore（新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种从分子动力学轨迹学习从头算相场模型的框架，通过神经网络参数化非局域自由能和迁移率，在铁-硼及氢-氦体系中验证了其精度与尺度优势。
AI 中文摘要
模拟微观结构演化需要量子力学精度和介观尺度上的长度与时间跨度，这一组合目前尚无方法能够实现。经典相场模型提供了这种跨度，但其精度受限于唯象自由能和迁移率。在此，我们开发了一个学习从头算相场模型的框架，其中介观方程并非假设得出，而是通过将分子动力学在显式假设下对物种密度场进行Mori-Zwanzig投影而推导出来。该方程未指定的非局域自由能和迁移率由神经网络参数化，并从使用从头算精度的机器学习原子间势生成的短分子动力学轨迹中学习。我们在铁-硼熔体以及行星条件下的氢-氦混合物上展示了该框架。对于铁-硼，模型表明FeB$_4$成分的熔体在环境压力下是旋节线不稳定的，但在10 GPa下稳定，这为FeB$_4$仅在高压下合成提供了热力学解释。对于氢-氦，模型预测了不混溶边界，并在对应220万原子的氦雨模拟中捕捉了液滴成核和生长，远超同等精度下原子级建模的规模。通过在不同成分和条件下训练，此类模型可以提供从头算分子动力学的介观对应物。
英文摘要
Simulating microstructure evolution requires quantum-mechanical accuracy and mesoscopic reach in length and time scales, a combination that no current method achieves. Classical phase-field models provide this reach, but their accuracy is limited by phenomenological free energies and mobilities. Here we develop a framework for learning ab initio phase-field models, where the mesoscopic equation is not postulated but derived from a Mori-Zwanzig projection of molecular dynamics onto species-density fields under explicit assumptions. The nonlocal free energy and mobility left unspecified by this equation are parametrized by neural networks and learned from short molecular dynamics trajectories generated with machine-learning interatomic potentials of ab initio accuracy. We demonstrate the framework on an iron-boron melt and on hydrogen-helium mixtures under planetary conditions. For iron-boron, the model shows that the melt at the FeB$_4$ composition is spinodally unstable at ambient pressure but stabilized at 10 GPa, offering a thermodynamic rationale for why FeB$_4$ has been synthesized only under high pressure. For hydrogen-helium, the model predicts the immiscibility boundary and captures droplet nucleation and growth in helium-rain simulations of a column corresponding to 2.2 million atoms, far beyond the scale of atomistic modeling at comparable accuracy. Trained across compositions and conditions, such models could provide a mesoscopic counterpart to ab initio molecular dynamics.
