arXiv：cs.LG（机器学习，全量分类）· Mengyi Chen, Peichen Zhong, Zihan Zhang, Qianxiao Li·· 7 小时前AI 评分41
从第一性原理学习相场模型：Mori-Zwanzig 投影结合神经网络
Learning ab initio phase-field models
AI 导读
研究者提出从第一性原理学习相场模型的框架，将介观方程由分子动力学经 Mori-Zwanzig 投影到物种密度场推导得出，而非假设，其中未定的非局域自由能与迁移率由神经网络参数化，并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训练。
正文
Abstract: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.
| Subjects: | Statistical Mechanics (cond-mat.stat-mech); Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.01432 [cond-mat.stat-mech] |
| (or arXiv:2610.01432v1 [cond-mat.stat-mech] for this version) | |
| https://doi.org/10.48550/arXiv.2610.01432 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Mengyi Chen [view email]
[v1]
Thu, 1 Oct 2026 10:30:37 UTC (20,336 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org