发表机构
Materials Genome Institute, Shanghai University; Department of Materials Science and Engineering, Southern University of Science and Technology（上海大学材料基因组研究院; 南方科技大学材料科学与工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述探讨物理驱动的专业化如何拓展机器学习原子间势（MLIPs）对复杂材料动力学系统的适用性，通过四类应用场景建立可观测量验证框架，提出开发物理自洽、硬件感知力场的路线图。
AI 中文摘要
机器学习原子间势（MLIPs）正通过触及前所未有的长度和时间尺度，变革原子模拟领域。尽管预训练的等变图神经网络在广泛化学空间的近平衡性质上实现了稳健的零样本性能，但其向复杂材料动力学的转化仍面临分布外反应态、表示偏差及计算缩放极限的根本性挑战。在本综述中，我们探究物理驱动的专业化如何拓展MLIPs对复杂动力学系统的适用性。我们系统评估MLIP设计中的结构权衡：高应变构型的欠采样、高阶消息传递架构的过高计算开销，以及开放系统和场耦合系统对非局域相互作用的必要性。通过四个严苛应用场景——带电界面、成分波动的开放系统、多相演化及大规模断裂——我们建立了针对可观测量的验证框架。我们强调通用基础模型与任务专用势的互补作用，指出针对性适配必须针对预期可观测量进行严格基准测试。最后，我们提出一条路线图，用于开发物理自洽、硬件感知的力场，实现从电子结构精度到宏观材料现象的无缝衔接。
英文摘要
Machine learning interatomic potentials (MLIPs) are transforming atomistic simulations by accessing unprecedented length and time scales. While pretrained equivariant graph neural networks achieve robust zero-shot performance for near-equilibrium properties across broad chemical spaces, their translation to complex materials dynamics remains fundamentally challenged by out-of-distribution reactive states, representation biases, and computational scaling limits. In this Review, we examine how physics-driven specialization extends the applicability of MLIPs to complex dynamical systems. We systematically evaluate the structural trade-offs in MLIP design: the undersampling of highly strained configurations, the prohibitive computational overhead of high-order message-passing architectures, and the necessity of nonlocal interactions for open and field-coupled systems. Through four demanding application contexts - electrified interfaces, compositionally fluctuating open systems, multiphase evolution, and large-scale fracture - we establish a framework for observable-specific validation. Highlighting the complementary roles of universal foundation models and task-specific potentials, we emphasize that targeted adaptations must be rigorously benchmarked against intended observables. We conclude with a roadmap for developing physically consistent, hardware-aware force fields that seamlessly connect electronic-structure accuracy to macroscopic materials phenomena.
