材料行为作为机制集合：一种涌现行为的概率框架
Materials Behavior as Mechanism Ensembles: A Probabilistic Framework for Emergent Behaviors
中文总结 AI 辅助
本文提出将材料行为视为机制集合的概率框架，以概率方式连接机制激活等与宏观可观测量，可应用于金属疲劳等系统，助力提前识别涌现行为的有利条件。
中文摘要 AI 辅助
材料行为常被视为从结构到性能的确定性映射，但许多重要现象源于跨尺度多种机制的条件激活，这在金属疲劳中尤为明显——尽管有证据表明局部微观结构、加载历史和竞争单元过程可改变扩展、止裂和自修复之间的平衡，裂纹扩展通常被建模为单调不可逆过程。本文提出一种概率框架，将材料行为描述为组成机制的集合，其激活、相互作用和演化决定涌现结果；该框架以概率方式连接机制激活、状态演化和宏观可观测量，在疲劳裂纹扩展案例中，它将损伤容限重新表述为机制竞争的推理问题，并为整合多尺度模拟、多模态表征和机器学习提供基础，相同逻辑可扩展至其他物理和化学系统，表明该框架可移植到任何涌现行为反映变化条件下机制竞争的系统；该视角综述的更广泛目标是从事后关联结构与性能，转向提前识别使期望涌现行为成为可能的条件。
英文摘要
Materials behavior is often treated as a deterministic mapping from structure to properties, yet many important phenomena emerge from the conditional activation of multiple mechanisms across scales. This is especially evident in fatigue of metals, where crack growth is typically modeled as monotonic and irreversible process, despite evidence that local microstructure, loading history, and competing unit processes can shift the balance among propagation, arrest, and self-healing. Here we present a probabilistic framework that describes materials behavior as an ensemble of constituent mechanisms whose activation, interaction, and evolution determine emergent outcomes. The framework connects mechanism activation, state evolution, and macroscopic observables in a probabilistic way. In the case of fatigue crack propagation, it reframes damage tolerance as an inference problem over mechanism competition and provides a basis for integrating multiscale simulation, multimodal characterization, and machine learning. The same logic extends to other physical and chemical systems suggesting a portable framework for any system in which emergent behavior reflects mechanism competition under changing conditions. The broader ambition of this perspective review is a shift from correlating structure and performance after the fact to identifying, in advance, the conditions that make desired emergent behavior probable.
