基于物理学原理的材料人工智能用于可靠的材料发现
Physics-Grounded Materials Artificial Intelligence for Reliable Materials Discovery
AI总结：
该研究提出PhysMat AI框架，整合物理知识用于材料发现，通过多领域示例说明其应用，还规划了从物理感知到自主AI的发展路线，助力可靠材料发现。
AI中文摘要：
人工智能（AI）正在变革材料发现领域，但传统的数据驱动方法往往存在可解释性有限、外推能力差以及与物理定律不一致的问题。由于材料行为从根本上由热力学、动力学、电子结构、输运过程和工作环境所支配，下一代材料智能必须超越基于相关性的预测，转向基于物理学原理的推理。在这篇观点文章中，我们系统地讨论了基于物理学原理的材料人工智能（PhysMat AI），将其作为一个统一视角，通过五个互补角色将物理知识整合到材料智能中：物理学作为先验知识、描述符、约束条件、验证器和基础设施。我们使用催化、固态电池中的固态电解质以及储氢材料的代表性例子，说明物理原理如何指导数据表示、模型推理、验证工作流程和知识管理。我们进一步展示了AI智能体如何利用这些物理感知组件，在物理可行的搜索空间内执行机制引导的发现。最后，我们概述了从物理感知AI到物理推理AI，最终到物理自主AI的发展路线图。展望未来，材料智能应从预测模型演进为自主科学系统，该系统能够整合物理推理、多尺度模拟、实验验证和持续知识更新，以实现可靠的材料发现。
英文摘要：
Artificial intelligence (AI) is transforming materials discovery, yet conventional data-driven approaches often suffer from limited interpretability, poor extrapolation, and inconsistency with physical laws. Since materials behavior is fundamentally governed by thermodynamics, kinetics, electronic structure, transport processes, and operating environments, the next generation of materials intelligence must move beyond correlation-based prediction toward physics-grounded reasoning. In this Perspective, we systematically discuss Physics-Grounded Materials AI (PhysMat AI) as a unifying perspective for integrating physical knowledge into materials intelligence through five complementary roles: physics as prior knowledge, descriptors, constraints, verifiers, and infrastructure. Using representative examples from catalysis, solid-state electrolytes in solid-state battery, and hydrogen-storage materials, we illustrate how physical principles guide data representation, model reasoning, validation workflows, and knowledge management. We further present how AI agents can leverage these physics-aware components to perform mechanism-guided discovery within physically feasible search spaces. Finally, we outline a developmental roadmap from physics-aware AI to physics-reasoning AI and ultimately physics-autonomous AI. Looking forward, materials intelligence should evolve from predictive models toward autonomous scientific systems capable of integrating physical reasoning, multiscale simulations, experimental validation, and continuous knowledge updating for reliable materials discovery.
