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The discovery of next-generation materials remains a slow, iterative, and resource-intensive process. Conventional approaches rely on sequential cycles of hypothesis generation, synthesis, characterization, and interpretation. Although this process has produced transformative materials, its pace is increasingly misaligned with urgent technological needs in energy, sustainability, electronics, and advanced manufacturing. Recent advances in artificial intelligence, robotics, high-throughput experimentation, and computational physics have created new opportunities to accelerate materials discovery. Self-driving laboratories and closed-loop experimentation systems can now propose experiments, execute them, learn from results, and refine subsequent decisions. These developments suggest the emergence of autonomous materials intelligence as a new paradigm for scientific discovery. However, current approaches often treat artificial intelligence, physics-based simulation, and human expertise as separate instruments rather than as mutually reinforcing partners. AI models may generate predictions without sufficient physical grounding, simulations may remain disconnected from experimental feedback, and human judgment may enter only after automated decisions have already been made. This fragmentation limits the development of truly autonomous and scientifically trustworthy materials discovery systems. This conceptual framework article develops a Human–AI–Physics framework for autonomous materials intelligence. The framework positions human expertise, AI algorithms, and physics-based models as co-equal pillars in a self-driving discovery pipeline. It explains how these pillars interact across discovery, optimization, and validation cycles. The article synthesizes 26 peer-reviewed publications published between 2017 and 2024 across autonomous experimentation, materials informatics, active learning, generative models, graph neural networks, physics-informed machine learning, and self-driving laboratories. The synthesis is not presented as a review or meta-analysis. Instead, it is used to construct a systems-oriented conceptual architecture for integrating human judgment, machine learning, and physical laws. The proposed framework defines autonomous materials intelligence as an iterative workflow in which AI proposes, physics constrains, humans guide, and experiments validate. By linking these functions into a closed-loop system, the framework offers a blueprint for discovering, optimizing, and validating next-generation materials with greater speed, interpretability, and scientific rigor.