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In the rapidly evolving field of artificial intelligence for materials science, research has overwhelmingly emphasized the development of predictive models, active learning algorithms, and inverse design strategies to accelerate the identification of novel functional materials. Yet, the critical boundary at which these computational outputs become experimental inputs—the model-science interface—remains largely ignored and treated as an unproblematic transmission step. Existing literature on self-driving laboratories and autonomous experimentation systems, while advancing integrated platforms for clean energy discovery and closed-loop workflows, assumes that model predictions, uncertainty estimates, and experimental recommendations flow seamlessly into synthesis protocols, characterization decisions, and iterative loops without significant distortion or loss. This paper proposes the model-science interface as a distinct object of study, worthy of its own conceptual framework rather than being subsumed under broader discussions of automation or machine learning. By formalizing the interface as the active zone of translation between algorithmic intelligence and empirical practice, the framework distinguishes it from upstream modeling or downstream execution phases, thereby enabling systematic analysis of its internal dynamics. The key concepts articulated herein include a typology of interface operation modes differentiated along dimensions of autonomy and stakes, a detailed examination of information transformations that occur when AI outputs cross into experimental inputs—including preservation of core predictions, loss of contextual nuance, addition of laboratory constraints, and potential distortion through interpretation—and the introduction of “interface fidelity” as a conceptual variable that quantifies the quality of this transition across multiple dimensions. These elements, which build directly upon foundational accounts of autonomous chemical experiments and minimal working examples for self-driving laboratories, provide a vocabulary and set of distinctions for diagnosing interface failure modes that can undermine the overall efficacy of materials discovery pipelines. The framework draws upon foundational ideas in autonomous experimentation while elevating the interface itself as the locus of negotiation between computational promise and physical reality. Ultimately, adopting an interface-aware perspective carries profound implications for materials AI practice. It encourages researchers to design interfaces with intentionality, to report interface specifications alongside model performance, and to study information dynamics explicitly, thereby realizing the full potential of self-driving laboratories for accelerating the discovery of materials for clean energy, piezoelectrics, and beyond. This conceptual contribution thus bridges the persistent gap between model sophistication and experimental impact, fostering more accountable, efficient, and robust autonomous materials research ecosystems.

In the evolving landscape of computational and data-driven materials engineering, autonomous experimentation platforms are transforming discovery pipelines by integrating machine learning algorithms with robotic systems to accelerate material synthesis and characterization. These self-driving laboratories operate through closed-loop cycles where data acquisition, model inference, and experimental steering occur without continuous human oversight, raising critical questions about resource allocation mechanisms that ensure efficient, unbiased, and scalable operations. This manuscript addresses a conceptual gap in the governance of such systems: the need for structures that allocate decision rights—encompassing experimental priorities, parameter spaces, and computational resources—absent deliberate intervention. We introduce the Implicit Allocation Governance (IAG) framework, which conceptualizes resource distribution as emergent from layered interactions between data representations, inference engines, and discovery logics, emphasizing epistemic trade-offs and feedback dynamics. By synthesizing recent advancements in Bayesian active learning, reinforcement learning-guided workflows, and multi-agent robotic systems, the framework highlights how governance can arise implicitly through system architectures that balance exploration-exploitation tensions and mitigate representational biases. Implications extend to enhancing the robustness of autonomous materials discovery, fostering interoperability across distributed labs, and informing the design of next-generation computational infrastructures. This work underscores the shift from human-centric deliberation to algorithmically embedded governance, paving the way for more resilient and adaptive materials engineering paradigms.