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Physics-guided machine learning (PGML) has emerged as a hybrid paradigm in materials science, integrating domain knowledge with data-driven methods to enhance predictive accuracy and generalizability. Conventional approaches typically embed physical principles as soft inputs—either through loss-function regularization or auxiliary features—allowing violations during optimization. This manuscript advances a conceptual reframing in which physics operates as a hard constraint on the model’s hypothesis space rather than as an additive input. By restricting permissible functional forms, symmetries, and conservation relations a priori, the framework enforces physical consistency at the architectural level, altering the interaction dynamics between data and prior knowledge. The reframing yields systems-level insights into epistemic trade-offs: reduced reliance on large datasets, improved extrapolation beyond training regimes, and inherent satisfaction of thermodynamic or mechanical invariants critical to materials behavior. Analytical implications include feedback structures that couple data refinement to constraint satisfaction, revealing emergent robustness in multiscale modeling. This perspective addresses persistent challenges in materials science, such as sparse experimental data and complex microstructure-property relationships, without resorting to empirical validation. The contribution lies in reinterpreting PGML’s epistemic foundation, steering future developments toward constraint-centric designs that prioritize physical fidelity over post-hoc penalization.

Materials informatics has become a central paradigm in materials science, leveraging machine learning and large-scale datasets to accelerate property prediction, discovery, and design. However, prevailing approaches often treat data as a neutral substrate for modeling, obscuring the value-laden processes through which data is generated. Measurement choices—what properties to quantify, which materials to prioritize, and which experimental or computational protocols to employ—are inherently shaped by epistemic commitments, practical constraints, and broader societal priorities. These choices embed values into data infrastructures, systematically influencing which material phenomena become visible and which remain obscured in downstream models. This manuscript advances a conceptual framework that interprets measurement choices as value-mediated interfaces linking scientific priorities to data constitution and modeling feedback in materials informatics. The framework elucidates how value horizons, choice architectures, data formation processes, and modeling circuits interact to produce steering logics, trade-offs, and path-dependent dynamics. By reframing data bias as a constitutive outcome of value-conditioned measurement rather than a purely technical artifact, the framework reveals characteristic failure modes—including value lock-in, patterned absences, and self-reinforcing feedback—that constrain epistemic exploration. Integrating insights from materials informatics, data bias studies, and philosophical analyses of scientific practice, the framework provides a diagnostic lens for understanding the non-neutrality of data in iterative AI-driven workflows. Rather than prescribing methodological interventions, it foregrounds the epistemic consequences of measurement decisions, inviting greater reflexivity in shaping data landscapes over time. This perspective repositions materials informatics as an evolving epistemic system whose possibilities and limits are co-produced by values, measurements, and models.

The integration of computational tools and data-driven methodologies has transformed materials engineering, enabling accelerated discovery through AI-assisted pipelines that link data acquisition, model training, and experimental validation. In this paradigm, materials informatics leverages vast datasets from high-throughput computations and multimodal sources to inform design decisions, yet inherent feedback dynamics often introduce biases that steer exploration trajectories in unintended ways. This conceptual manuscript identifies a critical gap in understanding how data-model-experiment loops can self-reinforce certain pathways, leading to narrowed exploration spaces and amplified discovery biases. To address this, we introduce the Feedback Steering Framework (FSF), a systems-level architecture that interprets the interplay between data representations, model inferences, and iterative design cycles. The framework elucidates mechanisms such as reinforcement discovery bias, where initial data patterns perpetuate model preferences, and exploration narrowing, wherein computational steering logics constrain the search space over successive iterations. By conceptualizing these dynamics, FSF provides insights into optimizing AI-guided materials exploration for broader epistemic coverage. Implications extend to computational materials science ecosystems, including enhanced uncertainty management in autonomous systems and more robust inverse design strategies, ultimately fostering resilient infrastructures for next-generation materials innovation. This work underscores the need for interpretive tools that balance computational efficiency with comprehensive discovery potential in data-steered environments.

The field of materials engineering has undergone a profound transformation through the integration of high-throughput computation and data-driven methodologies, evolving from traditional trial-and-error approaches to sophisticated closed-loop systems that accelerate discovery. This review synthesizes recent advancements in computational and data-driven materials ecosystems, focusing on the infrastructure enabling autonomous discovery. Key elements include materials informatics platforms that leverage machine learning for property prediction and inverse design, graph neural networks for representation learning, and high-throughput computational workflows that generate multimodal datasets. We examine the progression from static high-throughput screening to dynamic, closed-loop paradigms incorporating active learning, uncertainty quantification, and simulation-experiment integration. Autonomous laboratories represent a pinnacle of this evolution, where AI orchestrates iterative cycles of hypothesis generation, experimentation, and refinement. The synthesis highlights how these infrastructures bridge computational predictions with experimental validation, fostering inverse materials design and optimizing resource allocation in complex chemical spaces. Challenges in data interoperability and model generalizability are noted, alongside prospects for scalable, self-optimizing systems. Overall, this review positions closed-loop data infrastructures as foundational to next-generation materials engineering, promising accelerated innovation in areas like energy storage, catalysis, and structural materials. By integrating diverse literature, we provide a systems-level perspective on how these tools are reshaping the discovery landscape.

The advent of computational and data-driven materials engineering has transformed materials discovery by integrating machine learning with high-throughput simulations and experimental workflows. Within this ecosystem, feature engineering emerges not merely as a technical preprocessing step but as a fundamental scientific framing mechanism that encodes domain knowledge into data representations, influencing inference pathways and discovery outcomes. This conceptual manuscript explores how encoding choices in materials informatics shape epistemic structures, steering computational pipelines from raw multimodal datasets to inverse design strategies. We identify a conceptual gap in current paradigms, where representation decisions often remain implicit, leading to unexamined trade-offs in uncertainty propagation and model interpretability. To address this, we introduce the Encoding Dynamics Framework (EDF), a systems-level architecture that conceptualizes feature engineering as an interactive layer between data infrastructures and AI-guided discovery systems. EDF highlights feedback loops where encoding selections modulate representation learning, graph neural networks, and closed-loop experimentation, fostering more robust computational steering logics. Implications extend to foundation models for materials science, simulation-experiment coupling, and uncertainty quantification, promoting infrastructures that align encoding with scientific inquiry goals. By reframing feature engineering as epistemic framing, this work advances interpretive insights into how data encoding choices drive materials innovation without empirical validation.