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The integration of artificial intelligence (AI) into materials science has transformed the landscape of discovery and insight generation, enabling rapid analysis of complex datasets and simulation of material behaviors at unprecedented scales. However, the reproducibility of AI-generated insights remains a pivotal concern, as it underpins the epistemic validity of claims derived from such systems. This conceptual paper develops a novel theoretical framework that interprets reproducibility not as a static attribute but as an emergent property arising from dynamic interactions among data ecosystems, algorithmic architectures, and human interpretive practices. By synthesizing literature on AI trustworthiness and materials informatics, the framework elucidates the systemic conditions—such as data lineage transparency, algorithmic feedback loops, and ethical epistemic alignments—that must align for AI-derived claims to sustain scrutiny across contexts. It emphasizes interaction dynamics where data quality influences model robustness, while human oversight modulates algorithmic outputs, fostering a balanced ecosystem for reliable insights. Ethical reasoning is integrated throughout, highlighting trade-offs between computational efficiency and interpretive depth. This approach shifts focus from isolated reproducibility metrics to holistic systems-level insights, offering guidance for scholars and practitioners in applied AI for materials science. Ultimately, the framework advocates for a steering logic that prioritizes integrative processes over predictive assertions, ensuring that AI contributions enhance rather than undermine the foundational integrity of materials knowledge.

Materials artificial intelligence (MAI) has revolutionized the discovery, design, and optimization of new materials by leveraging machine learning algorithms to analyze complex datasets and predict properties with high accuracy. However, the rapid proliferation of MAI tools has raised critical questions about benchmarking practices, which are essential for evaluating model performance, ensuring reproducibility, and addressing ethical concerns. This narrative review examines current benchmarking frameworks in MAI, highlighting what is effectively measured—such as predictive accuracy and computational efficiency—and what is often overlooked —such as data bias, interpretability, fairness, and ethical implications. Drawing on recent advances in frameworks such as JARVIS-Leaderboard and Matbench, the review discusses challenges in data quality, reproducibility, and the integration of explainable AI (XAI) methods. It also explores active learning strategies for optimizing materials discovery under limited data conditions and proposes directions for more inclusive and transparent benchmarking. By synthesizing insights from diverse studies, this review aims to guide future MAI research toward robust, equitable, and ethically sound practices that accelerate innovation while mitigating risks.

The accelerating integration of artificial intelligence into materials discovery offers transformative potential for identifying novel compounds and optimizing properties at unprecedented speeds. Yet, this promise is tempered by persistent challenges in maintaining scientific rigor across computational workflows. This review employs a structured literature synthesis grounded exclusively in 35 peer-reviewed publications from 2017 to 2025, identified through targeted searches across Web of Science, Scopus, and arXiv using strings focused on scientific rigor, reproducibility in materials machine learning, reporting standards in materials informatics, methodological quality in AI-driven science, validation standards for materials AI, benchmarking in materials property prediction, replication in computational materials science, and quality assessment frameworks for AI in materials discovery, with inclusion criteria limited to studies addressing AI-assisted discovery practices and exclusion of purely experimental or non-computational works, following a PRISMA-style screening that yielded the final corpus after removing duplicates and off-topic items. Scientific rigor in this domain is understood as the systematic application of thorough, accurate, and transparent methods that ensure independent verification of AI-generated predictions while upholding honesty in reporting both positive and negative outcomes. Current practices in materials AI demonstrate growing sophistication in model development and data utilization but reveal inconsistent transparency in code and data sharing, limited replication efforts, and reliance on internal validation that falls short of broader scientific benchmarks, even as select studies begin to engage with established checklists and principles. Critical gaps emerge in the absence of tailored materials-AI rigor frameworks, the rarity of external experimental validation, and insufficient community mechanisms for enforcing completeness in reporting, which collectively risk resource misallocation and diminished confidence in AI-driven claims. Targeted recommendations for authors, reviewers, journals, and funders emphasize mandatory code and data deposition, comprehensive hyperparameter disclosure, and cultural shifts toward valuing replication and negative results to bridge these deficiencies and elevate the field’s overall integrity.

Reproducibility is a cornerstone of scientific integrity, yet in the rapidly expanding field of data-driven materials synthesis, the term is used inconsistently and often misleadingly. Authors frequently claim “reproducibility” without specifying whether they refer to computational verification of models or successful laboratory synthesis of predicted materials. This boundary/Definitional article clarifies that “reproducibility” in this domain encompasses two fundamentally distinct and orthogonal concepts: algorithmic reproducibility and experimental replication. Algorithmic reproducibility is defined as the ability to obtain identical numerical results (within documented floating-point tolerances) when the same code is executed on the same data in the same computational environment. Experimental replication, by contrast, is the ability of an independent laboratory to synthesize the same material—within clearly defined characterization tolerances—by strictly following the published synthesis protocol. The article demonstrates that these two forms of reproducibility operate in separate ontological domains (computational versus chemical) and are frequently conflated in the literature, leading to overclaims, misdirected research effort, and erosion of community trust. Through a systematic boundary analysis, four-level assessment scales are established for each concept, a comparative framework (including an interaction matrix) is presented, and common gray zones and boundary cases are examined. The analysis shows that algorithmic reproducibility is necessary but insufficient for validating materials predictions, while experimental replication is necessary but insufficient for validating the underlying machine-learning pipeline. Only explicit reporting of both at defined levels constitutes a complete and trustworthy claim. This framework provides authors, reviewers, journals, and funders with a precise, operational vocabulary and a practical two-part Reproducibility Declaration standard. Its adoption will reduce ambiguity, strengthen the credibility of AI-driven materials discovery, and accelerate the reliable translation of computational predictions into verifiable laboratory outcomes. The distinctions introduced here are essential for maturing data-driven materials science into a robust, reproducible discipline.