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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.

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.