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This review examines the problem of scientific consensus formation in AI-driven materials science by systematically analyzing conceptual approaches from philosophy and sociology of science alongside empirical developments in computational materials research, drawing exclusively on 31 peer-reviewed publications from 2017–2026 identified through targeted searches in Web of Science, Scopus, arXiv, and PhilPapers using terms such as “scientific consensus” AI materials, “consensus formation” machine learning science, “disagreement” materials AI, “benchmark” consensus materials informatics, “epistemic consensus” AI science, “paradigm” materials AI, “scientific disagreement” computational science, and “consensus mechanism” AI research, with inclusion criteria limited to papers addressing epistemology, disagreement, uncertainty, benchmarks, or paradigm dynamics in data-driven disciplines and exclusion of purely technical performance reports. Consensus concepts are traced from logical-positivist agreement on theories through Kuhnian paradigms and Mertonian social processes to Bayesian convergence and pragmatic problem-solving necessities, revealing how each framework illuminates different facets of knowledge coordination in materials science. AI’s impact on consensus formation operates through six distinct mechanisms—accelerated hypothesis validation, model disagreement, benchmark-driven focal points, opacity-induced dissent, data-driven convergence, and authority shifts—both facilitating rapid agreement on material properties and simultaneously generating new forms of epistemic fragmentation. These dynamics create profound tensions and paradoxes, including the trade-off between speed and deliberation, convergence versus diversity, predictive agreement versus explanatory understanding, local versus global consensus, and human versus AI authority, while exposing critical gaps such as the absence of a dedicated theory for AI-mediated consensus, the scarcity of empirical studies tracking real-time consensus processes in materials AI communities, and unresolved questions about managing productive disagreement. Recommendations are offered for researchers, journals, and the broader community to distinguish model agreement from scientific consensus, institutionalize empirical consensus studies, preserve productive dissent, and develop governance protocols that harness AI’s epistemic power without sacrificing critical scrutiny, thereby guiding the field toward more reflexive and robust knowledge production in the age of AI-augmented materials discovery.

In the rapidly evolving field of computational and data-driven materials engineering, the interplay between algorithmic processes and established scientific paradigms shapes the reliability of predictive outcomes. Traditional scientific consensus emerges from iterative experimental validation, peer review, and cumulative evidence, fostering a shared understanding of material behaviors and properties. In contrast, algorithmic consensus arises from the aggregation of computational models, often leveraging machine learning architectures to distill patterns from vast datasets. This manuscript explores the tensions and synergies between these two forms of consensus in materials prediction, highlighting how data-driven approaches can either reinforce or challenge longstanding scientific interpretations. A conceptual gap persists in integrating these consensus mechanisms, where algorithmic outputs may diverge from empirical benchmarks due to representation biases or uncertainty propagation. To address this, we introduce the Consensus Integration Lattice (CIL), a novel framework that structures the alignment of algorithmic and scientific consensus through layered computational workflows, feedback mechanisms, and epistemic risk assessments. By conceptualizing discovery pipelines that couple high-throughput simulations with multimodal data integration, CIL facilitates more robust materials predictions. Implications extend to autonomous discovery systems, inverse design strategies, and uncertainty quantification, potentially enhancing the efficiency of materials informatics ecosystems. This work underscores the need for infrastructure-level analyses to bridge computational agility with scientific rigor, paving the way for hybrid paradigms in materials engineering.