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Materials informatics, the application of data science and machine learning to materials research, has revolutionized the discovery and design of new materials. However, the field faces significant challenges in reporting failure cases, negative results, and biases, which are often silenced in the literature. This review examines documented failures in materials informatics, such as data bias, model overoptimism, and reproducibility issues, and highlights the systemic factors that lead to their underreporting. Drawing on 30 recent peer-reviewed articles, we explore themes including data quality, algorithmic limitations, and publication bias. The objectives are to assess what is typically documented, identify silenced aspects, such as unsuccessful experiments, and propose strategies for more transparent reporting. By addressing these gaps, the review aims to foster a more robust and trustworthy materials informatics ecosystem, ultimately accelerating sustainable innovation in materials science.

The integration of artificial intelligence (AI) into materials science has significantly accelerated discovery and optimization processes. Yet, it simultaneously amplifies long-standing epistemic vulnerabilities rooted in the systematic underrepresentation of negative results. Failed experiments, unstable material phases, and inaccurate predictions are often excluded from the published record, resulting in datasets that are skewed and shape AI model training and inference. This conceptual paper examines how epistemic gaps distort the dynamics of data generation, model development, and experimental validation in materials AI. By synthesizing literature on publication bias, model robustness, and uncertainty-aware learning, the study demonstrates how positive-only knowledge bases foster overconfident predictions, limit generalization, and obscure material boundary conditions. To address these challenges, the paper proposes a failure-aware epistemic learning framework that structurally integrates negative results into AI-driven materials discovery through recursive feedback structures, uncertainty modulation, and inclusive steering logics. Ethical reasoning situates this framework within principles of epistemic accountability, sustainability, and responsible innovation. By reinterpreting negative results as indispensable sources of information rather than peripheral artifacts, the paper advances a conceptual foundation for more resilient, transparent, and reliable AI applications in materials science.

Materials AI stands at a critical juncture where rapid advances in machine learning and data-driven discovery are reshaping materials engineering. Yet the field is severely constrained by a deep-rooted publication bias that systematically favors positive results while suppressing negative outcomes. This position paper asserts that Materials AI urgently requires widespread failure reporting and that negative results must be recognized and maintained as essential community infrastructure. In the prevailing culture, only successful predictions, high-accuracy models, and newly discovered materials reach publication, while failed models, insufficient datasets, misleading benchmarks, and unsuccessful synthesis attempts remain hidden. The consequences are profound: researchers repeatedly traverse the same unproductive paths, the scientific literature presents a distorted view of reality, meta-analysis becomes impossible, and overall progress slows dramatically. Failure reporting functions as shared infrastructure—much like open databases or standardized protocols—by mapping the boundaries of what does not work and thereby enabling more efficient exploration by the entire community. Four core types of failures demand routine documentation: model failures, data failures, benchmark failures, and synthesis failures, with additional emphasis on transfer and extrapolation failures. By treating negative results as valuable contributions rather than career liabilities, the community can accelerate discovery, enhance reproducibility, and build a more trustworthy knowledge base. This paper articulates the position, analyzes the publication bias problem, defines failure reporting as infrastructure, classifies failure types, and identifies barriers before offering concrete recommendations. Embracing negative results will transform Materials AI from a literature of selective successes into a robust, transparent, and collectively intelligent discipline capable of delivering sustainable materials innovation at scale.