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In the rapidly expanding field of materials artificial intelligence, the term “harm” appears with increasing frequency yet remains strikingly ambiguous, applied interchangeably to environmental degradation caused by AI-accelerated discovery of resource-intensive compounds, to health risks arising from the deployment of novel toxic materials, to security threats posed by dual-use generative models, and even to epistemic distortions that undermine the reliability of scientific knowledge itself. This conceptual boundary paper argues that such vagueness is not merely semantic. Still, it fundamentally impedes responsible innovation in materials science, where AI systems now routinely propose molecular structures, optimize synthesis pathways, and guide autonomous experimentation. The present work therefore proposes a precise definition of scientific harm as negative consequences—direct, indirect, potential, or actual—arising specifically from the development, deployment, or misuse of AI systems in materials science that affect humans, environments, knowledge systems, or social structures in ways that are both causally traceable to the AI intervention and normatively undesirable within scientific practice. Building on this foundation, the paper advances a six-fold typology of scientific harm tailored to materials AI: environmental, health, security, epistemic, social, and economic. Each type is defined, mechanistically elaborated, illustrated with materials-specific examples drawn from the literature, and accompanied by detectable signatures that practitioners can monitor. The typology is further sharpened through explicit distinctions from nearby concepts such as risk, danger, misuse, unintended consequence, and side effect, thereby clarifying the unique normative force carried by the language of harm. Ultimately, the framework carries direct implications for harm-aware practice: authors, reviewers, and the broader materials AI community must move beyond vague risk disclaimers toward typed, evidence-based harm assessments that enable targeted mitigation and more ethically robust research trajectories. By furnishing these conceptual tools, the paper seeks to transform “harm” from an overloaded rhetorical placeholder into a precise analytic category capable of guiding the responsible maturation of materials artificial intelligence.

This review examines the literature on ethical frameworks for artificial intelligence (AI) applied to materials science and discovery, synthesizing insights from 31 peer-reviewed publications spanning 2017 to 2026 to trace the evolution from high-level principles to practical implementation. The methodology involved a systematic search across Web of Science, Scopus, arXiv, and PhilPapers using targeted strings such as “ethics AI materials science,” “responsible AI materials discovery,” “ethical framework AI science,” “dual use materials AI,” “AI ethics principles materials,” “governance AI materials research,” “justice AI materials discovery,” and “value alignment materials AI,” with inclusion limited to peer-reviewed works directly addressing ethical dimensions in scientific or materials contexts, yielding 31 core references after PRISMA-style screening of over 500 initial results. Major ethical principles for AI—beneficence, non-maleficence, autonomy, justice, explicability, and sustainability—are surveyed as foundational guides originally developed in broader AI ethics literature but rarely adapted to materials-specific applications. The current state of materials AI literature reveals a predominant focus on technical acceleration of discovery, with explicit ethical engagement appearing in fewer than 20% of surveyed works and often limited to passing mentions rather than systematic analysis. Materials-specific ethical challenges, including dual-use risks in weaponizable materials, environmental harms from resource-intensive AI-driven synthesis, equity gaps in global access to discoveries, labor displacement through automation, intellectual property ambiguities, and intergenerational justice concerns, remain largely unaddressed despite the field’s rapid growth. Significant gaps persist in operationalizing principles, developing governance mechanisms, and providing domain-tailored guidance, underscoring an urgent need for actionable recommendations to bridge the principles-practices divide and foster responsible materials AI innovation that prioritizes societal benefit, sustainability, and justice.