TY - JOUR
T1 - Conceptual Foundations of Applied AI in Materials Science - Definitions, Assumptions, and Open Debates
AU - Hiroshi Tanaka
AU - Yuki Sato
AU - Kenji Mori
AU - Rina Okabe
JF - Journal of Artificial Intelligence for Materials Science
JO - J. Artif. Intell. Mater. Sci.
SN - 3149-8957
Y1 - 2024
VL - 3
IS - 2
DO - 10.68159/g392575577
SP - 63
N2 - The rapid integration of artificial intelligence (AI) into materials science marks a profound shift in how materials are discovered, characterized, and optimized. Rather than functioning merely as a computational aid, AI increasingly operates as an epistemic instrument that reshapes scientific workflows, decision-making practices, and notions of explanation within the field. This narrative review examines the conceptual foundations underpinning applied AI in materials science, with a particular focus on core definitions, implicit and explicit assumptions, and unresolved debates that continue to shape the domain. Key AI paradigms—including supervised, unsupervised, and reinforcement learning—are situated within materials-specific contexts such as property prediction, structure–property mapping, and autonomous experimentation. The review critically interrogates foundational assumptions regarding data quality, representativeness, generalization, and model transferability, highlighting how these assumptions condition both the successes and failures of AI-driven materials research. Persistent debates surrounding interpretability, epistemic trust, ethical responsibility, and environmental sustainability are synthesized from recent literature published. By articulating both the transformative potential and the conceptual limitations of applied AI, this review underscores the necessity of rigorous validation, transparent reasoning, and interdisciplinary collaboration to ensure that AI contributes robustly and responsibly to materials innovation.
UR - https://iamrp.net/g392575577
ER -