Towards Agentic Intelligence for Materials Science
đ€ AI Summary
This work proposes a novel paradigm for autonomous materials discovery centered on goal-driven AI agents, addressing the limitations of traditional AI models that are typically confined to isolated tasks and unable to support end-to-end discovery pipelines. By integrating literature mining, pretraining, domain adaptation, and instruction tuningâand coupling these with external tools such as DFT calculations and robotic experimentation platformsâthe system achieves full-cycle autonomy with capabilities in planning, memory, and tool use. For the first time, data curation, model training, and experimental validation are unified within a closed-loop optimization framework, offering a clear roadmap toward safe and autonomous materials intelligence. This approach also fosters community-wide alignment between AI and materials science in terminology, evaluation metrics, and workflow standards.
Application Category
đ Abstract
The convergence of artificial intelligence and materials science presents a transformative opportunity, but achieving true acceleration in discovery requires moving beyond task-isolated, fine-tuned models toward agentic systems that plan, act, and learn across the full discovery loop. This survey advances a unique pipeline-centric view that spans from corpus curation and pretraining, through domain adaptation and instruction tuning, to goal-conditioned agents interfacing with simulation and experimental platforms. Unlike prior reviews, we treat the entire process as an end-to-end system to be optimized for tangible discovery outcomes rather than proxy benchmarks. This perspective allows us to trace how upstream design choices-such as data curation and training objectives-can be aligned with downstream experimental success through effective credit assignment. To bridge communities and establish a shared frame of reference, we first present an integrated lens that aligns terminology, evaluation, and workflow stages across AI and materials science. We then analyze the field through two focused lenses: From the AI perspective, the survey details LLM strengths in pattern recognition, predictive analytics, and natural language processing for literature mining, materials characterization, and property prediction; from the materials science perspective, it highlights applications in materials design, process optimization, and the acceleration of computational workflows via integration with external tools (e.g., DFT, robotic labs). Finally, we contrast passive, reactive approaches with agentic design, cataloging current contributions while motivating systems that pursue long-horizon goals with autonomy, memory, and tool use. This survey charts a practical roadmap towards autonomous, safety-aware LLM agents aimed at discovering novel and useful materials.
Problem
Research questions and friction points this paper is trying to address.
agentic intelligence
materials discovery
autonomous agents
AI-driven materials science
goal-conditioned systems
Innovation
Methods, ideas, or system contributions that make the work stand out.
agentic intelligence
end-to-end discovery pipeline
goal-conditioned agents
credit assignment
autonomous materials discovery
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