A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools

• URL: http://arxiv.org/abs/2506.20743v1
• Date: Wed, 25 Jun 2025 18:10:30 GMT
• Title: A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools
• Authors: Minh-Hao Van, Prateek Verma, Chen Zhao, Xintao Wu,
• Abstract summary: Foundation models (FMs) are enabling scalable, general-purpose, and multimodal AI systems for scientific discovery.<n>This survey provides a comprehensive overview of foundation models, agentic systems, datasets, and computational tools supporting this growing field.
• Score: 15.928285656168422
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foundation models (FMs) are catalyzing a transformative shift in materials science (MatSci) by enabling scalable, general-purpose, and multimodal AI systems for scientific discovery. Unlike traditional machine learning models, which are typically narrow in scope and require task-specific engineering, FMs offer cross-domain generalization and exhibit emergent capabilities. Their versatility is especially well-suited to materials science, where research challenges span diverse data types and scales. This survey provides a comprehensive overview of foundation models, agentic systems, datasets, and computational tools supporting this growing field. We introduce a task-driven taxonomy encompassing six broad application areas: data extraction, interpretation and Q\&A; atomistic simulation; property prediction; materials structure, design and discovery; process planning, discovery, and optimization; and multiscale modeling. We discuss recent advances in both unimodal and multimodal FMs, as well as emerging large language model (LLM) agents. Furthermore, we review standardized datasets, open-source tools, and autonomous experimental platforms that collectively fuel the development and integration of FMs into research workflows. We assess the early successes of foundation models and identify persistent limitations, including challenges in generalizability, interpretability, data imbalance, safety concerns, and limited multimodal fusion. Finally, we articulate future research directions centered on scalable pretraining, continual learning, data governance, and trustworthiness.

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