CompMat-Bench is a new benchmark comprising 94 tasks drawn from recent computational materials science studies, designed to evaluate AI agents on realistic research steps without requiring costly simulations during testing. The benchmark pre‑reproduces inputs and outputs to provide ground truth, allowing agents to be graded with fixed rules rather than an LLM judge. It supports both single tasks and multi‑step workflows, with varying levels of methodological guidance, and shows that while agents can achieve high pass rates on individual tasks, performance drops in longer workflows or with reduced guidance, often due to scientific rather than software errors.
By Chenmu Zhang, Levi Felix, Jun-Jie Zhang, Xingfu Li, Xuelian Jiang, Tao Jiang, Subhendu Mishra, Xixi Qin, Boris Yakobson
arXiv:2608. 15776v1 Announce Type: cross Abstract: Despite the powerful multi-scale modeling methods and high-throughput infrastructures established in the materials community, real material computation workflows remain fragmented and heavily manual, requiring researchers to constantly bridge software tools, data analysis, and intermediate decisions.
By Hongfu Huang, Yuzhe Li, Ao Xu, Bo Liu, Changrui Wang, Kan Tang, Ning Yang, Shengxian Liu, Hanyu Liu, Pengpeng Zhang, Linggang Zhu, Fengkai Liu, Yichen Lu, Tong Zhao, Naihua Miao, Jian Zhou, Zhimei Sun
arXiv:2606. 29717v1 Announce Type: cross Abstract: Predicting a material's properties from its structure is a central, fast-advancing problem in computational materials science.
By Chenmu Zhang, Boris I. Yakobson
arXiv:2605. 26179v2 Announce Type: replace-cross Abstract: Density functional theory (DFT) serves as the basis for computational discovery in materials science and chemistry, yet each calculation demands extensive human effort: adjusting algorithms when convergence stalls, revising plans when unexpected physics emerges, and inserting steps as intermediate results reshape the problem.
By Penghui Yang, Zhonghan Zhang, Yue Li, Xinrun Wang, Yanchen Deng, Yuhao Lu, Bijun Tang, Zheng Liu, Bo An
arXiv:2607. 17100v1 Announce Type: cross Abstract: An AI research agent can improve the score it sees without finding a modelling change that works on new materials.
By Jingjie Ning, Xiaochuan Li, Shanshan Zhong, Ji Zeng, Guolin Ke
PeroMAS is a multi-agent system designed for discovering perovskite materials, integrating tools encapsulated as Model Context Protocols to handle the full workflow from literature retrieval to synthesis. It enables design under multi-objective constraints and outperforms single LLM or traditional search methods in discovery efficiency. The system’s effectiveness was validated by real synthesis experiments guided by expert evaluation.
By Yishu Wang, Wei Liu, Yifan Li, Shengxiang Xu, Xujie Yuan, Ran Li, Yuyu Luo, Jia Zhu, Shimin Di, Min-Ling Zhang, Guixiang Li
arXiv:2512. 11935v2 Announce Type: replace Abstract: Agentic AI systems increasingly connect large language models (LLMs) to external scientific tools, yet whether and when tool access improves prediction accuracy remains uncharacterized.
By Jaehyung Lee, Justin Ely, Kent Zhang, Akshaya Ajith, Charles Rhys Campbell, Kamal Choudhary
arXiv:2609.26402v1 Announce Type: new
Abstract: The discovery of novel inorganic materials drives technological breakthroughs in critical fields such as computing and energy storage. Generative AI ha...
By Thomas Egg, Harry Winston Sullivan, Ellad B. Tadmor, Stefano Martiniani
arXiv:2601. 21527v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) has transformed materials discovery, enabling rapid exploration of chemical space through generative models and surrogate screening.
By Sajid Mannan, Rupert J. Myers, Rohit Batra, Rocio Mercado, Lothar Wondraczek, N. M. Anoop Krishnan
arXiv:2606. 31366v1 Announce Type: cross Abstract: Driven by high-throughput experimentation, computational modeling, and artificial intelligence (AI), materials data has expanded at an unprecedented rate.
By Chenyao Ma, Di Zhang, Weibo Gong, Wei Du, Rui Su, Yuhang Chen, Kan Xu, Huan Gu, Limin Li, Piao Ma, Zhenghao Li, Hao Li
The article reviews methods for assessing large language model (LLM) based AI agents in materials synthesis, focusing on their integration with experimental tools. It outlines evaluation strategies—including knowledge, reasoning, tool‑use, and closed‑loop benchmarks—and applies them to atomic layer deposition (ALD) as a case study. A practical framework for evaluating LLMs in this context is also presented.
By Angel Yanguas-Gil
arXiv:2608. 14063v1 Announce Type: new Abstract: Machine learning is rapidly reshaping constitutive modeling, offers new ways to learn material behavior directly from experimental data, and challenges long-established modeling paradigms.
By Hagen Holthusen, Moritz Flaschel, Denisa Martonov\'a, Ellen Kuhl