Page 136 - Read Online
P. 136
Page 28 of 31 Li et al. J. Mater. Inf. 2026, 6, 10
62. Rajak, P.; Wang, B.; Nomura, K.; et al. Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials. npj.
Comput. Mater. 2021, 7, 572. DOI
63. Kang, Y.; Kim, J. ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large
language models. Nat. Commun. 2024, 15, 4705. DOI PubMed PMC
64. Takahara, I.; Mizoguchi, T.; Liu, B. Accelerated inorganic materials design with generative AI agents. arXiv 2025, arXiv:2504.00741.
Available online: https://doi.org/10.48550/arXiv.2504.00741 (accessed 22 January 2026).
65. Ghafarollahi, A.; Buehler, M. J. ProtAgents: protein discovery via large language model multi-agent collaborations combining physics
and machine learning. Digit. Discov. 2024, 3, 1389-409. DOI PubMed PMC
66. Bagal, V.; Aggarwal, R.; Vinod, P. K.; Priyakumar, U. D. MolGPT: molecular generation using a transformer-decoder model. J. Chem.
Inf. Model. 2022, 62, 2064-76. DOI PubMed
67. Gan, J.; Zhong, P.; Du, Y.; et al. MatLLMSearch: crystal structure discovery with evolution-guided large language models. arXiv 2025,
arXiv:2502.20933. Available online: https://doi.org/10.48550/arXiv.2502.20933 (accessed 22 January 2026).
68. Kim, H.; Jang, Y.; Ahn, S. MT-Mol: multi agent system with tool-based reasoning for molecular optimization. arXiv 2025,
arXiv:2505.20820. Available online: https://doi.org/10.48550/arXiv.2505.20820 (accessed 22 January 2026).
69. Hu, Z.; Zhou, Y.; Wang, Z.; et al. Osda agent: leveraging large language models for de novo design of organic structure directing agents.
In Proceedings of the The Thirteenth International Conference on Learning Representations, Singapore, Singapore, April 24-28, 2025;
OpenReview.net: Amherst, MA, USA, 2025. Available online: https://openreview.net/forum?id=9YNyiCJE3k (accessed 22 January
2026).
70. Zhou, L.; Ling, H.; Yan, K.; et al. Toward greater autonomy in materials discovery agents: unifying planning, physics, and scientists.
arXiv 2025, arXiv:2506.05616. Available online: https://doi.org/10.48550/arXiv.2506.05616 (accessed 22 January 2026).
71. Chaudhari, A.; Ock, J.; Farimani, A. B. Modular large language model agents for multi-task computational materials science. ChemRxiv
2025. Available online: https://doi.org/10.26434/chemrxiv-2025-zkn81-v2 (accessed 22 January 2026).
72. Hu, J.; Nawaz, H.; Rui, Y.; Chi, L.; Ullah, A.; Dral, P. O. Aitomia: your intelligent assistant for AI-driven atomistic and quantum
chemical simulations. arXiv 2025, arXiv:2505.08195. Available online: https://doi.org/10.48550/arXiv.2505.08195 (accessed 22 January
2026).
73. Pham, T. D.; Tanikanti, A.; Keçeli, M. ChemGraph: an agentic framework for computational chemistry workflows. arXiv 2025,
arXiv:2506.06363. Available online: https://doi.org/10.48550/arXiv.2506.06363 (accessed 22 January 2026).
74. Zou, Y.; Cheng, A. H.; Aldossary, A.; et al. El Agente: an autonomous agent for quantum chemistry. Matter 2025, 8, 102263. DOI
75. Lv, S.; Peng, L.; Wu, W.; Yao, Y.; Jiao, S.; Hu, W. Bridging language models and computational materials science: a prompt-driven
framework for material property prediction. MGE. Advances. 2025, 3, e70013. DOI
76. Gadde, R. S. K.; Devaguptam, S.; Ren, F.; et al. Chatbot-assisted quantum chemistry for explicitly solvated molecules. Chem. Sci. 2025,
16, 3852-64. DOI PubMed PMC
77. Ni, B.; Buehler, M. J. MechAgents: large language model multi-agent collaborations can solve mechanics problems, generate new data,
and integrate knowledge. Extreme. Mech. Lett. 2024, 67, 102131. DOI
78. Liu, H.; Li, L. On languaging a simulation engine: rapid modeling of nanoporous media sorption by hierarchical language model. Mater.
Today. Commun. 2024, 40, 109809. DOI
79. Shi, Z.; Xin, C.; Huo, T.; et al. A fine-tuned large language model based molecular dynamics agent for code generation to obtain material
thermodynamic parameters. Sci. Rep. 2025, 15, 10295. DOI PubMed PMC
80. Montoya, J. H.; Winther, K. T.; Flores, R. A.; Bligaard, T.; Hummelshøj, J. S.; Aykol, M. Autonomous intelligent agents for accelerated
materials discovery. Chem. Sci. 2020, 11, 8517-32. DOI PubMed PMC
81. Jia, S.; Zhang, C.; Fung, V. LLMatDesign: autonomous materials discovery with large language models. arXiv 2024, arXiv:2406.13163.
Available online: https://doi.org/10.48550/arXiv.2406.13163 (accessed 22 January 2026).
82. Lourenço, M. P.; Herrera, L. B.; Hostaš, J.; et al. QMLMaterial - a quantum machine learning software for material design and
discovery. J. Chem. Theory. Comput. 2023, 19, 5999-6010. DOI PubMed
83. Ghafarollahi, A.; Buehler, M. J. Rapid and automated alloy design with graph neural network-powered LLM-driven multi-agent systems.
arXiv 2024, arXiv:2410.13768. Available online: https://doi.org/10.48550/arXiv.2410.13768 (accessed 22 January 2026).
84. Ito, S.; Muraoka, K.; Nakayama, A. Knowledge-informed molecular design for zeolite synthesis using general-purpose pretrained large
language models toward human-machine collaboration. Chem. Mater. 2025, 37, 2447-56. DOI
85. Lu, D.; Malof, J. M.; Padilla, W. J. An agentic framework for autonomous metamaterial modeling and inverse design. arXiv 2025,
arXiv:2506.06935. Available online: https://doi.org/10.48550/arXiv.2506.06935 (accessed 22 January 2026).
86. Sprueill, H. W.; Edwards, C.; Agarwal, K.; et al. ChemReasoner: heuristic search over a large language model’s knowledge space using
quantum-chemical feedback. arXiv 2024, arXiv:2402.10980. Available online: https://doi.org/10.48550/arXiv.2402.10980 (accessed 22
January 2026).

