REFERENCES
1. Tom, G.; Schmid, S. P.; Baird, S. G.; et al. Self-driving laboratories for chemistry and materials science. Chem. Rev. 2024, 124, 9633-732.
2. Slattery, A.; Wen, Z.; Tenblad, P.; et al. Automated self-optimization, intensification, and scale-up of photocatalysis in flow. Science 2024, 383, eadj1817.
3. Huang, L.; Zhang, C.; Fu, Y.; et al. Natural-language-interfaced robotic synthesis for AI-copilot-assisted exploration of inorganic materials. J. Am. Chem. Soc. 2025, 147, 23014-25.
4. Song, T.; Luo, M.; Zhang, X.; et al. a multiagent-driven robotic AI chemist enabling autonomous chemical research on demand. J. Am. Chem. Soc. 2025, 147, 12534-45.
5. Li, X.; Wang, S.; Lin, Y.; Wu, Y. Retro-expert: collaborative reasoning for interpretable retrosynthesis. arXiv 2025, arXiv:2508.10967. Available online: https://doi.org/10.48550/arXiv.2508.10967 (accessed 8 July 2026).
6. Zhang, S.; Li, H.; Chen, L.; et al. Reasoning-driven retrosynthesis prediction with large language models via reinforcement learning. arXiv 2025, arXiv:2507.17448. Available online: https://doi.org/10.48550/arXiv.2507.17448 (accessed 8 July 2026).
7. Zhao, G.; Lu, Z.; Ge, Y.; et al. MolReasoner: toward effective and interpretable reasoning for molecular LLMs. arXiv 2025, arXiv:2508.02066. Available online: https://doi.org/10.48550/arXiv.2508.02066 (accessed 8 July 2026).
8. Zhao, Z.; Huang, Z.; Li, J.; et al. SUPERChem: a multimodal reasoning benchmark in chemistry. arXiv 2025, arXiv:2512.01274. Available online: https://doi.org/10.48550/arXiv.2512.01274 (accessed 8 July 2026).
9. Zhang, D.; Jia, X.; Tran, H. B.; et al. “DIVE” into hydrogen storage materials discovery with AI agents. Chem. Sci. 2026, 17, 3031-42.
10. Ding, K.; Yu, J.; Huang, J.; Yang, Y.; Zhang, Q.; Chen, H. SciToolAgent: a knowledge-graph-driven scientific agent for multitool integration. Nat. Comput. Sci. 2025, 5, 962-72.
11. Noh, H.; Na, G. S.; Lee, N.; Park, C. MSP-LLM: a unified large language model framework for complete material synthesis planning. arXiv 2026, arXiv:2602.07543. Available online: https://doi.org/10.48550/arXiv.2602.07543 (accessed 8 July 2026).
12. Bai, X.; Zheng, Z.; Zhang, X.; Wang, H.; Yang, R.; Li, J. MOFReasoner: think like a scientist - a reasoning large language model via knowledge distillation. Digit. Discov. 2026, 5, 869-77.
13. Vangala, B. P.; Mahmud, S.; Neupane, P.; Selvaraj, J.; Cheng, J. HalluMat: detecting hallucinations in LLM-generated materials science content through multi-stage verification. arXiv 2025, arXiv:2512.22396. Available online: https://doi.org/10.48550/arXiv.2512.22396 (accessed 8 July 2026).
14. Joung, J. F.; Fong, M. H.; Casetti, N.; Liles, J. P.; Dassanayake, N. S.; Coley, C. W. Electron flow matching for generative reaction mechanism prediction. Nature 2025, 645, 115-23.
15. Pilon, S.; Savino, E.; Bayley, O. M.; et al. A flexible and affordable self-driving laboratory for automated reaction optimization. Nat. Synth. 2026.
16. Zhang, Z.; Ren, Z.; Hsu, C. W.; et al. A multimodal robotic platform for multi-element electrocatalyst discovery. Nature 2025, 647, 390-6.
17. Chen, G.; Yuan, W.; You, F. Bridging electron microscopy and materials analysis with an autonomous agentic platform. Sci. Adv. 2026, 12, eaed0583.
18. Yao, T.; Yang, Y.; Cai, J.; et al. From LLM to agent: a large-language-model-driven machine learning framework for catalyst design of MgH2 dehydrogenation. J. Magnes. Alloys. 2026, 14, 101858.
