REFERENCES
1. Kusne, A. G.; Yu, H.; Wu, C.; et al. On-the-fly closed-loop materials discovery via Bayesian active learning. Nat. Commun. 2020, 11, 5966.
2. Szymanski, N. J.; Rendy, B.; Fei, Y.; et al. An autonomous laboratory for the accelerated synthesis of inorganic materials. Nature 2023, 624, 86-91.
3. Tan, H. D.; Gao, B.; Huang, H.; et al. Data-driven, mechanistically guided prediction of yield and chemoselectivity in SuFEx reactions. J. Am. Chem. Soc. 2026, 148, 24138-50.
4. Zhang, D.; Chen, Y.; Liu, C.; et al. Accelerating catalyst materials discovery with large artificial intelligence models. Angew. Chem. Int. Ed. Engl. 2026, 65, e26150.
5. Burger, B.; Maffettone, P. M.; Gusev, V. V.; et al. A mobile robotic chemist. Nature 2020, 583, 237-41.
6. Shields, B. J.; Stevens, J.; Li, J.; et al. Bayesian reaction optimization as a tool for chemical synthesis. Nature 2021, 590, 89-96.
7. Tom, G.; Schmid, S. P.; Baird, S. G.; et al. Self-driving laboratories for chemistry and materials science. Chem. Rev. 2024, 124, 9633-732.
