REFERENCES

1. Panel Discussion: Unlocking the AI future of Materials Science. 2024. https://www.koushare.com/live/details/33143?vid=150539. (accessed 23 Jul 2025).

2. Tran, H.; Gurnani, R.; Kim, C.; et al. Design of functional and sustainable polymers assisted by artificial intelligence. Nat. Rev. Mater. 2024, 9, 866-86.

3. Bartel, C. J.; Sutton, C.; Goldsmith, B. R.; et al. New tolerance factor to predict the stability of perovskite oxides and halides. Sci. Adv. 2019, 5, eaav0693.

4. Merchant, A.; Batzner, S.; Schoenholz, S. S.; Aykol, M.; Cheon, G.; Cubuk, E. D. Scaling deep learning for materials discovery. Nature 2023, 624, 80-5.

5. Szymanski, N. J.; Rendy, B.; Fei, Y.; et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature 2023, 624, 86-91.

6. Cheetham, A. K.; Seshadri, R. Artificial intelligence driving materials discovery? Perspective on the article: scaling deep learning for materials discovery. Chem. Mater. 2024, 36, 3490-5.

7. Leeman, J.; Liu, Y.; Stiles, J.; et al. Challenges in high-throughput inorganic materials prediction and autonomous synthesis. PRX. Energy. 2024, 3, 011002.

8. Zhang, B.; Zhu, Z.; Li, H.; Cao, J.; Jiang, J. Revolutionizing chemistry and material innovation: an iterative theoretical-experimental paradigm leveraged by robotic AI chemists. CCS. Chem. 2025, 7, 345-60.

9. MacLeod, B. P.; Parlane, F. G. L.; Morrissey, T. D. Self-driving laboratory for accelerated discovery of thin-film materials. Sci. Adv. 2020, 6, eaaz8867.

10. Xue, D.; Balachandran, P. V.; Hogden, J.; Theiler, J.; Xue, D.; Lookman, T. Accelerated search for materials with targeted properties by adaptive design. Nat. Commun. 2016, 7, 11241.

11. Jiang, J. A data-driven robotic AI-chemist. 2024. https://www.koushare.com/live/details/33143?vid=150642. (accessed 23 Jul 2025).

12. Zhu, Q.; Huang, Y.; Zhou, D.; et al. Automated synthesis of oxygen-producing catalysts from Martian meteorites by a robotic AI chemist. Nat. Synth. 2024, 3, 319-28.

13. Xie, Y.; Feng, S.; Deng, L.; et al. Inverse design of chiral functional films by a robotic AI-guided system. Nat. Commun. 2023, 14, 6177.

14. Wei, J.; Wang, X.; Schuurmans, D.; et al. Chain-of-thought prompting elicits reasoning in large language models. arXiv 2022, arXiv:2201.11903. https://doi.org/10.48550/arXiv.2201.11903. (accessed 23 Jul 2025).

15. Schulman, J.; Wolski, F.; Dhariwal, P.; Radford, A.; Klimov, O. Proximal policy optimization algorithms. arXiv 2017, arXiv:1707.06347. https://doi.org/10.48550/arXiv.1707.06347. (accessed 23 Jul 2025).

16. Trunscke, A. Creating synergies between experimental and computational approaches in advanced materials design. 2024. https://www.koushare.com/live/details/33143?vid=150646. (accessed 23 Jul 2025).

17. Foppa, L.; Ghiringhelli, L. M.; Girgsdies, F.; et al. Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence. MRS. Bull. 2021, 46, 1016-26.

18. Marshall, C. P.; Schumann, J.; Trunschke, A. Achieving digital catalysis: strategies for data acquisition, storage and use. Angew. Chem. Int. Ed. Engl. 2023, 62, e202302971.

19. Wilkinson, M. D.; Dumontier, M.; Aalbersberg, I. J.; et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data. 2016, 3, 160018.

20. The Materials Project. https://next-gen.materialsproject.org/. (accessed 23 Jul 2025).

21. RCSB Protein Data Bank. https://www.rcsb.org/. (accessed 23 Jul 2025).

22. Materials Cloud. https://www.materialscloud.org/home. (accessed 23 Jul 2025).

23. DP Technology. https://www.dp.tech/en. (accessed 23 Jul 2025).

24. NFDI4Cat. https://github.com/nfdi4cat/voc4cat/. (accessed 23 Jul 2025).

25. MolSSI- The Molecular Sciences Software Institute. https://molssi.org/. (accessed 23 Jul 2025).

26. Crawford, D. The Molecular Sciences Software Institute. 2024. https://www.koushare.com/live/details/33143?vid=150526. (accessed 23 Jul 2025).