Page 108 - Read Online

Kobayashi et al. J. Mater. Inf. 2025, 5, 50 https://dx.doi.org/10.20517/jmi.2025.44 Page 7 of 7

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