Page 138 - Read Online
P. 138
Page 30 of 31 Li et al. J. Mater. Inf. 2026, 6, 10
114. Chang, M.; Ament, S.; Amsler, M.; et al. Probabilistic phase labeling and lattice refinement for autonomous materials research. npj.
Comput. Mater. 2025, 11, 1627. DOI
115. Luo, Y.; Wang, B.; Smeets, S.; Sun, J.; Yang, W.; Zou, X. High-throughput phase elucidation of polycrystalline materials using serial
rotation electron diffraction. Nat. Chem. 2023, 15, 483-90. DOI PubMed PMC
116. Kalinin, S. V.; Mukherjee, D.; Roccapriore, K.; et al. Machine learning for automated experimentation in scanning transmission electron
microscopy. npj. Comput. Mater. 2023, 9, 1142. DOI
117. Dave, A.; Mitchell, J.; Burke, S.; Lin, H.; Whitacre, J.; Viswanathan, V. Autonomous optimization of non-aqueous Li-ion battery
electrolytes via robotic experimentation and machine learning coupling. Nat. Commun. 2022, 13, 5454. DOI PubMed PMC
118. Siemenn, A. E.; Das, B.; Ji, K.; Sheng, F.; Buonassisi, T. A self-supervised robotic system for autonomous contact-based spatial
mapping of semiconductor properties. Sci. Adv. 2025, 11, eadw7071. DOI PubMed PMC
119. Huang, K.; Kain, C.; Diaz-vallejo, N.; Sohn, Y.; Zhou, L. High throughput mechanical testing platform and application in metal additive
manufacturing and process optimization. J. Manuf. Process. 2021, 66, 494-505. DOI
120. Prince, M. H.; Chan, H.; Vriza, A.; et al. Opportunities for retrieval and tool augmented large language models in scientific facilities. npj.
Comput. Mater. 2024, 10, 251. DOI
121. Vriza, A.; Prince, M. H.; Zhou, T.; Chan, H.; Cherukara, M. J. Operating advanced scientific instruments with AI agents that learn on the
job. arXiv 2025, arXiv:2509.00098. Available online: https://doi.org/10.48550/arXiv.2509.00098 (accessed 22 January 2026).
122. Wang, Q.; Yang, F.; Wang, Y.; et al. Unraveling the complexity of divalent hydride electrolytes in solid-state batteries via a data-driven
framework with large language model. Angew. Chem. Int. Ed. Engl. 2025, 64, e202506573. DOI PubMed PMC
123. Yao, L.; Samantray, S.; Ghosh, A.; et al. Operationalizing serendipity: multi-agent AI workflows for enhanced materials characterization
with theory-in-the-loop. arXiv 2025, arXiv:2508.06569. Available online: https://doi.org/10.48550/arXiv.2508.06569 (accessed 22
January 2026).
124. Ding, N.; Qu, S.; Xie, L.; et al. Automating exploratory proteomics research via language models. arXiv 2024, arXiv:2411.03743.
Available online: https://doi.org/10.48550/arXiv.2411.03743 (accessed 22 January 2026).
125. Alber, S.; Chen, B.; Sun, E.; Isakova, A.A.; Wilk, A.J.; Zou, J. CellVoyager: AI CompBio agent generates new insights by autonomously
analyzing biological data. bioRxiv 2025. Available online: https://doi.org/10.1101/2025.06.03.657517 (accessed 22 January 2026).
126. Altayeb, M.; Wang, X.; Mahmoud, M. R.; Ali, Y. M.; Al-shami, H. A.; Jiang, K. AI agents for UHPC experimental design: high strength
and low cost with fewer experimental trials. Constr. Build. Mater. 2024, 416, 135206. DOI
127. Lin, J.; Zhao, D.; Lu, S.; et al. Conversational large-language-model artificial intelligence agent for accelerated synthesis of
metal-organic frameworks catalysts in olefin hydrogenation. ACS. Nano. 2025, 19, 23840-58. DOI PubMed
128. Lu, J.; Song, Z.; Zhao, Q.; et al. Generative design of functional metal complexes utilizing the internal knowledge and reasoning
capability of large language models. J. Am. Chem. Soc. 2025, 147, 32377-88. DOI PubMed
129. Burger, B.; Maffettone, P. M.; Gusev, V. V.; et al. A mobile robotic chemist. Nature 2020, 583, 237-41. DOI PubMed
130. Szymanski, N. J.; Rendy, B.; Fei, Y.; et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature 2023, 624,
86-91. DOI PubMed PMC
131. Slattery, A.; Wen, Z.; Tenblad, P.; et al. Automated self-optimization, intensification, and scale-up of photocatalysis in flow. Science
2024, 383, eadj1817. DOI PubMed
132. Boiko, D. A.; MacKnight, R.; Kline, B.; Gomes, G. Autonomous chemical research with large language models. Nature 2023, 624,
570-8. DOI PubMed PMC
133. M Bran, A.; Cox, S.; Schilter, O.; Baldassari, C.; White, A. D.; Schwaller, P. Augmenting large language models with chemistry tools.
Nat. Mach. Intell. 2024, 6, 525-35. DOI PubMed PMC
134. Chen, K.; Lu, J.; Li, J.; et al. Chemist-X: large language model-empowered agent for reaction condition recommendation in chemical
synthesis. arXiv 2023, arXiv:2311.10776. Available online: https://doi.org/10.48550/arXiv.2311.10776 (accessed 22 January 2026).
135. Zhang, Z.; Ren, Z.; Hsu, C. W.; et al. A multimodal robotic platform for multi-element electrocatalyst discovery. Nature 2025, 647,
390-6. DOI PubMed
136. Xu, J.; Moran, C. H. J.; Ghorai, A.; et al. Autonomous multi-robot synthesis and optimization of metal halide perovskite nanocrystals.
Nat. Commun. 2025, 16, 7841. DOI PubMed PMC
137. Fehlis, Y.; Crain, C.; Jensen, A.; et al. Accelerating drug discovery through agentic AI: a multi-agent approach to laboratory automation
in the DMTA cycle. arXiv 2025, arXiv:2507.09023. Available online: https://doi.org/10.48550/arXiv.2507.09023 (accessed 22 January
2026).
138. 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. DOI PubMed
139. Ruan, Y.; Lu, C.; Xu, N.; et al. An automatic end-to-end chemical synthesis development platform powered by large language models.
Nat. Commun. 2024, 15, 10160. DOI PubMed PMC

