Page 105 - Read Online
P. 105
Page 12 of 15 Wang et al. J. Mater. Inf. 2026, 6, 9
8. van der Giessen, E.; Schultz, P. A.; Bertin, N.; et al. Roadmap on multiscale materials modeling. Modell. Simul. Mater. Sci. Eng. 2020,
28, 043001. DOI
9. Botifoll, M.; Pinto-Huguet, I.; Arbiol, J. Machine learning in electron microscopy for advanced nanocharacterization: current
developments, available tools and future outlook. Nanoscale. Horiz. 2022, 7, 1427-77. DOI PubMed
10. Paier, J.; Marsman, M.; Hummer, K.; Kresse, G.; Gerber, I. C.; Angyán, J. G. Screened hybrid density functionals applied to solids. J.
Chem. Phys. 2006, 124, 154709. DOI PubMed
11. Aykol, M.; Kim, S.; Hegde, V. I.; et al. High-throughput computational design of cathode coatings for Li-ion batteries. Nat. Commun.
2016, 7, 13779. DOI PubMed PMC
12. Jang, S. H.; Tateyama, Y.; Jalem, R. High‐throughput data‐driven prediction of stable high‐performance Na‐ion sulfide solid electrolytes.
Adv. Funct. Mater. 2022, 32, 2206036. DOI
13. Benayad, A.; Diddens, D.; Heuer, A.; et al. High‐throughput experimentation and computational freeway lanes for accelerated battery
electrolyte and interface development research. Adv. Energy. Mater. 2022, 12, 2102678. DOI
14. Boyd, P. G.; Lee, Y.; Smit, B. Computational development of the nanoporous materials genome. Nat. Rev. Mater. 2017, 2,
BFnatrevmats201737. DOI
15. Lafferentz, L.; Eberhardt, V.; Dri, C.; et al. Controlling on-surface polymerization by hierarchical and substrate-directed growth. Nat.
Chem. 2012, 4, 215-20. DOI PubMed
16. Xu, D.; Zhang, Q.; Huo, X.; Wang, Y.; Yang, M. Advances in data‐assisted high‐throughput computations for material design. Mater.
Genome. Eng. Adv. 2023, 1, e11. DOI
17. Shu, Y.; Miao, N.; Li, R.; et al. Machine learning-enabled optoelectronic material discovery: a comprehensive review. J. Mater. Inf.
2025, 5, 36. DOI
18. Wang, X.; Wang, P.; Liu, X.; Wang, X.; Lu, Y.; Shen, L. Data-driven discovery of high-performance heterobilayer transition metal
dichalcogenide-based sliding ferroelectrics. ACS. Appl. Mater. Interfaces. 2025, 17, 7164-73. DOI PubMed
19. Garrity, K. F.; Bennett, J. W.; Rabe, K. M.; Vanderbilt, D. Pseudopotentials for high-throughput DFT calculations. Comput. Mater. Sci.
2014, 81, 446-52. DOI
20. Jain, A.; Hautier, G.; Moore, C. J.; et al. A high-throughput infrastructure for density functional theory calculations. Comput. Mater. Sci.
2011, 50, 2295-310. DOI
21. Calderon, C. E.; Plata, J. J.; Toher, C.; et al. The AFLOW standard for high-throughput materials science calculations. Comput. Mater.
Sci. 2015, 108, 233-8. DOI
22. Xu, Y.; Elcoro, L.; Song, Z. D.; et al. High-throughput calculations of magnetic topological materials. Nature 2020, 586, 702-7. DOI
PubMed
23. Naveed, H.; Khan, A. U.; Qiu, S.; et al. A comprehensive overview of large language models. arXiv 2023, arXiv:2307.06435. Available
online: https://doi.org/10.48550/arXiv.2307.06435 (accessed 23 January 2026).
24. Chang, Y.; Wang, X.; Wang, J.; et al. A survey on evaluation of large language models. ACM. Trans. Intell. Syst. Technol. 2024, 15,
1-45. DOI
25. Tian, S.; Jiang, X.; Wang, W.; et al. Steel design based on a large language model. Acta. Mater. 2025, 285, 120663. DOI
26. Jiang, X.; Wang, W.; Tian, S.; Wang, H.; Lookman, T.; Su, Y. Applications of natural language processing and large language models in
materials discovery. npj. Comput. Mater. 2025, 11, 1554. DOI
27. Hewitt, C.; Bishop, P.; Steiger, R. A universal modular ACTOR formalism for artificial intelligence. In Proceedings of the 3rd
international joint conference on Artificial intelligence, Stanford, CA, USA, August 20-23, 1973; Morgan Kaufmann Publishers Inc.:
San Francisco, CA, USA, 1973; Vol. 3, pp 235-45. https://www.eighty-twenty.org/files/Hewitt,%20Bishop,%20Steiger%20-%201973%
20-%20A%20universal%20modular%20ACTOR%20formalism%20for%20artificial%20intelligence.pdf (accessed 2026-01-23).
28. Zhao, W. X.; Zhou, K.; Li, J.; et al. A survey of large language models. arXiv 2023, arXiv:2303.18223. Available online: https://doi.org/
10.48550/arXiv.2303.18223 (accessed 23 January 2026).
29. Sapkota, R.; Roumeliotis, K. I.; Karkee, M. AI Agents vs. Agentic AI: a conceptual taxonomy, applications and challenges. arXiv 2025,
arXiv:2505.10468. Available online: https://doi.org/10.48550/arXiv.2505.10468 (accessed 23 January 2026).
30. Barua, S. Exploring autonomous agents through the lens of large language models: a review. arXiv 2024, arXiv:2404.04442. Available
online: https://doi.org/10.48550/arXiv.2404.04442 (accessed 23 January 2026).
31. Wu, J.; Or, C. K. Position paper: towards open complex Human-AI Agents Collaboration System for problem-solving and knowledge
management. arXiv 2025, arXiv:2505.00018. Available online: https://doi.org/10.48550/arXiv.2505.00018 (accessed 23 January 2026).
32. Fu, J.; Liu, X.; Cai, W.; Fu, H.; Shao, X. SpeLL: an agent for natural language-driven intelligent spectral modeling. J. Chem. Inf. Model.
2025, 65, 7844-50. DOI PubMed
33. Huang, H.; Shi, X.; Lei, H.; Hu, F.; Cai, Y. ProtChat: an AI multi-agent for automated protein analysis leveraging GPT-4 and protein
language model. J. Chem. Inf. Model. 2025, 65, 62-70. DOI PubMed

