Large language model in electrocatalysis

doi:10.1016/S1872-2067(23)64612-1

Abstract

Abstract:

Large language models have recently brought a massive storm on modern society in all fields. While many view them as mere search engines for specific answers or text refinement tools like a chatbot, their broader applications remain largely unexplored. These large language models, consisting of billions of interconnected neurons, derived from all knowledge of the human, possess the remarkable ability to engage in smooth and precise conversations with individuals across the globe. Human-like intelligence enables them to address modern challenges and display immense potential in various scientific domains. In this perspective, we delve into the potential applications of modern large language model and its future iterations within the field of catalysis, aiming to shed light on how these AI-driven models can contribute to a deeper understanding of catalysis science and the intelligent design of catalysts.

Key words: Large language model, Electrocatalysis, Artificial intelligence, Multimodal large language model

Chengyi Zhang, Xingyu Wang, Ziyun Wang. Large language model in electrocatalysis[J]. Chinese Journal of Catalysis, 2024, 59: 7-14.

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URL: https://www.cjcatal.com/EN/10.1016/S1872-2067(23)64612-1

https://www.cjcatal.com/EN/Y2024/V59/I4/7

Figures/Tables 2

Fig. 1. The potential applications of LLM in catalysis research. Including comprehensive guidance for designing catalysts for various catalytic reactions (OER, HER, CO2RR, and NRR), facilitating the understanding of reaction mechanisms, assisting in extracting key data from electrochemical tests, compiling experimental reports based on previous experimental results, and improving demonstration schemes based on experimental outcomes to guide experimental design.

Fig. 2. The various types of inputs and outputs in the future MLLM in catalysis. Including the combination of the graphs and language input of the spectroscopy analysis results, combinations of various figures and descriptions of previous literature for the experiments reports, and combinations of characterization data and experiments input for problem diagnosis for further improvement.

References

[1]	Z. W. Seh, J. Kibsgaard, C. F. Dickens, I. Chorkendorff, J. K. Norskov, T. F. Jaramillo, Science, 2017, 355, eaad4998.
[2]	W. Kohn, L. J. Sham, Phys. Rev., 1965, 140, A1133-A1138.
[3]	I. A. Hamad, M. A. Novotny, D. O. Wipf, P. A. Rikvold, Phys. Chem. Chem. Phys., 2010, 12, 2740-2743.
[4]	R. Car, M. Parrinello, Phys. Rev. Lett., 1985, 55, 2471-2474.
[5]	R. E. Rudd, J. Q. Broughton, Phys. Rev. B, 1998, 58, R5893-R5896.
[6]	T. Abe, J. Comput. Phys., 1997, 131, 241-246.
[7]	L. Börgesson, in: Developments in Geotechnical Engineering, O. Stephansson, L. Jing, C.-F. Tsang eds., Elsevier, Netherlands, 1996, 565-570
[8]	F. Liu, T. Zhu, X. Wu, B. Yang, C. You, C. Wang, L. Lu, Z. Liu, Y. Zheng, X. Sun, Y. Yang, L. Clifton, D. A. Clifton, NPJ Digit. Med., 2023, 6, 226.
[9]	L. De Angelis, F. Baglivo, G. Arzilli, G. P. Privitera, P. Ferragina, A. E. Tozzi, C. Rizzo, Front. Public Health, 2023, 11, 1166120.
[10]	R. Luo, L. Sun, Y. Xia, T. Qin, S. Zhang, H. Poon, T. Y. Liu, Brief. Bioinformatics, 2022, 23, 10.1093/bib/bbac409.
[11]	H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, A. Rodriguez, A. Joulin, E. Grave, G. Lample, arXiv preprint arXiv:2302.13971, 2023.
[12]	H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale, D. Bikel, L. Blecher, C. C. Ferrer, M. Chen, G. Cucurull, D. Esiobu, J. Fernandes, J. Fu, W. Fu, B. Fuller, C. Gao, V. Goswami, N. Goyal, A. Hartshorn, S. Hosseini, R. Hou, H. Inan, M. Kardas, V. Kerkez, M. Khabsa, I. Kloumann, A. Korenev, P. S. Koura, M.-A. Lachaux, T. Lavril, J. Lee, D. Liskovich, Y. Lu, Y. Mao, X. Martinet, T. Mihaylov, P. Mishra, I. Molybog, Y. Nie, A. Poulton, J. Reizenstein, R. Rungta, K. Saladi, A. Schelten, R. Silva, E. M. Smith, R. Subramanian, X. E. Tan, B. Tang, R. Taylor, A. Williams, J. X. Kuan, P. Xu, Z. Yan, I. Zarov, Y. Zhang, A. Fan, M. K., S. Narang, A. Rodriguez, R. Stojnic, S. Edunov, T. Scialom, arXiv preprint arXiv:2307.09288, 2023.
[13]	P. Perniss, Front. Psychol., 2018, 9, 1109-1109.
[14]	C. Stokel-Walker, R. Van Noorden, Nature, 2023, 614, 214-216.
[15]	Z. Zheng, Z. Rong, N. Rampal, C. Borgs, J. T. Chayes, O. M. Yaghi, Angew. Chem. Int. Ed., 2023, 62, e202311983.
[16]	Z. Wang, W. Liu, Q. He, X. Wu, Z. Yi, arXiv preprint arXiv:2203.00386, 2022.
[17]	O. Topsakal, T. C. Akinci, Creating large language model applications utilizing langchain: A primer on developing llm apps fast, Proceedings of the International Conference on Applied Engineering and Natural Sciences, Konya, Turkey, 2023, 1, 1050-1056.
[18]	M. Liu, A. Grinberg Dana, M. S. Johnson, M. J. Goldman, A. Jocher, A. M. Payne, C. A. Grambow, K. Han, N. W. Yee, E. J. Mazeau, K. Blondal, R. H. West, C. F. Goldsmith, W. H. Green, J. Chem. Inf. Model., 2021, 61, 2686-2696.
[19]	T. Weiss, E. Mayo Yanes, S. Chakraborty, L. Cosmo, A. M. Bronstein, R. Gershoni-Poranne, Nat. Comput. Sci., 2023, 3, 873-882.
[20]	P. K. Rubenstein, C. Asawaroengchai, D. D. Nguyen, A. Bapna, Z. Borsos, F. d. C. Quitry, P. Chen, D. E. Badawy, W. Han, E. Kharitonov, H. Muckenhirn, D. Padfield, J. Qin, D. Rozenberg, T. Sainath, J. Schalkwyk, M. Sharifi, M. T. Ramanovich, M. Tagliasacchi, A. Tudor, M. Velimirović, D. Vincent, J. Yu, Y. Wang, V. Zayats, N. Zeghidour, Y. Zhang, Z. Zhang, L. Zilka, C. Frank, arXiv preprint arXiv:2306.12925, 2023.