Large model-driven, human-computer collaborative robotic AI-chemist cloud facility

Authors

Yuanyuan CHONG, Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei 230026, ChinaFollow
Shuo FENG, Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei 230026, China
Song WANG, Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei 230026, China
Jun JIANG, Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei 230026, ChinaFollow

Keywords

paradigm shift in chemical research, robotic AI-chemist cloud facility, artificial intelligence, automated experiments, human-computer collaboration

Document Type

Vigorously Promote Scientific Research Paradigm Transform

Abstract

At present, chemical science is facing unprecedented opportunities and challenges due to the technological changes brought by artificial intelligence. In order to promote the paradigm shift in chemical research, this study proposes the construction plan of the robotic AI-chemist cloud facility. This system realizes a new paradigm of scientific research by collecting multi-channel data to build a database, developing large scientific models enhanced by chemical knowledge, constructing clusters of robotic facilities, and building an intelligent management decision system, which will dramatically improve the efficiency of scientific research and solve scientific problems in terminal applications. This infrastructure is expected to change the paradigm of research and lead to major scientific breakthroughs in chemistry.

First page

41

Last Page

49

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

References

1 Li W, Li Z, Zhang H, et al. Efficient catalysts of surface hydrophobic Cu-BTC with coordinatively unsaturated Cu(I) sites for the direct oxidation of methane. PNAS, 2023, 120(10): e2206619120.

2 Dereka B, Yu Q, Lewis N H C, et al. Crossover from hydrogen to chemical bonding. Science, 2021, 371: 160-164.

3 Majerle A, Hadži S, Aupič J, et al. A nanobody toolbox targeting dimeric coiled-coil modules for functionalization of designed protein origami structures. PNAS, 2021, 118(17): e2021899118.

4 Mou T, Pillai H S, Wang S, et al. Bridging the complexity gap in computational heterogeneous catalysis with machine learning. Nature Catalysis, 2023, 6(2): 122-136.

5 Yang J, Huang Y, Qi H, et al. Modulating the strong metal-support interaction of single-atom catalysts via vicinal structure decoration. Nature Communications, 2022, 13(1): 4244.

6 Sun Y, Dai S. High-entropy materials for catalysis: A new frontier. Science Advances, 2021, 7(20): eabg1600.

7 Rai S K, Khanna R, Avni A, et al. Heterotypic electrostatic interactions control complex phase separation of tau and prion into multiphasic condensates and co-aggregates. PNAS, 2023, 120(2): e2216338120.

8 Liang X, Fu H, Crosby A J. Phase-transforming metamaterial with magnetic interactions. PNAS, 2022, 119(1): e2118161119.

9 Li Y M, Yuan J, Ren H, et al. Fine-tuning the micro-environment to optimize the catalytic activity of enzymes immobilized in multivariate metal-organic frameworks. Journal of the American Chemical Society, 2021, 143(37): 15378-15390.

10 Tang T C, An B, Huang Y, et al. Materials design by synthetic biology. Nature Reviews Materials, 2021, 6(4): 332-350.

11 Hirschi S, Ward T R, Meier W P, et al. Synthetic biology: Bottom-up assembly of molecular systems. Chemical Reviews, 2022, 122(21): 16294-16328.

12 Jousset A, Eisenhauer N, Merker M, et al. High functional diversity stimulates diversification in experimental microbial communities. Science Advances, 2016, 2(6): e1600124.

13 Sun Y, Latora V. The evolution of knowledge within and across fields in modern physics. Scientific Reports 2020, 10(1): 12097.

14 E W N. The Dawning of a new era in applied mathematics. Notices of the American Mathematical Society, 2021, 68(4): 121-130.

15 Schmidt M, Lipson H. Distilling free-form natural laws from experimental data. Science, 2009, 324: 81-85.

16 Silver D, Huang A, Maddison C J, et al. Mastering the game of Go with deep neural networks and tree search. Nature, 2016, 529: 484-489.

17 Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature, 2021, 596, 583-589.

18 Mehr S H M, Craven M, Leonov A I, et al. A universal system for digitization and automatic execution of the chemical synthesis literature. Science, 2020, 370: 101-108.

19 Burger B, Maffettone P M, Gusev V V, et al. A mobile robotic chemist. Nature, 2020, 583, 237-241.

20 Zhu Q, Zhang F, Huang Y, et al. An all-round AI-chemist with a scientific mind. National Science Review, 2022, 9(10): nwac190.

Recommended Citation

CHONG, Yuanyuan; FENG, Shuo; WANG, Song; and JIANG, Jun (2024) "Large model-driven, human-computer collaborative robotic AI-chemist cloud facility," Bulletin of Chinese Academy of Sciences (Chinese Version): Vol. 39 : Iss. 1 , Article 6.
DOI: https://doi.org/10.16418/j.issn.1000-3045.20231205003
Available at: https://bulletinofcas.researchcommons.org/journal/vol39/iss1/6