Publications
Kitano, H., Nobel Turing Challenge: creating the engine for scientific discovery. npj Syst Biol Appl 7, 29 (2021). https://doi.org/10.1038/s41540-021-00189-3
Kitano, H., Artificial Intelligence to Win the Nobel Prize and Beyond: Creating the Engine for Scientific Discovery. AI Magazine, 37(1), 39-49 (2016). https://doi.org/10.1609/aimag.v37i1.2642
The following publications are potentially relevant to the Nobel Turing Challenge.
It is listed for reference - for any new publications relevant to the challenge, please let us know.
2023
Dama, A.C., Kim, K.S., Leyva, D.M., Lunkes, A.P., Schmid, N.S., Jijakli, K. and Jensen, P.A. BacterAI maps microbial metabolism without prior knowledge. Nature Microbiology (2023). https://doi.org/10.1038/s41564-023-01376-0
Cornelio, C., Dash, S., Austel, V., Tyler, R., Josephson, T.R., Goncalves, J., Clarkson, K.L., Megiddo, N., Khadir, B.E. and Horesh, L. Combining data and theory for derivable scientific discovery with AI-Descartes. Nature Communications 14, 1777 (2023). https://doi.org/10.1038/s41467-023-37236-y
Boiko, D.A., MacKnight, R., and Gomes, G. Emergent autonomous scientific research capabilities of large language models. arXiv:2304.05332 [physics.chem-ph] (2023). https://doi.org/10.48550/arXiv.2304.05332
2022
Kanda, G.N., Tsuzuki, T., Terada, M., Sakai, N., Motozawa, N., Masuda, T., Nishida, M., Watanabe, C.T., Higashi, T., Horiguchi, S.A., Kudo, T., Kamei, M., Sunagawa, G.A., Matsukuma, K., Sakurada, T., Ozawa, Y., Takahashi, M., Takahashi, K. and Natsume, T. Robotic search for optimal cell culture in regenerative medicine. eLife 11:e77007. https://doi.org/10.7554/eLife.77007
2021
Raayoni, G., Gottlieb, S., Manor, Y., Pisha, G., Harris, Y., Mendlovic, U., Haviv, D., Hadad, Y., and Kaminer, I. Generating conjectures on fundamental constants with the Ramanujan Machine. Nature 590, 67–73 (2021). https://doi.org/10.1038/s41586-021-03229-4
Davies, A., Veličković, P., Buesing, L. et al. Advancing mathematics by guiding human intuition with AI. Nature 600, 70–74 (2021). https://doi.org/10.1038/s41586-021-04086-x
2020
Burger, B., Maffettone, P.M., Gusev, V.V., Aitchison, C.M., Bai, Y., Wang, X., Li, X., Alston, B.M., Li, B., Clowes, R., Rankin, N., Harris, B., Sprick, R.S., and Cooper, A.I. A mobile robotic chemist. Nature 583, 237–241 (2020). https://doi.org/10.1038/s41586-020-2442-2
Murari, A., Peluso, E., Lungaroni, M. et al. Data driven theory for knowledge discovery in the exact sciences with applications to thermonuclear fusion. Sci Rep 10, 19858 (2020). https://doi.org/10.1038/s41598-020-76826-4
Udrescu SM, Tegmark M. AI Feynman: A physics-inspired method for symbolic regression. Sci Adv. 2020 Apr 15;6(16):eaay2631. doi: 10.1126/sciadv.aay2631. PMID: 32426452; PMCID: PMC7159912.
2019
Coutant, A., Roper, K., Trejo-Banos, D., Bouthinon, D., Carpenter, M., Grzebyta, J., Santini, G., Soldano, H., Elati, M., Ramon, J., Rouveirol, C., Soldatova, L.N. and King, R.D. Closed-loop cycles of experiment design, execution, and learning accelerate systems biology model development in yeast. PNAS 116, 18142–18147 (2019). https://doi.org/10.1073/pnas.1900548116
2016
Raccuglia, P., Elbert, K.C., Adler, P.D.F., Falk, C., Wenny, M.B., Mollo, A., Zeller, M., Friedler, S.A., Schrier, J., and Norquist, A.J. Machine-learning-assisted materials discovery using failed experiments. Nature 533, 73–76 (2016). https://doi.org/10.1038/nature17439
2010
Sparkes, A., Aubrey, W., Byrne, E., Clare, A., Khan, M.N., Liakata, M., Markham, M., Rowland, J., Soldatova, L.N., Whelan, K.E., Young, M. and King, R.D. Towards Robot Scientists for autonomous scientific discovery. Autom Exp 2, 1 (2010). https://doi.org/10.1186/1759-4499-2-1
2009
King, R.D., Rowland, J., Oliver, S.G., Young, M., Aubrey, W., Byrne, E., Liakata, M., Markham, M., Pir, P., Soldatova, L.N., Sparkes, A., Whelan, K.E. and Clare, A. The Automation of Science. Science 324, 85-89 (2009). https://doi.org/10.1126/science.1165620