Coscientist系统生成的代码,从方法定义到执行反应、关闭模块。图片来源:Nature 不得不感慨,AI机器人已经要成精了。最后的最后,要是有一个能替我讲组会的AI机器人,我真的会谢!不说了,这就去彻底躺平了…… 原文(扫描或长按二维码,识别后直达原文页面):Autonomous chemical research with large language modelsDaniil A. Boiko, Robert MacKnight, Ben Kline & Gabe Gomes Nature, 2023, 624, 570-578. DOI: 10.1038/s41586-023-06792-0 参考文献:[1] M. H. S. Segler, et al., Planning chemical syntheses with deep neural networks and symbolic AI. Nature 2018, 555, 604-610. DOI: 10.1038/nature25978[2] B. Mikulak-Klucznik, et al., Computational planning of the synthesis of complex natural products. Nature 2020, 588, 83-88. DOI: 10.1038/s41586-020-2855-y[3] X. Zhang, et al., Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. 2023, arXiv:2307.08423 [4] L. Pattanaik, et al., Generating transition states of isomerization reactions with deep learning. Phys. Chem. Chem. Phys. 2020, 22, 23618-23626. DOI: 10.1039/D0CP04670A[5] R. Jackson, et al., TSNet: predicting transition state structures with tensor field networks and transfer learning. Chem. Sci. 2021, 12, 10022-10040. DOI: 10.1039/D1SC01206A[6] S. Choi, Prediction of transition state structures of gas-phase chemical reactions via machine learning. Nat. Commun.2023, 14, 1168. DOI: 10.1038/s41467-023-36823-3[7] J. Jumper, et al., Highly accurate protein structure prediction with AlphaFold. Nature 2021, 596, 583-589. DOI: 10.1038/s41586-021-03819-2[8] M. Baek, et al., Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA. Nat. Methods2023, DOI: 10.1038/s41592-023-02086-5[9] Z. Lin, et al., Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 2023, 379, 1123-1130. DOI: 10.1126/science.ade2574[10] Computational model captures the elusive transition states of chemical reactionshttps://news.mit.edu/2023/computational-model-captures-elusive-transition-states-1215[11] A. Merchant, et. al., Scaling deep learning for materials discovery. Nature 2023, 624, 80-85.DOI: 10.1038/s41586-023-06735-9[12] C. Zeni, et. al., MatterGen: a generative model for inorganic materials design. 2023,arXiv:2312.03687[13] N. J. Szymanski, et. al., An autonomous laboratory for the accelerated synthesis of novel materials. Nature 2023, 624, 86-91. DOI: 10.1038/s41586-023-06734-w [14] B. Burger, et. al., A mobile robotic chemist. Nature 2020, 583, 237-241. DOI: 10.1038/s41586-020-2442-2[15] S. Hessam, A universal system for digitization and automatic execution of the chemical synthesis literature. Science 2020, 370, 101-108. DOI: 10.1126/science.abc2986[16] A. M. Bran, ChemCrow: Augmenting large-language models with chemistry tools. 2023, arXiv:2304.05376[17] Nature News: This GPT-powered robot chemist designs reactions and makes drugs — on its own. https://www.nature.com/articles/d41586-023-04073-4 (本文由小希供稿)