Skoraczyński, G. et al. Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient? Sci. Rep. 7, 3582 (2017).
Saebi, M. et al. On the use of real-world datasets for reaction yield prediction. Chem. Sci. 14, 4997–5005 (2023).
Liu, Z., Moroz, Y. S. & Isayev, O. The challenge of balancing model sensitivity and robustness in predicting yields: a benchmarking study of amide coupling reactions. Chem. Sci. 14, 10835–10846 (2023).
Szymkuć, S., Wołos, A., Roszak, R. & Grzybowski, B. A. Estimation of multicomponent reactions’ yields from networks of mechanistic steps. Nat. Commun. 15, 10286 (2024).
Restrepo, G. Spaces of mathematical chemistry. Theory Biosci. 143, 237–251 (2024).
Granda, J. M., Donina, L., Dragone, V., Long, D. L. & Cronin, L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature 559, 377–381 (2018).
Lin, S. et al. Mapping the dark space of chemical reactions with extended nanomole synthesis and MALDI-TOF MS. Science 361, eaar6236 (2018).
Coley, C. W. et al. A robotic platform for flow synthesis of organic compounds informed by AI planning. Science 365, eaax1566 (2019).
Angello, N. H. et al. Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling. Science 378, 399–405 (2022).
Rohrbach, S. et al. Digitization and validation of a chemical synthesis literature database in the ChemPU. Science 377, 172–180 (2022).
Slattery, A. et al. Automated self-optimization, intensification, and scale-up of photocatalysis in flow. Science 383, eadj1817 (2024).
Dai, T. et al. Autonomous mobile robots for exploratory synthetic chemistry. Nature 635, 890–897 (2024).
Buitrago Santanilla, A. et al. Nanomole-scale high-throughput chemistry for the synthesis of complex molecules. Science 347, 49–53 (2015).
Davies, I. W. The digitization of organic synthesis. Nature 570, 175–181 (2019).
Shields, B. J. et al. Bayesian reaction optimization as a tool for chemical synthesis. Nature 590, 89–96 (2021).
Wilbraham, L., Mehr, S. H. M. & Cronin, L. Digitizing chemistry using the chemical processing unit: from synthesis to discovery. Acc. Chem. Res. 54, 253–262 (2021).
Mahjour, B. et al. Rapid planning and analysis of high-throughput experiment arrays for reaction discovery. Nat. Commun. 14, 3924 (2023).
Wang, J. Y. et al. Identifying general reaction conditions by bandit optimization. Nature 626, 1025–1033 (2024).
Strieth-Kalthoff, F. et al. Artificial intelligence for retrosynthetic planning needs both data and expert knowledge. J. Am. Chem. Soc. 146, 11005–11017 (2024).
Stadler, E. et al. A versatile method for the determination of photochemical quantum yields via online UV-Vis spectroscopy. Photochem. Photobiol. Sci. 17, 660–669 (2018).
Lu, J.-M. et al. Roboticized AI-assisted microfluidic photocatalytic synthesis and screening up to 10,000 reactions per day. Nat. Commun. 15, 8826 (2024).
Bioucas-Dias, J. M. et al. Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5, 354–379 (2012).
Banert, K. & Kurnianto, A. Nucleophile substitution bei 4,4-dimethyl-2-adamantyl-substraten: rückseitenangriff bei 2-adamantan-derivaten. Chem. Ber. 119, 3826–3841 (1986).
Thibblin, A. & Sidhu, H. Mechanisms of competing solvolytic elimination and substitution reactions. The role of ion-pair intermediates in aqueous solvents. J. Chem. Soc., Perkin Trans. 2 2, 1423–1428 (1994).
Clennan, E. L. Aromatic endoperoxides. Photochem. Photobiol. 99, 204–220 (2023).
Klaper, M., Wessig, P. & Linker, T. Base catalysed decomposition of anthracene endoperoxide. Chem. Commun. 52, 1210–1213 (2016).
Ammer, J., Sailer, C. F., Riedle, E. & Mayr, H. Photolytic generation of benzhydryl cations and radicals from quaternary phosphonium salts: how highly reactive carbocations survive their first nanoseconds. J. Am. Chem. Soc. 134, 11481–11494 (2012).
Nishimae, Y., Kurata, H. & Oda, M. Arylbis(9-anthryl)methyl cations: highly crowded, near infrared light absorbing hydrocarbon cations. Angew. Chem. Int. Ed. 43, 4947–4950 (2004).
Nojima, M., Takagi, M., Morinaga, M., Nagao, G. & Tokura, N. Reaction of some triarylmethyl radicals, polyarylalkenes, and 9,10-dihydro-9,10-epidioxyanthracenes with sulphur dioxide; detection of radicals and/or cations derived from the corresponding cation radicals. J. Chem. Soc. Perkin Trans. 1 5, 488–495 (1978).
Hollenstein, S. & Laali, K. K. Efficient conversion of 9-isopropenylphenanthrene to 4,6,6-trimethyl-6H-benz[de]anthracene in FSO3H; 5,6-dihydro-4H-benzanthracen-4-ium ion and its charge delocalization mode. Chem. Commun. 2145–2146 (1997).
Cankařová, N., Nemec, I. & Krchňák, V. p-TSA-mediated four-component reaction: one-step access to mesoionic 1H-imidazol-3-ium-4-olates, direct NHC precursors. Adv. Synth. Catal. 364, 2996–3003 (2022).
Medeiros, G. A. et al. Probing the mechanism of the Ugi four-component reaction with charge-tagged reagents by ESI-MS(/MS). Chem. Commun. 50, 338–340 (2014).
Rocha, R. O., Rodrigues, M. O. & Neto, B. A. D. Review on the Ugi multicomponent reaction mechanism and the use of fluorescent derivatives as functional chromophores. ACS Omega 5, 972–979 (2020).
Alvim, H. G. O., da Silva Júnior, E. N. & Neto, B. A. D. What do we know about multicomponent reactions? Mechanisms and trends for the Biginelli, Hantzsch, Mannich, Passerini and Ugi MCRs. RSC Adv. 4, 54282–54299 (2014).
Chéron, N., Ramozzi, R., Kaïm, L. E., Grimaud, L. & Fleurat-Lessard, P. Challenging 50 years of established views on Ugi reaction: a theoretical approach. J. Org. Chem. 77, 1361–1366 (2012).
Hantzsch, A. Condensationsprodukte aus Aldehydammoniak und ketonartigen Verbindungen. Ber. Dtsch. Chem. Ges. 14, 1637–1638 (1881).
Shen, L. et al. A revisit to the Hantzsch reaction: unexpected products beyond 1,4-dihydropyridines. Green Chem. 11, 1414–1420 (2009).
Santos, V. G. et al. The multicomponent Hantzsch reaction: comprehensive mass spectrometry monitoring using charge-tagged reagents. Chem. Eur. J. 20, 12808–12816 (2014).
Chang, C.-C. et al. Antagonism of 4-substituted 1,4-dihydropyridine-3,5-dicarboxylates toward voltage-dependent L-type Ca2+ channels CaV1.3 and CaV1.2. Bioorg. Med. Chem. 18, 3147–3158 (2010).
Petrenko-Kritschenko, P. Über die kondensation des acetondicarbonsäureesters mit aldehyden, ammoniak und aminen. J. Prakt. Chem. 85, 1–37 (1912).
Singh, B. & Indra, A. Prussian blue- and Prussian blue analogue-derived materials: progress and prospects for electrochemical energy conversion. Mater. Today Energy 16, 100404 (2020).
Li, W. et al. Chemical properties, structural properties, and energy storage applications of Prussian blue analogues. Small 15, 1900470 (2019).
Choo, J. P. S. & Li, Z. Styrene oxide isomerase catalyzed Meinwald rearrangement reaction: discovery and application in single-step and one-pot cascade reactions. Org. Process Res. Dev. 26, 1960–1970 (2022).
Guo, S. et al. Synthesis of trimetallic Prussian blue analogues and catalytic application for the epoxidation of styrene. Ind. Eng. Chem. Res. 59, 13831–13840 (2020).
Liang, Y. et al. Prussian blue analogues as heterogeneous catalysts for epoxidation of styrene. RSC Adv. 5, 17993–17999 (2015).
Zhang, L., Zhang, Z., He, X., Zhang, F. & Zhang, Z. Regulation of the products of styrene oxidation. Chem. Eng. Res. Des. 120, 171–178 (2017).
Pal, A. et al. Finding thermodynamically favorable pathways in chemical reaction networks using flows in hypergraphs and mixed-integer linear programming. J. Chem. Inf. Model. 65, 6772–6787 (2025).
Grzybowski, B. A., Bishop, K. J. M., Kowalczyk, B. & Wilmer, C. E. The ‘wired’ universe of organic chemistry. Nat. Chem. 1, 31–36 (2009).
Krzeszewski, M. et al. Computer-generated, mechanistic networks assist in assigning the outcomes of complex multicomponent reactions. J. Am. Chem. Soc. 147, 15636–15644 (2025).
Mikulak-Klucznik, B., Klucznik, T., Beker, W., Moskal, M. & Grzybowski, B. A. Catalyst: curtailing the scalable supply of fentanyl by using chemical AI. Chem 10, 1319–1326 (2024).
Mahjour, B., Shen, Y., Liu, W. & Cernak, T. A map of the amine–carboxylic acid coupling system. Nature 580, 71–75 (2020).
Baltussen, M. G., de Jong, T. J., Duez, Q., Robinson, W. E. & Huck, W. T. S. Chemical reservoir computation in a self-organizing reaction network. Nature 631, 549–555 (2024).
Seelig, G., Soloveichik, D., Zhang, D. Y. & Winfree, E. Enzyme-free nucleic acid logic circuits. Science 314, 1585–1588 (2006).
Daniel, R., Rubens, J. R., Sarpeshkar, R. & Lu, T. K. Synthetic analog computation in living cells. Nature 497, 619–623 (2013).
Wołos, A. et al. Computer-designed repurposing of chemical wastes into drugs. Nature 604, 668–676 (2022).
Halder, J. et al. Insight of solvent effect on CeO2 catalyzed oxidation of styrene with tert-butyl hydroperoxide: a combined experimental and theoretical approach. Catal. Commun. 164, 106413 (2022).
Jia, Y. et al. Code and raw data for ‘Robot-assisted mapping of chemical reaction hyperspaces and networks’. Zenodo https://doi.org/10.5281/zenodo.14880579 (2025).
Schroeder, W., Martin, K. & Lorensen, B. The Visualization Toolkit: An Object-oriented Approach to 3D Graphics 4th edn (Kitware, 2006).
Goodman, J. & Weare, J. Ensemble samplers with affine invariance. Commun. Appl. Math. Comput. Sci. 5, 65–80 (2010).
Branch, M. A., Coleman, T. F. & Li, Y. A subspace, interior, and conjugate gradient method for large-scale bound-constrained minimization problems. SIAM J. Sci. Comput. 21, 1–23 (1999).
Du, M. et al. High‐entropy Prussian blue analogues and their oxide family as sulfur hosts for lithium‐sulfur batteries. Angew. Chem. Int. Ed. 61, e202209350 (2022).
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