Automation High-Throughput


Laboratory automation utilizes a combination of robotics, hardware, and software planning to decrease manual effort from the researcher, particularly in high-throughput testing and screening. Further combining automation platforms with ML models can work towards closed-loop systems for chemical optimization and discovery. 


  • Casetti, N., Alfonso-Ramos, J.E., Coley, C.W. and Stuyver, T., Combining Molecular Modeling and Machine Learning for Accelerated Reaction Screening and Discovery. Chemistry Europ. J., 2023 p.e202301957.

  • Christensen M, Yunker L, Adedeji F, Häse F, Roch L, Gensch T, dos Passos Gomes G, Zepel T, Sigman M, Aspuru-Guzik A, Hein J.  Data-science driven autonomous process optimization. Commun. Chem. 2021, 4 112.

  • Raghavan, P.; Haas, B.C.; Ruos, M.E.; Schleinitz, J.; Doyle, A.G.; Reisman, S.E.; Sigman, M.S.; Coley, C.W.  Dataset Design for Building Models of Chemical Reactivity. ACS Cent. Sci 2023, 9, ASAP

  • Maloney MP, Coley CW, Genheden S, Carson N, Helquist P, Norrby PO, Wiest O. Negative Data in Data Sets for Machine Learning Training. Organic Letters. 2023, ;25, 2945-2947 Published in parallel in J. Org. Chem. 2023, 88, 5239–5241

  • Żurański, A.M., Martinez Alvarado, J.I., Shields, B.J. and Doyle, A.G. 2021.  Predicting Reaction Yields via Supervised Learning. Acc. Chem. Res. 2021, 54, 1856-865.

  • Williams, W.L.; Zeng, L.; Gensch, T.; Sigman, M.S.; Doyle, A.G.; Anslyn, E. V. The Evolution of Data-Driven Modeling in Organic Chemistry.  ACS Cent. Sci. 2021, 7, 1622-1637.