Machine-learning-assisted materials discovery using failed experiments

Paul Raccuglia, Katherine C. Elbert, Philip D. F. Adler, Casey Falk, Malia B. Wenny, Aurelio Mollo, Matthias Zeller, Sorelle A. Friedler (), Joshua Schrier () and Alexander J. Norquist ()
Additional contact information
Paul Raccuglia: Haverford College
Katherine C. Elbert: Haverford College
Philip D. F. Adler: Haverford College
Casey Falk: Haverford College
Malia B. Wenny: Haverford College
Aurelio Mollo: Haverford College
Matthias Zeller: Purdue University
Sorelle A. Friedler: Haverford College
Joshua Schrier: Haverford College
Alexander J. Norquist: Haverford College

Nature, 2016, vol. 533, issue 7601, 73-76

Abstract: Failed chemical reactions are rarely reported, even though they could still provide information about the bounds on the reaction conditions needed for product formation; here data from such reactions are used to train a machine-learning algorithm, which is subsequently able to predict reaction outcomes with greater accuracy than human intuition.

Date: 2016
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DOI: 10.1038/nature17439

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