beyond the range of selectivities in the training
data, the models quantitatively underpredicted
these reactions. By using Keras ( 49 ) with the
Theano backend, a Python package that can
facilitate deep learning, we generated a deep
feed-forward network. Grid-based hyperparam-
eter optimization was used with linear, relu, elu,
and selu activation functions; 0.05, 0.1, and
0.2 dropouts on the layers; 4, 40, 400, and
4000 nodes per layer; and 0 to 6 hidden layers.
Further, all optimizers available in Keras were
tested. This method of hyperparameter optimi-
zation was very time intensive, and it is strongly
recommended that practitioners instead use a
Bayesian optimization of hyperparameters. At-
tempts to use this kind of optimization and
more modern machine learning methods are
currently under way.
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ACKNOWLEDGMENTS
We thank K. A. Robb and Z. Wickenhauser for experimental
assistance and N. Russell for informative discussions about
machine learning. We are also grateful for the support services of
the NMR, mass spectrometry, and microanalytical laboratories of
the University of Illinois at Urbana-Champaign.Funding:We are
grateful for generous financial support from the W. M. Keck
Foundation. A.F.Z. is grateful to the University of Illinois for
graduate fellowships. Y.W. thanks Janssen Research Development,
San Diego, CA, for a postdoctoral fellowship.Author
contributions:A.F.Z. contributed to catalyst synthesis, acquisition
of experimental selectivity data, and computer modeling and
composed the manuscript. J.J.H. contributed to creating
ccheminfolib, designing and implementing the ASO descriptors,
and revising the manuscript. B.T.R., Y.W., and W.T.D. contributed
to catalyst synthesis. S.E.D. secured funding, supervised the
project, analyzed data, and revised the manuscript.Competing
interests:The authors declare no competing interests.Data and
materials availability:Full experimental procedures,
characterization data, and copies of^1 H,^13 C,^31 P, and^19 F spectra
can be found in the supplementary materials, along with analytical
supercritical fluid chromatography traces of all products. The
computer code used in these studies is available in GitLab ( 48 ).SUPPLEMENTARY MATERIALS
http://www.sciencemag.org/content/363/6424/eaau5631/suppl/DC1
Materials and Methods
Supplementary Text
Figs. S1 to S10
Table S1
References ( 50 – 86 )
Data S1 to S3
22 June 2018; accepted 3 December 2018
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