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Machine-Learning Models for Combinatorial Catalyst Discovery

Published online by Cambridge University Press:  01 February 2011

Gregory A. Landrum ,
Julie Penzotti  and
Santosh Putta
Gregory A. Landrum
Affiliation:
Rational Discovery LLC, 555 Bryant St. #467, Palo Alto, CA 94301, USA
Julie Penzotti
Affiliation:
Rational Discovery LLC, 555 Bryant St. #467, Palo Alto, CA 94301, USA
Santosh Putta
Affiliation:
Rational Discovery LLC, 555 Bryant St. #467, Palo Alto, CA 94301, USA
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Abstract

Standard machine-learning algorithms were used to build models capable of predicting the molecular weights of polymers generated by a homogeneous catalyst. Using descriptors calculated from only the two-dimensional structures of the ligands, the average accuracy of the models on an external validation data set was approximately 70%. Because the models show no bias and perform significantly better than equivalent models built using randomized data, we conclude that they learned useful rules and did not overfit the data.

Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 804: Symposium JJ – Combinatorial and Artificial Intelligence Methods in Materials Science II , 2003 , JJ11.5
Copyright
Copyright © Materials Research Society 2004

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