Constant size descriptors for accurate machine learning models of molecular properties-Reference-Cited by-同舟云学术

Constant size descriptors for accurate machine learning models of molecular properties

Published:2018-06-28 Issue:24 Volume:148 Page:241718
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:The Journal of Chemical Physics

Author:

Collins Christopher R.¹,Gordon Geoffrey J.²,von Lilienfeld O. Anatole³,Yaron David J.¹

Affiliation:

1. Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA

2. Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA

3. Department of Chemistry, Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials (MARVEL), University of Basel, 4056 Basel, Switzerland

Funder

National Science Foundation

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://aip.scitation.org/doi/am-pdf/10.1063/1.5020441

Reference59 articles.

1. Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

2. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

3. Improving the Accuracy of Density-Functional Theory Calculation: The Statistical Correction Approach

4. On representing chemical environments

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