Bias-Free Chemically Diverse Test Sets from Machine Learning-Reference-Cited by-同舟云学术

Bias-Free Chemically Diverse Test Sets from Machine Learning

Published:2017-07-27 Issue:8 Volume:19 Page:544-554
ISSN:2156-8952
Container-title:ACS Combinatorial Science
language:en
Short-container-title:ACS Comb. Sci.

Author:

Swann Ellen T.¹^ORCID,Fernandez Michael¹,Coote Michelle L.²^ORCID,Barnard Amanda S.¹

Affiliation:

1. Data61 CSIRO, Molecular & Materials Modelling, Door 34, Goods Shed, Village Street, Docklands, Victoria 3008, Australia

2. ARC Centre of Excellence for Electromaterials Science, Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory 2601, Australia

Funder

Commonwealth Scientific and Industrial Research Organisation

Publisher

American Chemical Society (ACS)

Subject

General Chemistry,General Medicine

Link

https://pubs.acs.org/doi/pdf/10.1021/acscombsci.7b00087

Reference101 articles.

1. Gaussian‐1 theory of molecular energies for second‐row compounds

2. Gaussian‐2 theory for molecular energies of first‐ and second‐row compounds

3. Assessment of Gaussian-2 and density functional theories for the computation of enthalpies of formation

4. Assessment of Gaussian-2 and density functional theories for the computation of ionization potentials and electron affinities

5. Assessment of Gaussian-3 and density functional theories for a larger experimental test set

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