Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies-Reference-Cited by-同舟云学术

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

Published:2013-07-30 Issue:8 Volume:9 Page:3404-3419
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Hansen Katja¹,Montavon Grégoire²,Biegler Franziska²,Fazli Siamac²,Rupp Matthias³,Scheffler Matthias¹,von Lilienfeld O. Anatole⁴,Tkatchenko Alexandre¹,Müller Klaus-Robert²⁵

Affiliation:

1. Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany

2. Machine Learning Group, TU Berlin, Germany

3. Institute of Pharmaceutical Sciences, ETH Zurich, Switzerland

4. Argonne Leadership Computing Facility, Argonne National Laboratory, Lemont, Illinois, United States

5. Department of Brain and Cognitive Engineering, Korea University, Korea

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/ct400195d

Reference78 articles.

1. Montavon, G.; Rupp, M.; Gobre, V.; Vazquez-Mayagoitia, A.; Hansen, K.; Tkatchenko, A.; Müller, K.R.; von Lilienfeld, O. A.New J. Phys. 2013, accepted

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

3. Optimization of parameters for semiempirical methods V: Modification of NDDO approximations and application to 70 elements

4. Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations

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