Crystal structure representations for machine learning models of formation energies-Reference-Cited by-同舟云学术

Crystal structure representations for machine learning models of formation energies

Published:2015-04-20 Issue:16 Volume:115 Page:1094-1101
ISSN:0020-7608
Container-title:International Journal of Quantum Chemistry
language:en
Short-container-title:Int. J. Quantum Chem.

Author:

Faber Felix¹,Lindmaa Alexander²,von Lilienfeld O. Anatole¹³,Armiento Rickard²^ORCID

Affiliation:

1. Department of ChemistryInstitute of Physical Chemistry and National Center for Computational Design and Discovery of Novel MaterialsUniversity of Basel Switzerland

2. Department of PhysicsChemistry and Biology, Linköping UniversitySE‐581 83Linköping Sweden

3. Argonne Leadership Computing FacilityArgonne National Laboratory9700 S. Cass AvenueLemont Illinois60439

Funder

U.S. Department of Energy

Vetenskapsr??det

Publisher

Wiley

Subject

Physical and Theoretical Chemistry,Condensed Matter Physics,Atomic and Molecular Physics, and Optics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/qua.24917

Reference41 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. First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties

4. How to represent crystal structures for machine learning: Towards fast prediction of electronic properties

5. Machine learning of molecular electronic properties in chemical compound space

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