Fourier series of atomic radial distribution functions : A molecular fingerprint for machine learning models of quantum chemical properties

@inproceedings{Lilienfeld2015FourierSO,
  title={Fourier series of atomic radial distribution functions : A molecular fingerprint for machine learning models of quantum chemical properties},
  author={O. Anatole von Lilienfeld and Raghunathan Ramakrishnan and Matthias Rupp and Aaron Knoll},
  year={2015}
}
O. Anatole von Lilienfeld, 2, ∗ Raghunathan Ramakrishnan, Matthias Rupp, and Aaron Knoll 4 Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials, Department of Chemistry, University of Basel, Switzerland. Argonne Leadership Computing Facility, Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, IL 60439, USA Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois 60439, USA Texas Advanced Computing… CONTINUE READING

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Any ab initio method must either be void of empirical parameters , or at least have parameters that do not depend on the system being studied . ” Oral communication

  • P. Jørgensen, J. Olsen
  • Molecular Electronic - Structure Theory

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