## Samenvatting

We present a Kernel Ridge Regression (KRR) based supervised learning method combined with Genetic Algorithms (GAs) for the calculation of quasiparticle energies within Many-Body Green’s Functions Theory. These energies representing electronic excitations of a material are solutions to a set of non-linear equations, containing the electron self-energy (SE) in the GW approximation. Due to the

frequency-dependence of this SE, standard approaches are computationally expensive and may yield non-physical solutions, in particular for larger systems.

In our proposed model, we use KRR as a self-adaptive surrogate model which reduces the number of explicit calculations of the SE. Transforming the standard fixed-point problem of finding quasiparticle energies into a global optimization problem with a suitably defined fitness function, application of the GA yields uniquely the physically relevant solution. We demonstrate the applicability of our method for a set of molecules from the GW100 dataset, which are known to exhibit a particularly problematic structure of the SE. Results of the KRR-GA model agree within less than 0.01 eV with the reference standard implementation, while reducing the number of required SE evaluations roughly by a factor of ten.

frequency-dependence of this SE, standard approaches are computationally expensive and may yield non-physical solutions, in particular for larger systems.

In our proposed model, we use KRR as a self-adaptive surrogate model which reduces the number of explicit calculations of the SE. Transforming the standard fixed-point problem of finding quasiparticle energies into a global optimization problem with a suitably defined fitness function, application of the GA yields uniquely the physically relevant solution. We demonstrate the applicability of our method for a set of molecules from the GW100 dataset, which are known to exhibit a particularly problematic structure of the SE. Results of the KRR-GA model agree within less than 0.01 eV with the reference standard implementation, while reducing the number of required SE evaluations roughly by a factor of ten.

Originele taal-2 | Engels |
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Status | Gepubliceerd - 2020 |

Evenement | NeurIPS 2020 workshop on Machine Learning for Molecules - Duur: 12 dec. 2020 → … https://ml4molecules.github.io/ |

### Workshop

Workshop | NeurIPS 2020 workshop on Machine Learning for Molecules |
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Periode | 12/12/20 → … |

Internet adres |