Machine learning enables long time scale molecular photodynamics simulations

By Julia Westermayr, Michael Gastegger, M. Menger, L. González, Philipp, Marquetand. 2018

99 citations recorded by the source corpus.

Machine learning enables excited-state molecular dynamics simulations including nonadiabatic couplings on nanosecond time scales.

Subfields: Atomic and Molecular Physics, and Optics, Materials Chemistry

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In Atomic and Molecular Physics, and Optics

1. Alán Aspuru-Guzik
2. Jonny Proppe
3. Pascal Friederich
4. Florian Hase

In Materials Chemistry

1. Florian Hase
2. Pascal Friederich
3. Jonny Proppe
4. Alán Aspuru-Guzik
5. José Miguel Hernández-Lobato
6. Ryan-Rhys Griffiths
7. Tommi S. Jaakkola
8. Nicholas J. Schork

Citations in the corpus

• Machine-learned potentials for next-generation matter simulations (2021, methodology): “While Westermayr et al. predict non-adiabatic couplings and spin–orbit couplings close to conical intersections 51 , Hu et al. switch from ML potentials to ab initio methods in the vicinity of conical intersections 137 .”