Quantum-Dynamics-Hub/libra-code: Libra with the ANN; new xTB/CP2k workflows; generalized and simplified workflows; libint2 and pickling support;

Akimov, Alexey V.; Smith, B.L.; Shakiba, Mohammad Reza; Sato, Kosuke; Temen, Story; Li, Wei; Sun, Xiang; Stippell, Liz; Chan, M.; chemweili,

doi:10.5281/zenodo.5708303

2021

DOI: 10.5281/zenodo.5708303

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Quantum-Dynamics-Hub/libra-code: Libra with the ANN; new xTB/CP2k workflows; generalized and simplified workflows; libint2 and pickling support;

Alexey V. Akimov¹,

B.L. Smith²,

Mohammad Reza Shakiba³

et al.

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“…Tracking the error function of the trained ANNs shows that this is sufficient to achieve the convergence of the trained MLPs. The training data preparation, ANN training algorithms, and the NA-MD computations, both direct and ANN-based, are conducted with the Libra (version 5.1.0) software package in which all these capabilities are implemented.…”

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Extending the Time Scales of Nonadiabatic Molecular Dynamics via Machine Learning in the Time Domain

Akimov

2021

J. Phys. Chem. Lett.

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A novel methodology for direct modeling of long-time scale nonadiabatic dynamics in extended nanoscale and solid-state systems is developed. The presented approach enables forecasting the vibronic Hamiltonians as a direct function of time via machine-learning models trained directly in the time domain. The use of periodic and aperiodic functions that transform time into effective input modes of the artificial neural network is demonstrated to be essential for such an approach to work for both abstract and atomistic models. The best strategies and possible limitations pertaining to the new methodology are explored and discussed. An exemplary direct simulation of unprecedentedly long 20 picosecond trajectories is conducted for a divacancy-containing monolayer black phosphorus system, and the importance of conducting such extended simulations is demonstrated. New insights into the excited states photophysics in this system are presented, including the role of decoherence and model definition.

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Extending the Time Scales of Nonadiabatic Molecular Dynamics via Machine Learning in the Time Domain

Akimov

2021

J. Phys. Chem. Lett.

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Quantum-Dynamics-Hub/libra-code: Libra with the ANN; new xTB/CP2k workflows; generalized and simplified workflows; libint2 and pickling support;

Cited by 1 publication

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Extending the Time Scales of Nonadiabatic Molecular Dynamics via Machine Learning in the Time Domain

Extending the Time Scales of Nonadiabatic Molecular Dynamics via Machine Learning in the Time Domain

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