Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics
Citation: Jingbai Li, Patrick Reiser, Benjamin R. Boswell, André Eberhard, Noah Z. Burns, Pascal Friederich, and Steven A. Lopez, "Automatic discovery of photoisomerization mechanisms with nanosecond machine learning photodynamics simulations", Chem. Sci. 2021. DOI: 10.1039/D0SC05610C
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Python Rapid
Artificial Intelligence
Ab Initio Molecular Dynamics
Developer@Jingbai Li
Northeastern University, USA
version: 0.9
With contributions from(in alphabetic order):
Andre Eberhard - Gaussian process regression
Jingbai Li/Daniel Susman - Zhu-Nakamura surface hopping
Jingbai Li - Fewest switches surface hopping
Velocity Verlet
Interface to OpenMolcas/BAGEL
Adaptive sampling (with enforcement)
QC/ML non-adiabatic molecular dynamics
Patrick Reiser - Neural networks (pyNNsMD)
Special acknowledgment to:
Steven A. Lopez - Project directorship
Pascal Friederich - ML directorship
Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics (PyRAI2MD) is a package for nonadiabatic molecular dynamics simulation using potential energy surface predicted by machine-learning models. The primary aim of this project is to leverage the present nonadiabatic molecular dynamics techniques enabling nano-second scale simulations in practical molecular systems, which cost unaffordable computational resources using high-level quantum chemical methods like complete active space self-consistent field (CASSCF) with extended multistate second-order perturbative corrections (XMS-CASPT2).
This is the legacy version. The latest version is 2.0 alpha.