10 citations · 15 across the 4 of their papers we have counts for
4 papers
Toward Machine Learning Enhancement of Accelerated Surface Hopping with Scaled Spin--Orbit Couplings
Jakub Martinka, Mahesh Kumar Sit, Pavlo O. Dral +1
Surface hopping (SH) methods are typically employed to simulate ultrafast nonadiabatic processes, but long timescales often remain beyond their reach. To address this, accelerated…
Flexible Framework for Surface Hopping: From Hybrid Schemes for Machine Learning to Benchmarkable Nonadiabatic Dynamics
Jakub Martinka, Mikołaj Martyka, Biman Medhi +2
Nonadiabatic molecular dynamics is a key technique for investigating a broad range of photochemical and photophysical processes. Among the established approaches, surface hopping s…
A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors
Jakub Martinka, Lina Zhang, Yi-Fan Hou +4
Nonadiabatic couplings (NACs) play a crucial role in modeling photochemical and photophysical processes with methods such as the widely used fewest-switches surface hopping (FSSH).…
A simple approach to rotationally invariant machine learning of avector quantity
Jakub Martinka, Marek Pederzoli, Mario Barbatti +2
Unlike with the energy, which is a scalar property, machine learning (ML) predictions of vector or tensor properties poses the additional challenge of achieving proper invariance (…