70 citations · 171 across the 11 of their papers we have counts for
11 papers
Perspective on a challenge: predicting the photochemistry of cyclobutanone
Jiří Janoš, Nanna Holmgaard List, Andrew J. Orr-Ewing +37
This Perspective is part of a Special Topic that explored the maturity of nonadiabatic molecular dynamics for predicting photochemical processes. In 2023, a prediction challenge wa…
milearn: A Python Package for Multi-Instance Machine Learning
Dmitry Zankov, Pavlo Polishchuk, Michal Sobieraj +1
We introduce milearn, a Python package for multi-instance learning (MIL) that follows the familiar scikit-learn fit/predict interface while providing a unified framework for both c…
Ehrenfest Dynamics with Spontaneous Localization
Anderson A. Tomaz, Rafael S. Mattos, Saikat Mukherjee +1
We propose Ehrenfest Dynamics with Spontaneous Localization (SLED), a decoherence-corrected extension of Ehrenfest dynamics based on the Gisin-Percival quantum-state diffusion (QSD…
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).…
The Quantum Measurement Problem: A Review of Recent Trends
Anderson A. Tomaz, Rafael S. Mattos, Mario Barbatti
Left on its own, a quantum state evolves deterministically under the Schrödinger Equation, forming superpositions. Upon measurement, however, a stochastic process governed by the B…
Roadmap for Molecular Benchmarks in Nonadiabatic Dynamics
Léon E. Cigrang, Basile F. E. Curchod, Rebecca A. Ingle +42
Simulating the coupled electronic and nuclear response of a molecule to light excitation requires the application of nonadiabatic molecular dynamics. However, when faced with a spe…
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 (…
MLatom software ecosystem for surface hopping dynamics in Python with quantum mechanical and machine learning methods
Lina Zhang, Sebastian V. Pios, Mikołaj Martyka +7
We present an open-source MLatom@XACS software ecosystem for on-the-fly surface hopping nonadiabatic dynamics based on the Landau-Zener-Belyaev-Lebedev (LZBL) algorithm. The dynami…
Prediction Challenge: Simulating Rydberg Photoexcited Cyclobutanone with Surface Hopping Dynamics based on Different Electronic Structure Methods
Saikat Mukherjee, Rafael S. Mattos, Josene M. Toldo +2
This research examines the nonadiabatic dynamics of cyclobutanone after excitation into the n-3s Rydberg S2 state. It stems from our contribution to the Special Topic of the Journa…
MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows
Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou +15
Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framewor…
Probing the electronic structure and photophysics of thiophene-diketopyrrolopyrrole derivatives in solution
Daniel W Polak, Mariana T do Casal, Josene M Toldo +7
Diketopyrrolopyrroles are a popular class of electron-withdrawing unit in optoelectronic materials. When combined with electron donating side-chain functional groups such as thioph…