922 citations · 1.4k across the 25 of their papers we have counts for
8 papers · 1 filter
All-in-one foundational models learning across quantum chemical levels
Yuxinxin Chen, Pavlo O. Dral
Machine learning (ML) potentials typically target a single quantum chemical (QC) level while the ML models developed for multi-fidelity learning have not been shown to provide scal…
Molecular Quantum Chemical Data Sets and Databases for Machine Learning Potentials
Arif Ullah, Yuxinxin Chen, Pavlo O. Dral
The field of computational chemistry is increasingly leveraging machine learning (ML) potentials to predict molecular properties with high accuracy and efficiency, providing a viab…
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…
Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine
Fuchun Ge, Ran Wang, Chen Qu +6
Machine learning potentials (MLPs) are widely applied as an efficient alternative way to represent potential energy surfaces (PES) in many chemical simulations. The MLPs are often…
Physics-Informed Neural Networks and Beyond: Enforcing Physical Constraints in Quantum Dissipative Dynamics
Arif Ullah, Yu Huang, Ming Yang +1
Neural networks (NNs) accelerate simulations of quantum dissipative dynamics. Ensuring that these simulations adhere to fundamental physical laws is crucial, but has been largely i…
Physics-informed active learning for accelerating quantum chemical simulations
Yi-Fan Hou, Lina Zhang, Quanhao Zhang +2
Quantum chemical simulations can be greatly accelerated by constructing machine learning potentials, which is often done using active learning (AL). The usefulness of the construct…
AI-enhanced on-the-fly simulation of nonlinear time-resolved spectra
Sebastian V. Pios, Maxim F. Gelin, Arif Ullah +2
Time-resolved spectroscopy is an important tool for unraveling the minute details of structural changes of molecules of biological and technological significance. The nonlinear fem…