Geometric phase in coupled cluster theory.

It has been well-established that the topography around conical intersections between excited electronic states is incorrectly described by coupled cluster and many other single reference theories (the intersections are "defective"). Despite this, we show both analytically and numerically that the geometric phase effect (GPE) is correctly reproduced upon traversing a path around a defective excited-state conical intersection (CI) in coupled cluster theory. The theoretical analysis is carried out by using a non-Hermitian generalization of the linear vibronic coupling approach.

Simulation of the photodetachment spectra of the nitrate anion (NO) in the B̃ E' energy range and non-adiabatic electronic population dynamics of NO.

The photodetachment spectrum of the nitrate anion (NO) in the energy range of the NO second excited state is simulated from first principles using quantum wave packet dynamics. The prediction at 10 K and 435 K relies on the use of an accurate full-dimensional fully coupled five state diabatic potential model utilizing an artificial neural network. The ability of this model to reproduce experimental spectra was demonstrated recently for the lower energy range [A.

Accurate quantum dynamics simulation of the photodetachment spectrum of the nitrate anion (NO ) based on an artificial neural network diabatic potential model.

The photodetachment spectrum of the nitrate anion (NO ) is simulated from first principles using wavepacket quantum dynamics propagation and a newly developed accurate full-dimensional fully coupled five state diabatic potential model. This model utilizes the recently proposed complete nuclear permutation inversion invariant artificial neural network diabatization technique [D. M.

Complete Nuclear Permutation Inversion Invariant Artificial Neural Network (CNPI-ANN) Diabatization for the Accurate Treatment of Vibronic Coupling Problems.

A recently developed scheme to produce accurate high-dimensional coupled diabatic potential energy surfaces (PESs) based on artificial neural networks (ANNs) [ 2018, 149, 204106 and 2019, 151, 164118] is modified to account for the proper complete nuclear permutation inversion (CNPI) invariance. This new approach cures the problem intrinsic to the highly flexible ANN representation of diabatic PESs to account for the proper molecular symmetry accurately. It turns out that the use of CNPI invariants as coordinates for the input layer of the ANN leads to a much more compact and thus more efficient representation of the diabatic PES model without any loss of accuracy.

Diabatic neural network potentials for accurate vibronic quantum dynamics-The test case of planar NO.

A recently developed scheme to produce high-dimensional coupled diabatic potential energy surfaces (PESs) based on artificial neural networks (ANNs) [D. M. G.

Neural network diabatization: A new for accurate high-dimensional coupled potential energy surfaces.

A new diabatization method based on artificial neural networks (ANNs) is presented, which is capable of reproducing high-quality data with excellent accuracy for use in quantum dynamics studies. The diabatic potential matrix is expanded in terms of a set of basic coupling matrices and the expansion coefficients are made geometry-dependent by the output neurons of the ANN. The ANN is trained with respect to data using a modified Marquardt-Levenberg back-propagation algorithm.