Theoretical simulations of electronic excitations and associated processes in molecules are
indispensable for fundamental research and technological innovations. However, such …

Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …

Highlights•Drug discovery is time consuming, expensive and experiences increasing
challenges.•Generation of new drug candidates is one of the major challenges.•Quantum …

Exploring excitation energy transfer (EET) in light-harvesting complexes (LHCs) is essential
for understanding the natural processes and design of highly-efficient photovoltaic devices …

The recurrent neural network with the long short-term memory cell (LSTM-NN) is employed
to simulate the long-time dynamics of open quantum systems. The bootstrap method is …

It has been recently shown that supervised machine learning (ML) algorithms can accurately
and efficiently predict long-time population dynamics of dissipative quantum systems given …

Having made significant advancements in understanding living organisms at various levels
such as genes, cells, molecules, tissues, and pathways, the field of life sciences is now …

In this work, we report a simple, efficient, and scalable machine-learning (ML) approach for
map** non-self-consistent Kohn–Sham Hamiltonians constructed with one kind of density …

The all-atomic full-dimensional-level simulations of nonadiabatic molecular dynamics
(NAMD) in large realistic systems has received high research interest in recent years …

The utilization of artificial neural networks (ANNs) provides strategies for accelerating
molecular simulations. Herein, ANNs are implemented as propagators of the time …