Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Electronically excited states of molecules are at the heart of photochemistry, photophysics,
as well as photobiology and also play a role in material science. Their theoretical description …

Molpro is a general purpose quantum chemistry software package with a long development
history. It was originally focused on accurate wavefunction calculations for small molecules …

Machine learning (ML) techniques applied to chemical reactions have a long history. The
present contribution discusses applications ranging from small molecule reaction dynamics …

Density functional theory predictions of binding energies and reaction barriers provide
invaluable data for analyzing chemical transformations in heterogeneous catalysis. For high …

In the past two decades, the density matrix renormalization group (DMRG) has emerged as
an innovative new method in quantum chemistry relying on a theoretical framework very …

Quantum computers promise to revolutionise molecular electronic simulations by
overcoming the exponential memory scaling. While electronic wave functions can be …

Accurate modeling of the response of molecular systems to an external electromagnetic field
is challenging on classical computers, especially in the regime of strong electronic …

The workhorse method of computational materials science is undeniably the density
functional theory (DFT) in the Kohn-Sham framework of approximate exchange and …

We report cutting edge performance results on a single node hybrid CPU-multi-GPU
implementation of the spin adapted ab initio Density Matrix Renormalization Group (DMRG) …