Reticular materials rely on a unique building concept where inorganic and organic building
units are stitched together giving access to an almost limitless number of structured ordered …

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

Machine learning has been pervasively touching many fields of science. Chemistry and
materials science are no exception. While machine learning has been making a great …

Empirical parametrization underpins many scientific methodologies including certain
quantum-chemistry protocols [eg, density functional theory (DFT), machine-learning (ML) …

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 scalable …

Quantum chemical methods developed since 1927 are instrumental in chemical simulations
but human expertise has been still essential in choosing a suitable method. Here we …

Existing machine learning models attempt to predict the energies of large molecules by
training small molecules, but eventually fail to retain high accuracy as the errors increase …

The relationship between chemical structure and chiroptical properties is not always clearly
understood. Nowadays, efforts to develop new systems with enhanced optical properties …

In recent years, the rapid advancement of digital technologies has precipitated a paradigm
shift in global healthcare, heralding a new era of digital health methodologies. This transition …

Computing high dimensional potential surfaces for molecular and materials systems is
considered to be a great challenge in computational chemistry with potential impact in a …