Materials chemistry is being profoundly influenced by the uptake of machine learning
methodologies. Machine learning techniques, in combination with established techniques …

Current electrochemical energy storage systems (EESSs) are insufficient to meet the
escalating energy demands in grid-scale energy storage. The main deficiencies of the …

Deep learning methods for the prediction of molecular excitation spectra are presented. For
the example of the electronic density of states of 132k organic molecules, three different …

Nanomaterials have a critical role in functional materials engineering; however, their
efficient recycling, durable use, and multifunctional integration remain a huge challenge for …

Conformational changes (ie, dynamic transitions between pairs of conformational states)
play important roles in many chemical and biological processes. Constructing the Markov …

Tailoring the functional properties of advanced organic/inorganic heterogeneous devices to
their intended technological applications requires knowledge and control of the microscopic …

The computational characterization of inorganic–organic hybrid interfaces is arguably one of
the technically most challenging applications of density functional theory. Due to the …

Uncovering slow collective variables (CVs) of self-assembly dynamics is important to
elucidate its numerous kinetic assembly pathways and drive the design of novel structures …

In solvent extraction, the self-assembly of amphiphilic molecules into an organized structure
is the phenomenon responsible for the transfer of the metal ion from the aqueous phase to …

Since the launch of the Materials Genome Initiative in 2011, there has been an increasing
interest in the application of statistical and machine-learning techniques to materials …