The first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …

Machine learning encompasses tools and algorithms that are now becoming popular in
almost all scientific and technological fields. This is true for molecular dynamics as well …

Inspired by the success of deep learning techniques in the physical and chemical sciences,
we apply a modification of an autoencoder type deep neural network to the task of …

Evaluating the (dis) similarity of crystalline, disordered and molecular compounds is a critical
step in the development of algorithms to navigate automatically the configuration space of …

Highlights•The principles of protein structural ensemble determination are described.•The
use of experimental data averaged over multiple states is analysed.•Approaches for …

Although hydrocarbon production from unconventional reservoirs, the so-called shale gas,
has exploded recently, reliable predictions of resource availability and extraction are …

Macromolecular and biomolecular folding landscapes typically contain high free energy
barriers that impede efficient sampling of configurational space by standard molecular …

Automated analyses of the outcome of a simulation have been an important part of atomistic
modeling since the early days, addressing the need of linking the behavior of individual …

Molecular crystals play an important role in several fields of science and technology. They
frequently crystallize in different polymorphs with substantially different physical properties …

In many branches of materials science it is now routine to generate data sets of such large
size and dimensionality that conventional methods of analysis fail. Paradigms and tools from …