Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …

An overwhelming diversity of colloidal building blocks with distinct sizes, materials and
tunable interaction potentials are now available for colloidal self-assembly. The application …

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 …

Classical molecular dynamics simulates the time evolution of molecular systems through the
phase space spanned by the positions and velocities of the constituent atoms. Molecular …

Auto-associative neural networks (“autoencoders”) present a powerful nonlinear
dimensionality reduction technique to mine data-driven collective variables from molecular …

Electronically active organic molecules have demonstrated great promise as novel soft
materials for energy harvesting and transport. Self-assembled nanoaggregates formed from …

The typically rugged nature of molecular free-energy landscapes can frustrate efficient
sampling of the thermodynamically relevant phase space due to the presence of high free …

Peptoids (N-substituted glycines) are a class of tailorable synthetic peptidomic polymers.
Amphiphilic diblock peptoids have been engineered to assemble 2D crystalline lattices with …

In molecular simulations, the identification of suitable reaction coordinates is central to both
the analysis and sampling of transitions between metastable states in complex systems. If …

Recent advances in computer hardware and algorithms are spawning an explosive growth
in the use of computer-based systems aimed at analyzing and ultimately correlating large …