Molecular simulations, which simulate the motions of particles according to fundamental
laws of physics, have been applied to a wide range of fields from physics and materials …

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 …

In this review paper, we will present different data-driven dimension reduction techniques for
dynamical systems that are based on transfer operator theory as well as methods to …

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

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 …

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 …

Development and applications of neural network (NN)‐based approaches for representing
potential energy surfaces (PES) of bound and reactive molecular systems are reviewed …

The study of the conformation properties of macromolecules is at the heart of polymer
science. Essentially all physical properties of polymers are manifestations of the underlying …

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

Reaction coordinates are integral to several classic rate theories that can (a) predict kinetic
trends across conditions and homologous reactions,(b) extract activation parameters with a …