Manifold learning (ML), also known as nonlinear dimension reduction, is a set of methods to
find the low-dimensional structure of data. Dimension reduction for large, high-dimensional …

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 …

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 …

By combining metal nodes with organic linkers we can potentially synthesize millions of
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …

Deep learning, which is especially formidable in handling big data, has achieved great
success in various fields, including bioinformatics. With the advances of the big data era in …

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 …

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 …

Models that combine quantum mechanics (QM) with machine learning (ML) promise to
deliver the accuracy of QM at the speed of ML. This hands‐on tutorial introduces the reader …

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

Markov State Models (MSMs) provide an automated framework to investigate the dynamical
properties of high-dimensional molecular simulations. These models can provide a human …