We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …

Modern technology has enabled the isolation of nanocellulose from plant-based fibers, and
the current trend focuses on utilizing nanocellulose in a broad range of sustainable …

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 …

Implicit solvation is an effective, highly coarse-grained approach in atomic-scale simulations
to account for a surrounding liquid electrolyte on the level of a continuous polarizable …

Spectroscopic techniques can probe molecular systems non-invasively and investigate their
structure, properties and dynamics in different environments and physico-chemical …

The hypothesis that water has a second critical point at deeply supercooled conditions was
formulated to provide a thermodynamically consistent interpretation of numerous …

How are water's material properties encoded within the structure of the water molecule? This
is pertinent to understanding Earth's living systems, its materials, its geochemistry and …

The possible existence of a metastable liquid–liquid transition (LLT) and a corresponding
liquid–liquid critical point (LLCP) in supercooled liquid water remains a topic of much …

This Review explores the dynamic behavior of water within nanopores and biological
channels in lipid bilayer membranes. We focus on molecular simulation studies, alongside …

Thermodynamic properties of liquid water as well as hexagonal (Ih) and cubic (Ic) ice are
predicted based on density functional theory at the hybrid-functional level, rigorously taking …