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

Over the last few years, coarse-grained molecular dynamics has emerged as a way to model
large and complex systems in an efficient and inexpensive manner due to its lowered …

Accurate force fields are necessary for predictive molecular simulations. However,
developing force fields that accurately reproduce experimental properties is challenging …

While energy-structure-function (ESF) maps are a powerful new tool for in silico materials
design, the cost of acquiring an ESF map for many properties is too high for routine …

We explore the use of coarse-grained dissipative particle dynamics simulations to predict
critical micelle concentrations (CMCs) in polydisperse surfactant mixtures and blends. By …

Lithium/sulfur (Li/S) cells that offer an ultrahigh theoretical specific energy of 2600 Wh/kg are
considered one of the most promising next-generation rechargeable battery systems for the …

Abstract Characterization of phases of soft matter systems is a challenge faced in many
physical chemical problems. For polymorphic fluids it is an even greater challenge …

Digitisation offers significant opportunities for the formulated product industry to transform
the way it works. Recent developments in the fields of artificial intelligence, machine …

The coarse grained (CG) model implements the molecular dynamics simulation by
simplifying atom properties and interaction between them. Despite losing certain detailed …