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

By averaging over atomic details, coarse-grained (CG) models provide profound
computational and conceptual advantages for studying soft materials. In particular, bottom …

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 …

Molecular simulation has been widely used to study microbial proteins' structural
composition and dynamic properties, such as volatility, flexibility, and stability at the …

Dimensionality reduction via coarse grain modeling is a valuable tool in biomolecular
research. For large assemblies, ultra coarse models are often knowledge-based, relying on …

Precise regulation of the peptide self-assembly into ordered nanostructures with intriguing
properties has attracted intense attention. However, predicting peptide assembly at atomic …

This work presents a framework governing the development of an efficient, accurate, and
transferable coarse-grained (CG) model of a polyether material. The framework combines …

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 review the recent literature on particle-based simulation of surfactants, focusing on key
methodological developments in the areas of surfactant self-assembly, micelle formation …