Description

This Research Topic deals with computational materials science as a key in the discovery of new functional molecules and materials by exploring the huge uncharted territory of the chemical and structural space of materials. As such, it will help to overcome current global challenges like the quest for efficient and sustainable use of energy resources.

In that spirit, this collection of review articles will:

(i) educate scientists in the relevant techniques: electronic-structure theory, spectroscopy, ab initio thermodynamics and statistical mechanics, multi-scale modeling, and machine learning approaches to potential parametrization, big-data dimensionality reduction (e.g. machine learning and compressed sensing), and property prediction;

(ii) extend the existing canon of electronic-structure theory text books and reviews through a practically oriented introduction of distinct timely topics that are relevant in modern materials and molecular simulations.

The orientation toward real-world examples and the tutorial style of this review collection ensures that theoretical knowledge will become practical skills. Thematically, this Research Topic is at the forefront of a paradigm change in materials science from trial-and-error experiments to informed data-driven design