Materials Advances is delighted to introduce a special online collection on ‘Materials Informatics‘, guest edited by Chris Pickard, Krishna Rajan & Jörg Behler.
The discipline of Materials Informatics has emerged from a fusion of increasing availability of materials data, high throughput experimental & computational methods, first principles & other advanced materials models, and machine learning. It is being fuelled by the dramatic growth in available computational power and its ubiquity.
This Themed Collection features articles from across the wide diversity of Materials Informatics. Articles in the collection are published in Materials Advances so they are all open access and freely available.
A small selection of the papers are featured below:
Introduction to Materials Informatics, Chris Pickard, Krishna Rajan & Jörg Behler, Mater. Adv., 2023,4, 2695-2697, DOI: 10.1039/ D3MA90047A
Experimental absence of the non-perovskite ground state phases of MaPbI3 explained by a Funnel Hopping Monte Carlo study based on a neural network potential, Jonas A. Finkler and Stefan Goedecker, Mater. Adv., 2023,4, 184-194, DOI: 10.1039/ D2MA00958G
ICHOR: a modern pipeline for producing Gaussian process regression models for atomistic simulations, Matthew J. Burn and Paul L. A. Popelier, Mater. Adv., 2022, 3, 5383-5392 DOI: 10.1039/ D2MA00673A
Selected machine learning of HOMO–LUMO gaps with improved data-efficiency, Bernard Mazouin, Alexandre Alain Schöpfer and O. Anatole von Lilienfeld, Mater. Adv., 2022,3, 8306-8316, DOI: 10.1039/ D2MA00742H
We hope you enjoy reading the special collection.
Do you have an idea for our next themed collection? Suggest a topic using our online form.
