Editorial
Introduction to Materials Informatics
Krishna Rajan, Jörg Behler and Chris J. Pickard introduce the Materials Advances themed collection on Materials Informatics.
Paper
Single-bonded nitrogen chain and porous nitrogen layer via Ce–N compounds
We explored the phase diagram of Ce–N compounds, and identified several interesting poly-nitrogen species, including the infinite helical chain, and porous poly-nitrogen layer.
Paper
Diamond-XII: a new type of exotic cubic carbon allotrope
Structural candidates for the super-size cubic carbon phase discovered in the Popigai crater are proposed. They are superhard transparent insulators as the discovered sample and their simulated XRD can partially explain the experimental results.
Paper
Theoretical design of two-dimensional AMInP2X3Y3 (AM = Li, Na, K; X/Y = S, Se, Te) monolayers for highly efficient excitonic solar cells
Two-dimensional alkali metal indium phosphorus trichalcogenides AMInP2X3Y3 monolayers are regarded as promising candidates for use in photovoltaic solar cells.
Paper
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
Funnel Hopping Monte Carlo simulations of MaPbI3 show that the delta phases which have a lower energy than the perovskite phases are only thermodynamically preferred up to 200 K. This explains the absence of the delta phases in experiments.
Paper
ICHOR: a modern pipeline for producing Gaussian process regression models for atomistic simulations
There is now a highly automated electronic tool (called ICHOR) that facilitates the construction and validation of actively learnt machine learning models (Gaussian process regression) for molecules and molecular clusters.
Paper
Selected machine learning of HOMO–LUMO gaps with improved data-efficiency
Selected machine learning (SML) relies on prior data classification and leads to improved data-efficiency for modeling molecular electronic properties, such as HOMO–LUMO-gaps.
About this collection
Guest Edited by Professor Chris Pickard (University of Cambridge, UK), Professor Jörg Behler (Georg-August-Universität Göttingen, Germany), and Professor Krishna Rajan (University at Buffalo, USA)
The discipline of Materials Informatics has emerged from a fusion of increasing availability of materials data, high throughput experimental and computational methods, first principles and other advanced materials models, and machine learning. It has been fuelled by the dramatic growth in available computational power, and its ubiquity.
In this Themed Collection we have featured articles from across the wide diversity of Materials Informatics, ranging from novel computational and experimental methods to state-of-the-art applications.