Computational Materials Discovery
The spectacular advances in computational techniques for materials discovery including high-throughput screenings, data-mining, machine learning, and artificial intelligence, as well as structure prediction based calculations, have led to the in silico discovery of a plethora of targeted materials. At the same time, the structure-property relationships that have been elucidated via computations have made it possible to design compounds for specific applications. Recently, there have been a number of remarkable materials-by-design success stories, and undoubtedly many other predicted materials will be experimentally realized soon. This special issue showcases advances in methods used to discover and design new materials, and illustrate their applications towards energy, quantum, structural, and 2D materials, as well as molecular crystals, MOFs, and more.
Topics covered include, but are not limited to:
- Photovoltaic
- Battery
- Energetic
- Superhard
- Superconducting
- Quantum
- Topological
- Earth and Planetary Science Inspired
- High Pressure
- Structural (Alloys)
- 2 Dimensional
- Molecular crystals
- Metal Organic Frameworks
- Hydrogen Storage
- Multiferroics
- Nanostructures
- Metamaterials
- Methods for Computational and Data-Driven Materials Discovery
Guest Editors
Eva Zurek, University of Buffalo
Noa Marom, Carnegie Mellon University
Johannes Hachmann, University of Buffalo
JCP Editors
David Manolopoulos, University of Oxford
Todd Martínez, Stanford University
Angelos Michaelides, University College London
David Sherrill, Georgia Institute of Technology
More information:
Please note that papers will be published as normal when they are ready in a regular issue of the journal and will populate on a virtual collection page within a few days of publication. Inclusion in the collection will not cause delay in publication.