Materials Discovery is a new multidisciplinary forum for researchers of all disciplines within the materials science community that is designed as scholarly link between the materials sciences and information sciences, and provides the foundation for advancing materials science knowledge by extracting and exploiting information from "big data".
This issue seeks articles that include but are not limited to: describing new genres of materials data from experimental or computational methods; analytical techniques that extract new forms of correlative information; assessment of uncertainty in databases; cyber-infrastructure and data sharing projects. The manuscript format is the author’s choice ranging from a perspective type article to a full length research paper.
The issue will be edited by Prof. Dr. Isao Tanaka, Department of Materials Science and Engineering, Kyoto University, Japan (http://cms.mtl.kyoto-u.ac.jp/tanaka-e.html) .
Submission Guidelines
The special issue is now welcoming submissions and has a deadline for submission on 1st November 2017. We expect to publish the issue in March 2018, but all papers will be published in the journal as soon as they are published – they will then be grouped in a special issue section on ScienceDirect.
All manuscripts and any supplementary material should be submitted through Elsevier Editorial System (EVISE). The authors must select as “ ” when they reach the “issue” selection step in the submission process. The EVISE website is located at: https://www.evise.com/evise/jrnl/MD
We encourage all authors to consider uploading any supplemental and underlying data with their papers in the Mendeley Data repository, or as supplemental data with their submission. Full details of how to do this can be found during the submission process.
All papers will be peer-reviewed by three independent reviewers. Requests for additional information should be addressed to the guest editor or publisher:
Guest Editor: Prof Tanaka: tanaka@cms.mtl.kyoto-u.ac.jp
Publisher: Joe d’Angelo: j.dangelo@elsevier.com