Special Issue on Advanced methods of Machine Learning/ Artificial intelligence applied to the discovery and design of materials - Call for Papers

In materials science, the rate at which fundamental discoveries are being made has greatly augmented mankind’s potential to manipulate matter. While impressive, these advancements pale in comparison with the impact that data sciences have had on the way our society ingests, manipulates, and exploits information, contributing to the realignment of entire segments of the economy and transforming the scientific enterprise in a number of fields. Over the past few years, the field of materials science has undergone a rapid transformation facilitated by the pervasive data revolution, as the field has quickly recapitulated the steps followed by any other data-enabled field: from insight, through predictions, to actions.

In this special issue, we call for original and timely contributions in the general field of materials science in which advanced methods in machine learning or artificial intelligence have been used to: unravel correlations between materials descriptors and properties; establish predictive models that serve as linkages along the process-microstructure-property continuum; detect and quantify materials features responsible for specific materials phenomena; efficiently explore and exploit the materials space, among others.

Authors are strongly encouraged to share portion of their additional material (including data and code) related to the articles published in this Special Issue in the Mendeley Data Repository https://data.mendeley.com/, in other publicly accessible repositories (Github, CodeOcean, etc) or in one of the repositories listed here,in order to encourage others to reproduce/replicate their work in the spirit of the FAIR principles of findability, accessibility, interoperability and reuse of digital assets.

Additional datasets or codes can be referred to as indicated in the Guide for Authors of this journal. 

Example of Reference to a dataset or code:
[dataset] [6] M. Oguro, S. Imahiro, S. Saito, T. Nakashizuka, Mortality data for Japanese oak wilt disease and surrounding forest compositions, Mendeley Data, v1, 2015. https://doi.org/10.17632/xwj98nb39r.1.

All papers submitted to this Special Issue will be handled by the dedicated editor, Prof. Raymundo Arroyave.

All full papers must be submitted through the Elsevier Editorial System (https://ees.elsevier.com/mlblue). When submitting your manuscripts, please select the article type “VSI:ML/AI at Mat Sci” Please also refer to the Author Instructions (https://www.elsevier.com/journals/materials-letters/0167-577x/guide-for-authors) prior to submission for the proper format of your manuscripts.

The submission of articles is open from 13th April 2020.