Special Issue "Machine Learning in Inorganic Materials Chemistry"

A special issue of Inorganics (ISSN 2304-6740). This special issue belongs to the section "Inorganic Solid-State Chemistry".

Deadline for manuscript submissions: 1 December 2019.

Special Issue Editor

Dr. Volker Deringer
E-Mail Website
Guest Editor
Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
Interests: computational materials chemistry; amorphous solids; chemical bonding; machine-learning based interatomic potentials

Special Issue Information

Dear Colleagues,

Machine learning (ML) algorithms, which aim to extract knowledge from large amounts of data, are increasingly used to solve basic problems in physics and chemistry. This is largely driven by the recent availability of raw supercomputing power, but it also enables new ways of using the structural knowledge that chemists have collected for decades—in the Inorganic Crystal Structure Database (ICSD), for example. In the recent years, ML-based techniques have begun to identify promising synthesis targets from the wide space of candidate compositions, leading to possible new superhard materials, for example. Atomic, molecular, and materials properties have been successfully “machine-learned”; this includes, but is not limited to, atomization energies, band gaps, or NMR chemical shifts. Finally, ML-based interatomic potentials (force fields) are becoming increasingly popular: by fitting to accurate quantum-mechanical reference data, these ML-driven simulation methods can create structural models of materials with an unprecedented combination of accuracy and speed. Today, these methods are beginning to be applicable to questions in solid-state and materials chemistry, such as the chemical reactivity at surfaces and interfaces, or the search for new crystal structures. This Special Issue is meant to capture a snapshot of this exciting and emerging field. It is hoped that it will inspire new work at the intersection of ML-driven techniques, solid-state and materials chemistry, and neighboring research fields.

Dr. Volker Deringer
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Inorganics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning (ML)
  • data mining and Open Data
  • artificial neural network (NN) models
  • kernel methods
  • ML-based interatomic potentials (force fields)

Published Papers

This special issue is now open for submission.
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