Special Issue "Machine-Learning and Deep Learning Applications in Crystalline Materials Science and Computational Mechanics"
A special issue of Crystals (ISSN 2073-4352). This special issue belongs to the section "Crystal Engineering".
Deadline for manuscript submissions: 20 October 2021.
Special Issue Editors
Interests: soft materials; molecular dynamics; machine learning in materials science
2. Institute of Structural Mechanics, Bauhaus Universität-Weimar, 99423 Weimar, Germany
Interests: machine learning; deep learning; data science; big data; sustainable concrete; materials informatics
Special Issues and Collections in MDPI journals
Special Issue Information
Dear Colleagues,
Machine-learning (ML) has recently attracted much attention from materials researchers. ML has become an integral part of how materials scientists and engineers conduct research, ranging from material discovery to the optimization of materials processing. Generally, ML is a paradigm in data science, which is building the analytical model by statistical data analyses. ML capable of learning from a set of data (databases) to identify, classify, cluster, diagnose, and prescribe with almost no or minimal human intervention.
The availability of materials information on databases ranging from the structure, characteristics, and potential applications is not a problem currently. Furthermore, the extraction of materials data from scientific journals can be a future possibility for materials design and selection. This opportunity is a just-right recipe for the successful future for ML in applications in materials science and engineering. In the old days, experiments were the primary key to discovering new materials and optimizing the characteristics of materials. Not until the 1960s was this position was challenged by computational methods ranging from Density Functional Theory (DFT), Monte Carlo (MC) simulation, Molecular Dynamics (MD), and many others. However, the computational power still has a limitation in predicting the new behaviour of materials.
In the computational approach, the paradigm of the complete understanding mechanism of a phenomenon through an analytical model, instead of advantage, is actually a barrier in discovering new materials. In the ML paradigm, instead of searching for a complete understanding of what is happening in the system, the interpolation of a new phenomenon from large data sets is the primary key. ML treats a complex model as a mere black box, which speeds the discovery of the latest materials and the optimization of existing materials.
Dr. Jaka Fajar Fatriansyah
Prof. Dr. Amir H. Mosavi
Guest Editors
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. Crystals 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 1800 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
- Materials discovery
- Materials science
- Optimization
- Design and selection of materials
- Artificial intelligence
- Deep learning
- Data science
- Big data