Special Issue "Machine Learning and Materials Informatics"

A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".

Deadline for manuscript submissions: closed (31 August 2020).

Special Issue Editors

Prof. Dr. Yuqing Lin
E-Mail Website
Guest Editor
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
Dr. Shruba Gangopadhyay
E-Mail Website
Guest Editor
Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904-4259, USA
Dr. Aniruddha Dutta
E-Mail Website
Guest Editor
Smith School of Business, Queen's University, Kingston, ON, Canada

Special Issue Information

Dear Colleagues,

Propelled by multiple big data repositories and algorithmic development, machine-learning- and deep-learning-focused methods are becoming almost indispensable for predicting novel materials and their properties, which are otherwise difficult to measure experimentally or problematic to compute accurately. These methods often rely on the use of already existing datasets to train a machine (computer) and map it to new materials or the material property of interest. Some of the earlier prediction endeavors include machine learning models for the thermodynamic, mechanical, and thermal properties of materials, such as the estimation of formation enthalpies, free energies, defect energetics, melting temperatures, mechanical properties, thermal conductivity, catalytic activity, and radiation damage resistance. Multiple global efforts are also underway to identify the states of the functional art materials, such as novel shape memory alloys, improved piezoelectrics, and novel perovskites and halide perovskites relevant to electronics and energy applications. The evolution of high-performance computing has also increased the efficiency of image-based characterization techniques, as well as complex statistical calculations. Today, high dimensional raw data can be extracted from experimental micrographs to study existing numerous nanoparticle and thin film surface properties. Data generated from sophisticated atomic-scale resolution instruments, such as atomic force, scanning, and transmission electron microscope images, are being used to train, test, and predict the shape and important characteristic features of nanomaterials. These whole new image data lead us to various powerful characterization algorithm techniques, which tremendously save experimental time and cost, thus eventually contributing to the development of materials processing research.

We are looking forward to putting together a comprehensive set of research publications aiming to highlight the latest findings in material science using machine learning and deep learning algorithms. We welcome original research articles involving material discovery, structure–property prediction, material characterization, and software development for the broader scientific community. We are seeking contributions involving the development, characterization, and simulation studies of nanomaterials, biomaterials, and electronic materials. We expect to bring awareness to the physical science and informatics communities, especially focusing on what challenges lie in the acquisition, processing, storage, and analyses of the material data sets. Novel methods for data cleaning, feature extraction, and analysis will also be considered and highlighted in this Special Issue.

Prof. Dr. Yuqing Lin
Dr. Shruba Gangopadhyay
Dr. Aniruddha Dutta
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. Data 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 1400 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

  • Material informatics
  • Machine learning
  • Material characterization
  • Image processing
  • Microscopy
  • Deep learning
  • Neural network
  • Pattern recognition
  • Material discovery
  • Nanomaterials
  • Electronic materials
  • Biomaterials

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Other

Article
Geometrical Platform of Big Database Computing for Modeling of Complex Physical Phenomena in Electric Current Treatment of Liquid Metals
Data 2019, 4(4), 136; https://doi.org/10.3390/data4040136 - 05 Oct 2019
Cited by 1 | Viewed by 1067
Abstract
According to the principles of multiphysical, multiscale simulation of phenomena and processes which take place during the electric current treatment of liquid metals, the need to create an adjustable and concise geometrical platform for the big database computing of mathematical models and simulations [...] Read more.
According to the principles of multiphysical, multiscale simulation of phenomena and processes which take place during the electric current treatment of liquid metals, the need to create an adjustable and concise geometrical platform for the big database computing of mathematical models and simulations is justified. In this article, a geometrical platform was developed based on approximation of boundary contours using arcs for application of the integral equations method and matrix transformations. This method achieves regular procedures using multidimensional scale matrices for big data transfer and computing. The efficiency of this method was verified by computer simulation and used for different model contours, which are parts of real contours. The obtained results showed that the numerical algorithm was highly accurate based on the presented geometrical platform of big database computing and that it possesses a potential ability for use in the organization of computational processes regarding the modeling and simulation of electromagnetic, thermal, hydrodynamic, wave, and mechanical fields (as a practical case in metal melts treated by electric current). The efficiency of this developed approach for big data matrices computing and equation system formation was displayed, as the number of numerical procedures, as well as the time taken to perform them, were much smaller when compared to the finite element method used for the same model contours. Full article
(This article belongs to the Special Issue Machine Learning and Materials Informatics)
Show Figures

Figure 1

Other

Jump to: Research

Data Descriptor
An Open Access Data Set Highlighting Aggregation of Dyes on Metal Oxides
Data 2020, 5(2), 45; https://doi.org/10.3390/data5020045 - 13 May 2020
Cited by 2 | Viewed by 1105
Abstract
The adsorption of a dye to a metal oxide surface such as TiO2, NiO and ZnO leads to deprotonation and often undesirable aggregation of dye molecules, which in turn impacts the photophysical properties of the dye. While controlled aggregation is useful [...] Read more.
The adsorption of a dye to a metal oxide surface such as TiO2, NiO and ZnO leads to deprotonation and often undesirable aggregation of dye molecules, which in turn impacts the photophysical properties of the dye. While controlled aggregation is useful for some applications, it can result in lower performance for dye-sensitized solar cells. To understand this phenomenon better, we have conducted an extensive search of the literature and identified over 4000 records of absorption spectra in solution and after adsorption onto metal oxide. The total data set comprises over 3500 unique compounds, with observed absorption maxima in solution and after adsorption on the semiconductor electrode. This data may serve to provide further insight into the structure-property relationships governing dye-aggregation behaviour. Full article
(This article belongs to the Special Issue Machine Learning and Materials Informatics)
Show Figures

Figure 1

Data Descriptor
The Ionic Liquid Property Explorer: An Extensive Library of Task-Specific Solvents
Data 2019, 4(2), 88; https://doi.org/10.3390/data4020088 - 21 Jun 2019
Cited by 6 | Viewed by 2396
Abstract
Ionic liquids have a broad spectrum of applications ranging from gas separation to sensors and pharmaceuticals. Rational selection of the constituent ions is key to achieving tailor-made materials with functional properties. To facilitate the discovery of new ionic liquids for sustainable applications, we [...] Read more.
Ionic liquids have a broad spectrum of applications ranging from gas separation to sensors and pharmaceuticals. Rational selection of the constituent ions is key to achieving tailor-made materials with functional properties. To facilitate the discovery of new ionic liquids for sustainable applications, we have created a virtual library of over 8 million synthetically feasible ionic liquids. Each structure has been evaluated for their-task suitability using data-driven statistical models calculated for 12 highly relevant properties: melting point, thermal decomposition, glass transition, heat capacity, viscosity, density, cytotoxicity, CO 2 solubility, surface tension, and electrical and thermal conductivity. For comparison, values of six properties computed using quantum chemistry based equilibrium thermodynamics COSMO-RS methods are also provided. We believe the data set will be useful for future efforts directed towards targeted synthesis and optimization. Full article
(This article belongs to the Special Issue Machine Learning and Materials Informatics)
Show Figures

Graphical abstract

Back to TopTop