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Topic: Computational Modeling, Machine Learning and Data Mining in Ferroic Materials

A special issue of Journal of Materials Informatics

ISSN 2770-372X (Online)

Submission deadline: 31 Mar 2023

Guest Editor(s)

  • Prof. Xiang-Dong Ding

    State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, Shaanxi, China.

    Website | E-mail

  • Prof. Turab Lookman
    AiMaterials Research LLC, Santa Fe, NM, United States.

    Website | E-mail

Special Issue Introduction

Ferroic and multiferroic materials, characterized by their ferroic order, provide an efficient route for the control of magnetic, mechanical, and electrical subsystems in energy transduction. This has been an intensive subject for contemporary materials science with applications to a wide range of energy-critical technologies. However, developing these is quite challenging since they strongly depend on the composition complexity, crystallographic structure, size effects and operating conditions. The traditional way of deploying new ferroic materials is through trial and error, intuition and experience as materials scientists need to perform theoretical calculations and experimental confirmation, a highly inefficient, resource-intensive and expensive process in today’s information-driven society. The combination of big data and artificial intelligence has been called the “fourth paradigm of science”, and materials prediction tools based on machine learning have been successfully applied to various materials fields, including the modeling and design of new ferroic materials. We look forward to assembling a comprehensive set of research publications aimed at highlighting the latest findings in ferroic/multiferroic materials using computational simulations and machine learning algorithms with a focus on ferroelastic, ferroelectric, magnetic and multiferroic systems. Specific areas to be covered include, but are not limited to:

● Development of high throughput experimental/computational methods for high-fidelity databases
● Computational modeling-enabled understanding of thermodynamics and kinetics in 2D/3D
● Machine learning guided design and development of 2D/3D ferroic materials
● Applications of machine learning to computer simulations
● Machine learning-enabled structural characterization
● Applications of natural language processing and graphical methods
● Advances in algorithms, methods and software interfaces


Ferroelectricity, ferromagnetism, shape memory alloy, multiferroic, multi-scale modeling, machine learning

Submission Deadline

31 Mar 2023

Submission Information

Articles of special issue are free of charge for article processing.
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Submission Deadline: 31 Mar 2023
Contacts: Lijun Jin, Managing Editor,

Published Articles

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