Guest Editors
Dr. Reliance Jain
Email: reliancegprs@gmail.com
Affiliation: Automation and Robotics Department, Prestige Institute of Engineering Management and Research, Indore, 452010, India
Homepage:
Research Interests: multiscale design of high entropy alloys, predictive modeling of mechanical properties, big data and materials informatics, machine learning in hot deformation behavior
Dr. Sheetal Kumar Dewangan
Email: sheetal@ajou.ac.kr
Affiliation: Department of Materials Science and Engineering, Ajou University, Suwon, 16499, South Korea
Homepage:
Research Interests: properties prediction by artificial neural network, microstructure evaluation in high entropy alloy
Dr. Sandeep Jain
Email: sandeep20@skku.edu
Affiliation: Department of Materials Science and Engineering, SKKU University, Suwon, 16499, South Korea
Homepage:
Research Interests: design and development of multicomponent alloys using machine leaning, integration of machine learning for prediction and design of alloys
Dr. Rameshwar L. Kumawat
Email: ramgt88@gmail.com
Affiliation: Chemistry Department, Northwestern University, Evanston, 60208, USA
Homepage:
Research Interests: machine learning and transfer learning for intermolecular interactions, computational materials science and engineering, molecular dynamics
Dr. Kapil Gupta
Email: kapil.gupta@ddn.upes.ac.in
Affiliation: School of Computer Science, University of Petroleum and Energy Studies, Dehradun, 248007, India
Homepage:
Research Interests: artificial intelligence, machine learning, heart machine interface, data analysis
Summary
Machine learning has revolutionized materials discovery by enabling faster, more accurate predictions and optimizations of new materials. This approach is critical for industries like aerospace, automotive, and energy storage, where the demand for innovative materials is rapidly growing. By leveraging large datasets, machine learning identifies patterns and correlations that accelerate material development and understanding.
This Special Issue focuses on the intersection of materials discovery and machine learning, highlighting the latest techniques and applications in data-driven materials research. It aims to provide a platform for researchers and industry professionals to share insights on machine learning’s role in advancing material design, optimization, and predictive modeling, with an emphasis on real-world applications.
Key areas of focus include (not limited to):
1. Machine Learning and AI in Materials Discovery
· Topics: Predictive modeling, data-driven material discovery, AI-assisted material design, reinforcement learning in materials science
2. Materials Genome and Integrated Materials Science
· Topics: Integrated computational materials engineering (ICME), materials genome initiative, multiscale modeling, computational tools for material design
3. High-Performance Computing and Big Data in Material Technologies
· Topics: Novel computing methods for material simulation, big data analytics in materials science, high-throughput computational screening
4. Bio-inspired and Robotic Materials
· Topics: Design and simulation of bio-inspired materials, smart and adaptive materials, robotic materials for soft robotics
5. Multifunctional and Modern Functional Materials
· Topics: Design of multifunctional materials, materials with smart functionalities, tunable properties for advanced applications
6. Modeling and Engineering of Modern Functional Materials
· Topics: Finite element analysis, process-structure-property relationships, simulation of material properties, integration of design and manufacturing processes
7. Data Analytics in Materials Design and Manufacturing
· Topics: Data-driven optimization in material processing, material informatics, machine learning for process modeling and optimization
8. Sustainable and Renewable Materials Design
· Topics: Machine learning in green technologies, data-driven discovery of sustainable materials, energy-efficient materials for renewable energy
Keywords
Materials discovery, Machine learning in materials science, Predictive modeling, Data mining, High entropy alloys, Material optimization, Neural networks in materials, AI-driven materials design, Genetic algorithms in materials, Sustainable materials
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