Guest Editors
Prof. Bradha Madhavan, Rathinam Research Centre, Rathinam Technical Campus, India.
Dr. Yuvaraj Subramanian, University of Ulsan, South Korea.
Dr. Manikandan Ramu, Dongguk University, South Korea.
Summary
Computational methods play an increasingly important role in materials science. Computational methods can be used to calculate the physical and chemical properties of materials, predict the behavior of materials under different conditions, and simulate the interactions between materials and their environment. This information can then be used to design and optimize materials for specific applications. For example, computational methods can be used to design materials with specific properties such as high strength, toughness, or electrical conductivity. Computational methods can also be used to identify and eliminate potential failure points and improve the efficiency of production processes.
Recently, the fields of materials science, condensed matter physics, and chemistry have all embraced machine learning (ML) and artificial intelligence (AI) techniques. Machine learning has built on its strength in property prediction to enable the discovery, design, and development of novel materials spanning a variety of applications and materials classes by supplying new understanding of fundamental chemical or physical relationships governing properties of interest. the time-consuming and unsuccessful ancient method of material inventions through trial and error. By learning rules from datasets and building predictive models, machine learning-based advancements in energy storage materials, in particular, can accelerate the process.
The recent boom has been largely attributed to the advancement of deep learning (DL), as well as the concomitant rise in graphics processing units and overall computing power. The next concern is whether the vast improvements in AI can be translated into the discipline of materials science.
The foremost complications scientists deal at the materials is the simulation of the prescribed research system. In materials science, several programs, applications not available to accurately simulate atomic effects in different systems. Hence, if artificial intelligence is properly employed in materials, they will be extremely beneficial for accurate sensing of the required parameters accurately. However, choosing the apt materials, effective artificial intelligence AI algorithm and pertinent power source are still grim to custom in most of the cases. In this background, Computational methods will be beneficial to enhance the sensing quality of materials development.
Keywords
Topics include, but are not limited to, the following:
Computational material design
Performance prediction using ML or AI
Organic electronics, biophotonics and smart materials
Materials data analysis using AI
Materials Innovation
Data simulation - structure property relationship studies of materials
ML &AI in battery systems
Role of ML and AI in fuel cells
Mini reviews related to energy applications
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