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Revolutionizing Battery Design Through Quantum Computing and Machine Intelligence
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Author(s): C. Sushama (Mohan Babu University, India), R. V. V. Krishna (Aditya College of Engineering and Technology, Jawaharlal Nehru Technological University, Kakinada, India), J. Srimathi (KPR College of Arts, Science, and Research, India)and C. H. Anil (Koneru Lakshmaiah Education Foundation, India)
Copyright: 2024
Pages: 20
Source title:
Real-World Challenges in Quantum Electronics and Machine Computing
Source Author(s)/Editor(s): Christo Ananth (Samarkand State University, Uzbekistan), T. Ananth Kumar (IFET College of Engineering, India)and Osamah Ibrahim Khalaf (Al-Nahrain University, Iraq)
DOI: 10.4018/979-8-3693-4001-1.ch022
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Abstract
There is a growing demand for longer-lasting and more efficient batteries due to the increasing number of portable electronic gadgets, electric cars, and renewable energy sources. It can be expensive and time-consuming to use traditional methods for designing and optimizing batteries because they rely on trial and error. Nevertheless, there are substantial chances to enhance battery design because of recent advances in AI and quantum computing. Complex chemical interactions and materials can be recreated using quantum computing. Researchers may examine huge swaths of chemical space and anticipate the characteristics of new battery materials with unparalleled accuracy by applying the concepts of quantum mechanics. Predictions made by machine learning (ML) are data-driven and have the potential to be valued. Instead of going into detail about each machine learning technique, the authors will focus on the scientific problems related to electro chemical systems that can be solved with the use of machine learning.
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