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Machine Learning-Driven Optimization of Battery Materials via Quantum Computing
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Author(s): Loveleen Kumar (Swami Keshvanand Institute of Technology, Management, and Gramothan, India), R. V. V. Krishna (Aditya College of Engineering and Technology, Jawaharlal Nehru Technological University, Kakinada, India), S. Radhakrishnan (KKR and KSR Institute of Technology and Sciences, India)and Yudhishther Singh Bagal (Lovely Professional University, India)
Copyright: 2024
Pages: 17
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.ch009
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Abstract
This research explores the possible synergy between machine learning algorithms and quantum computers to advance the progress of battery materials. To streamline the investigation of materials suitable for high-performance batteries, we introduce a novel framework that employs optimization approaches guided by machine learning. This comprehensive collection of properties for Mg-ion and Li-ion battery electrode materials allows machine learning algorithms to accurately forecast their voltage, capacity, and energy density. This advancement is anticipated to expedite the exploration of more effective materials for energy storage. The results showed a strong relationship between energy density and capacity, but no such relationship was found between average voltage and the aforesaid factors. Implementing this technique in high-throughput systems has the potential to greatly expedite breakthroughs in computational materials research.
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