Application of Quantum Computing in the Design of New Materials for Batteries
Abstract
The background of this research focuses on the challenges of developing batteries with high capacity, efficiency, and long life. Quantum computing is considered a promising technology for designing new materials that can solve these problems. The purpose of the study is to examine the potential application of quantum computing in the design of battery materials that are more efficient and have better stability. The method used is a quantum simulation to model the interactions of atoms and molecules in various materials that have the potential to be used for batteries, such as lithium-sulfur, graphene, and sodium-ion. The results showed that lithium-sulfur-based materials have a high energy capacity but are less stable, while graphene is more stable with excellent conductivity despite a slightly lower energy capacity. These results provide new insights into the selection of battery materials based on the balance between energy capacity, conductivity, and thermal stability. The conclusion of this study confirms the importance of quantum computing in accelerating the development of more efficient and environmentally friendly battery materials, although further physical experiments are needed to verify the results of quantum simulations.
Full text article
References
Ajagekar, A. (2021). Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems. Applied Energy, 303(Query date: 2024-12-07 00:48:28). https://doi.org/10.1016/j.apenergy.2021.117628
Ajagekar, A. (2022). Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality. Renewable and Sustainable Energy Reviews, 165(Query date: 2024-12-07 00:48:28). https://doi.org/10.1016/j.rser.2022.112493
Andritsos, E. I. (2021). Single-Atom Catalysts as Promising Cathode Materials for Lithium-Sulfur Batteries. Journal of Physical Chemistry C, 125(33), 18108–18118. https://doi.org/10.1021/acs.jpcc.1c04491
Awan, U. (2022). Quantum computing challenges in the software industry. A fuzzy AHP-based approach. Information and Software Technology, 147(Query date: 2024-12-07 00:48:28). https://doi.org/10.1016/j.infsof.2022.106896
Bardin, J. C. (2021). Microwaves in Quantum Computing. IEEE Journal of Microwaves, 1(1), 403–427. https://doi.org/10.1109/JMW.2020.3034071
Bayerstadler, A. (2021). Industry quantum computing applications. EPJ Quantum Technology, 8(1). https://doi.org/10.1140/epjqt/s40507-021-00114-x
Blunt, N. S. (2022). Perspective on the Current State-of-the-Art of Quantum Computing for Drug Discovery Applications. Journal of Chemical Theory and Computation, 18(12), 7001–7023. https://doi.org/10.1021/acs.jctc.2c00574
Bravyi, S. (2022). The future of quantum computing with superconducting qubits. Journal of Applied Physics, 132(16). https://doi.org/10.1063/5.0082975
Burg, V. von. (2021). Quantum computing enhanced computational catalysis. Physical Review Research, 3(3). https://doi.org/10.1103/PhysRevResearch.3.033055
Dai, H. (2021). Recent advances in molybdenum-based materials for lithium-sulfur batteries. Research, 2021(Query date: 2024-12-07 07:51:10). https://doi.org/10.34133/2021/5130420
Emani, P. S. (2021). Quantum computing at the frontiers of biological sciences. Nature Methods, 18(7), 701–709. https://doi.org/10.1038/s41592-020-01004-3
Fan, K. (2022). Two-dimensional host materials for lithium-sulfur batteries: A review and perspective. Energy Storage Materials, 50(Query date: 2024-12-07 07:51:10), 696–717. https://doi.org/10.1016/j.ensm.2022.06.009
Giebeler, L. (2021). MXenes in lithium–sulfur batteries: Scratching the surface of a complex 2D material – A minireview. Materials Today Communications, 27(Query date: 2024-12-07 07:51:10). https://doi.org/10.1016/j.mtcomm.2021.102323
Gill, S. S. (2022). Quantum computing: A taxonomy, systematic review and future directions. Software - Practice and Experience, 52(1), 66–114. https://doi.org/10.1002/spe.3039
Gonzalez-Zalba, M. F. (2021). Scaling silicon-based quantum computing using CMOS technology. Nature Electronics, 4(12), 872–884. https://doi.org/10.1038/s41928-021-00681-y
Govia, L. C. G. (2021). Quantum reservoir computing with a single nonlinear oscillator. Physical Review Research, 3(1). https://doi.org/10.1103/PhysRevResearch.3.013077
Hashim, A. (2021). Randomized Compiling for Scalable Quantum Computing on a Noisy Superconducting Quantum Processor. Physical Review X, 11(4). https://doi.org/10.1103/PhysRevX.11.041039
Hegade, N. N. (2021). Shortcuts to Adiabaticity in Digitized Adiabatic Quantum Computing. Physical Review Applied, 15(2). https://doi.org/10.1103/PhysRevApplied.15.024038
Herman, D. (2023). Quantum computing for finance. Nature Reviews Physics, 5(8), 450–465. https://doi.org/10.1038/s42254-023-00603-1
Hu, T. (2021). Movable oil content evaluation of lacustrine organic-rich shales: Methods and a novel quantitative evaluation model. Earth-Science Reviews, 214(Query date: 2024-12-01 09:57:11). https://doi.org/10.1016/j.earscirev.2021.103545
Jian, C. (2020). Quantitative PCR provides a simple and accessible method for quantitative microbiota profiling. PLoS ONE, 15(1). https://doi.org/10.1371/journal.pone.0227285
Kavokin, A. (2022). Polariton condensates for classical and quantum computing. Nature Reviews Physics, 4(7), 435–451. https://doi.org/10.1038/s42254-022-00447-1
Leon, N. P. de. (2021). Materials challenges and opportunities for quantum computing hardware. Science, 372(6539). https://doi.org/10.1126/science.abb2823
Li, Y. (2021). Material design and structure optimization for rechargeable lithium-sulfur batteries. Matter, 4(4), 1142–1188. https://doi.org/10.1016/j.matt.2021.01.012
Liu, H. (2021). A novel method for semi-quantitative analysis of hydration degree of cement by 1H low-field NMR. Cement and Concrete Research, 141(Query date: 2024-12-01 09:57:11). https://doi.org/10.1016/j.cemconres.2020.106329
Liu, K. (2021). Recent progress in organic Polymers-Composited sulfur materials as cathodes for Lithium-Sulfur battery. Chemical Engineering Journal, 417(Query date: 2024-12-07 07:51:10). https://doi.org/10.1016/j.cej.2021.129309
Mangini, S. (2021). Quantum computing models for artificial neural networks. EPL, 134(1). https://doi.org/10.1209/0295-5075/134/10002
Morgado, M. (2021). Quantum simulation and computing with Rydberg-interacting qubits. AVS Quantum Science, 3(2). https://doi.org/10.1116/5.0036562
Mujal, P. (2021). Opportunities in Quantum Reservoir Computing and Extreme Learning Machines. Advanced Quantum Technologies, 4(8). https://doi.org/10.1002/qute.202100027
Nokkala, J. (2021). Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing. Communications Physics, 4(1). https://doi.org/10.1038/s42005-021-00556-w
Outeiral, C. (2021). The prospects of quantum computing in computational molecular biology. Wiley Interdisciplinary Reviews: Computational Molecular Science, 11(1). https://doi.org/10.1002/wcms.1481
Pan, H. (2021). Sandwich structural TixOy-Ti3C2/C3N4 material for long life and fast kinetics Lithium-Sulfur Battery: Bidirectional adsorption promoting lithium polysulfide conversion. Chemical Engineering Journal, 410(Query date: 2024-12-07 07:51:10). https://doi.org/10.1016/j.cej.2021.128424
Rasool, R. U. (2023). Quantum Computing for Healthcare: A Review. Future Internet, 15(3). https://doi.org/10.3390/fi15030094
Shi, C. (2021). A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm. Eurasip Journal on Wireless Communications and Networking, 2021(1). https://doi.org/10.1186/s13638-021-01910-w
Smart, S. E. (2021). Quantum Solver of Contracted Eigenvalue Equations for Scalable Molecular Simulations on Quantum Computing Devices. Physical Review Letters, 126(7). https://doi.org/10.1103/PhysRevLett.126.070504
Suzuki, Y. (2022). Quantum Error Mitigation as a Universal Error Reduction Technique: Applications from the NISQ to the Fault-Tolerant Quantum Computing Eras. PRX Quantum, 3(1). https://doi.org/10.1103/PRXQuantum.3.010345
Xiang, Y. (2022). Status and perspectives of hierarchical porous carbon materials in terms of high-performance lithium–sulfur batteries. Carbon Energy, 4(3), 346–398. https://doi.org/10.1002/cey2.185
Yue, F. (2022). Effects of monosaccharide composition on quantitative analysis of total sugar content by phenol-sulfuric acid method. Frontiers in Nutrition, 9(Query date: 2024-12-01 09:57:11). https://doi.org/10.3389/fnut.2022.963318
Zhang, F. (2021). Multishelled Ni2P Microspheres as Multifunctional Sulfur Host 3D-Printed Cathode Materials Ensuring High Areal Capacity of Lithium-Sulfur Batteries. ACS Sustainable Chemistry and Engineering, 9(17), 6097–6106. https://doi.org/10.1021/acssuschemeng.1c01580
Zhao, F. (2023). Toward high-sulfur-content, high-performance lithium-sulfur batteries: Review of materials and technologies. Journal of Energy Chemistry, 80(Query date: 2024-12-07 07:51:10), 625–657. https://doi.org/10.1016/j.jechem.2023.02.009
Authors
Copyright (c) 2024 Ahmet Demir, Emine Yildiz, Cemil Kaya

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.