Artificial Intelligence for Energy Conversion


Artificial Intelligence for Energy Conversion

Tapasi Ghosh, Bhargavi Koneru, Prasun Banerjee

Many aspects of modern life are dependent on energy of various forms, which has already created strain on natural energy reserves and affected our environment adversely. Scientists and researchers are searching for alternative sources of energy that are sustainable, environment friendly, and renewable. However, any developmental work to invent a material or technique as a new source of energy involves a lengthy and complex experimental process to produce in scale. The last decade has seen remarkable progress in the field of Artificial Intelligence (AI) due to advancements of many new computer hardware, software’s, algorithms, technologies, and availability of a large amount of raw input data. We have started harnessing the power of AI to facilitate the process of discovering new materials as alternative energy sources and exploring the different advanced methodologies over the traditional approaches to utilize natural and eco-friendly sources for energy conversion. This book chapter will highlight some of the advancements in Machine Learning and Deep Learning techniques to explore new material resources and methodologies for energy conversion.

AI, ML, ANN, Energy Conversion, Catalysts, Microbial Fuel Cell

Published online , 16 pages

Citation: Tapasi Ghosh, Bhargavi Koneru, Prasun Banerjee, Artificial Intelligence for Energy Conversion, Materials Research Foundations, Vol. 147, pp 123-138, 2023


Part of the book on Application of Artificial Intelligence in New Materials Discovery

[1] I. Dincer, Comprehensive Energy Systems, first ed., Elsevier, 2018.
[2] J. Powles, H. Hodson, Google DeepMind and healthcare in an age of algorithms, Health Technol. (Berl). 7 (2017) 351-367.
[3] X. Yang, Z. Luo, Z. Huang, Y. Zhao, Z. Xue, Y. Wang, W. Liu, S. Liu, H. Zhang, K. Xu, Development status and prospects of artificial intelligence in the field of energy conversion materials, Front. Energy Res. 8 (2020) 167.
[4] F. Rosenblatt, The Perceptron: A perceiving and recognizing automaton, Cornell University, Ithaca, NY, Project PARA, Cornell Aeronautical Laboratory, Rep. (1957) 85-460.
[5] D.O. Hebb, The first stage of perception: Growth of the assembly, in: J.A. Anderson, E. Rosenfeld (Eds.), The Organization of Behavior, Wiley, New York, 1949, pp. 60-78.
[6] R.L. Watrous, L. Shastri, A.H. Waibel, Learned phonetic discrimination using connectionist networks, Proc. European Conference on Speech Technology. 1987, 1377-1380.
[7] K. Naidu, N.S. Kumar, P. Banerjee, B. Reddy, A review on the origin of nanofibers/nanorods structures and applications, J. Mater. Sci. – Mater. Med. 32 (2021) 1-25.
[8] Y. Bengio, Y.L. Cun, Scaling learning algorithms towards AI, in: L. Bottou, O. Chapelle, D. Decoste, J. Weston (Eds.), Large-Scale Kernel Machines, MIT Press, 2007, pp. 1- 41.
[9] K.R.M. Rao, K. Haripriya, P. Banerjee, A. Franco, Microbiologically influenced corrosion, in: N.S. Kumar, P. Banerjee, H. Manjunatha, K.C.B. Naidu (Eds.), Corrosion Science: Modern Trends and Applications, Bentham Science, 2021, pp 121-146.
[10] A. Géron, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O’Reilly Media, Inc., 2019.
[11] N.S. Kumar, K.C.B. Naidu, P. Banerjee, T.A. Babu, B.V.S. Reddy, A review on metamaterials for device applications, Crystals (Basel). 11 (2021) 518.
[12] M.R. Jedla, B. Koneru, A.F. Jr, D. Rangappa, P. Banerjee, Recent developments in nanomaterials based adsorbents for water purification techniques, Biointerface Res. Appl. Chem. 12 (2021) 5821-5835.
[13] Q. Yan, J. Yu, S.K. Suram, L. Zhou, A. Shinde, P.F. Newhouse, W. Chen, G. Li, K.A. Persson, J.M. Gregoire, Solar fuels photoanode materials discovery by integrating high-throughput theory and experiment, Proc. Natl. Acad. Sci. 114 (2017) 3040-3043.
[14] R. Nagai, R. Akashi, O. Sugino, Completing density functional theory by machine learning hidden messages from molecules, NPJ Comput. Mater. 6 (2020) 1-8.
[15] G.R. Schleder, A.C.M. Padilha, C.M. Acosta, M. Costa, A. Fazzio, From DFT to machine learning: Recent approaches to materials science-A review, J. Phys.: Mater. 2 (2019) 032001.
[16] A. Jain, S.P. Ong, G. Hautier, W. Chen, W.D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, Commentary: The Materials Project: A materials genome approach to accelerating materials innovation, APL Mater. 1 (2013) 011002.
[17] S. Kirklin, J.E. Saal, B. Meredig, A. Thompson, J.W. Doak, M. Aykol, S. Rühl, C. Wolverton, The Open Quantum Materials Database (OQMD): aAssessing the accuracy of DFT formation energies, NPJ Comput. Mater. 1 (2015) 1-15.
[18] C.P. Gomes, B. Selman, J.M. Gregoire, Artificial intelligence for materials discovery, MRS Bulletin. 44 (2019) 538-544.
[19] P. Banerjee, A. Franco, K.C.B. Naidu, N.S. Kumar, Water-borne polyurethane metal oxide nanocomposite applications, in: Inamuddin, R. Boddula, A. Khan (Eds.), Sustainable Production and Applications of Waterborne Polyurethanes, Springer, 2021, pp. 155-169.
[20] J.I.G. Peralta, X. Bokhimi, Ternary halide perovskites for possible optoelectronic applications revealed by Artificial Intelligence and DFT calculations, Mater. Chem. Phys. 267 (2021) 124710.
[21] P. Banerjee, A. Franco Jr, K.C.B. Naidu, A. Khan, A.M. Asiri, S. Natarajan, Metal-organic framework-based materials and renewable energy, in: A. Khan, F. Verpoort, A.M. Asiri, M.E. Hoque, A. Bilgrami, M. Azam, K.C.B. Naidu (Eds.), Metal-Organic Frameworks for Chemical Reactions, Elsevier, 2021, pp. 153-166.
[22] B. Koneru, J. Swapnalin, S. Natarajan, A. Franco Jr, P. Banerjee, Intercalation of nanoscale multiferroic spacers between the two-dimensional interlayers of MXene, ACS Omega.7 (2022) 20369-20375.
[23] P. Banerjee, A.F. Jr, R.Z. Xiao, Effects of Y and Ni co-doping in Bi2Fe4O9 BiFeO3 based multiferroic ceramics, Mater. Today: Proc. 46 (2021) 4716-4719.
[24] N. Dropka, M. Holena, Application of artificial neural networks in crystal growth of electronic and opto-electronic materials, Crystals (Basel) 10 (2020) 663.
[25] P.V.V. Romanholo, T.E.P. Alves, J. Swapnalin, P. Banerjee, A.F. Jr, Tailoring the magnetic properties of Zn doped Nickel, Magnesium and Cobalt Ferrite ceramics, Mater. Chem. Phys. 284 (2022) 126072.
[26] K.C.B. Naidu, N.S. Kumar, R. Boddula, S. Ramesh, R. Pothu, P. Banerjee, M. Sarma, H. Manjunatha, B. Kishore, Recent advances in nanomaterials for Li-ion batteries, in: Inamuddin, R. Boddula, M.F. Ahmer, A.M. Asiri (Eds.), Lithium-Ion Batteries: Materials and Applications, Materials Research Forum, 2020, pp. 148 160.
[27] X. Guo, S. Lin, J. Gu, S. Zhang, Z. Chen, S. Huang, Simultaneously achieving high activity and selectivity toward two-electron O2 electroreduction: The power of single-atom catalysts, ACS Catal. 9 (2019) 11042-11054.
[28] B. Koneru, J. Swapnalin, P. Banerjee, K.C.B. Naidu, N.S. Kumar, Materials under extreme pressure: Combining theoretical and experimental techniques, Eur. Phys. J. Spec. Top. 137 (2022) 1-12.
[29] X. Yang, Z. Luo, Z. Huang, Y. Zhao, Z. Xue, Y. Wang, W. Liu, S. Liu, H. Zhang, K. Xu, Development status and prospects of artificial intelligence in the field of energy conversion materials, Front. Energy Res. 8 (2020) 167.
[30] M. Prakash, N.S. Kumar, K.C.B. Naidu, M. Sarma, P. Banerjee, R.J. Kumar, R. Pothu, R. Boddula, Electrode materials for K‐ion batteries and applications, in: Inamuddin, R. Boddula, A.M. Asiri (Eds.), Potassium‐Ion Batteries: Materials and Applications, Wiley, 2020, pp.123-136.
[31] J. Hu, X. Cao, X. Zhao, W. Chen, G. Lu, Y. Dan, Z. Chen, Catalytically active sites on Ni5P4 for efficient Hydrogen evolution reaction from atomic scale calculation, Front. Chem. 7 (2019) 444.
[32] R.S. Melo, A.F. Jr, P. Banerjee, Nanoscale-driven single-domain structure in Nickel substituted superparamagnetic Cobalt Ferrites, Solid State Commun. 341 (2022) 114560.
[33] P. Banerjee, A.F. Jr, R.Z. Xiao, K.C.B. Naidu, R.M. Rao, R. Pothu, R. Boddula, Advancement in electrolytes for rechargeable batteries, in: R. Boddula, Inamuddin, R. Pothu, A.M. Asiri (Eds.), Rechargeable Batteries: History, Progress, and Applications, Wiley, 2020, pp. 87-98.
[34] R. Palkovits, S. Palkovits, Using artificial intelligence to forecast water oxidation catalysts, ACS Catal. 9 (2019) 8383-8387.
[35] K.L. Lesnik, H. Liu, Predicting microbial fuel cell biofilm communities and bioreactor performance using artificial neural networks, Environ. Sci Technol. 51 (2017) 10881-10892.
[36] Z. Yang, B. Wang, X. Sheng, Y. Wang, Q. Ren, S. He, J. Xuan, K. Jiao, An artificial intelligence solution for predicting short-term degradation behaviors of proton exchange membrane fuel cells, Appl. Sci. 11 (2021) 6348.
[37] X. Yang, Z. Luo, Z. Huang, Y. Zhao, Z. Xue, Y. Wang, W. Liu, S. Liu, H. Zhang, K. Xu, Development status and prospects of artificial intelligence in the field of energy conversion materials, Front. Energy Res. 8 (2020) 167.