Unlocking new horizons, challenges of integrating machine learning to energy conversion and storage research

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., et al. (2013) · APL Materials · doi:10.1063/1.4812323 · Cited by 13,094 · cited 1× · see citing works

The Cambridge Structural Database

Groom, C. R., Bruno, I. J., Lightfoot, M. P., & Ward, S. C. (2016) · Acta Crystallographica Section B Structural Science, Crystal Engineering and Materials · doi:10.1107/s2052520616003954 · Cited by 10,297 · see citing works

Machine learning for molecular and materials science

Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968 · see citing works

Data-driven prediction of battery cycle life before capacity degradation

Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z., Chen, M. H., Aykol, M., Herring, P. K., Fraggedakis, D., et al. (2019) · Nature Energy · doi:10.1038/s41560-019-0356-8 · Cited by 3,228 · see citing works

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

Xie, T., & Grossman, J. C. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.145301 · Cited by 2,828 · see citing works

Recent advances and applications of machine learning in solid-state materials science

Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0221-0 · Cited by 2,655 · see citing works

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

Saal, J. E., Kirklin, S. J., Aykol, M., Meredig, B., & Wolverton, C. M. (2013) · JOM · doi:10.1007/s11837-013-0755-4 · Cited by 2,514 · see citing works

Computational predictions of energy materials using density functional theory

Jain, A., Shin, Y., & Persson, K. A. (2016) · Nature Reviews Materials · doi:10.1038/natrevmats.2015.4 · Cited by 924 · see citing works

Recent developments in the Inorganic Crystal Structure Database: theoretical crystal structure data and related features

Zagorac, D., Müller, H., Ruehl, S., Zagorac, J., & Rehme, S. (2019) · Journal of Applied Crystallography · doi:10.1107/s160057671900997x · Cited by 750 · see citing works

A Critical Review of Machine Learning of Energy Materials

Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201903242 · Cited by 609 · see citing works

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

Choudhary, K., Garrity, K. F., Reid, A. C. E., DeCost, B., Biacchi, A. J., Walker, A. R. H., Trautt, Z., Hattrick-Simpers, J., Kusne, A. G., Centrone, A., et al. (2020) · npj Computational Materials · doi:10.1038/s41524-020-00440-1 · Cited by 582 · see citing works

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

Mai, H., Le, T. C., Chen, D., Winkler, D. A., & Caruso, R. A. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00061 · Cited by 574 · see citing works

Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments

Ren, F., Ward, L. T., Williams, T., Laws, K. J., Wolverton, C., Hattrick-Simpers, J. R., & Mehta, A. (2018) · Science Advances · doi:10.1126/sciadv.aaq1566 · Cited by 519 · see citing works

Machine learning assisted materials design and discovery for rechargeable batteries

Liu, Y., Guo, B., Zou, X., Li, Y., & Shi, S. (2020) · Energy storage materials · doi:10.1016/j.ensm.2020.06.033 · Cited by 472 · see citing works

Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries

Lv, C., Zhou, X., Zhong, L., Yan, C., Srinivasan, M., Seh, Z. W., Liu, C., Pan, H., Li, S., Wen, Y., et al. (2021) · Advanced Materials · doi:10.1002/adma.202101474 · Cited by 424 · see citing works

Machine learning for high-entropy alloys: Progress, challenges and opportunities

Liu, X., Zhang, J., & Pei, Z. (2022) · Progress in Materials Science · doi:10.1016/j.pmatsci.2022.101018 · Cited by 417 · see citing works

Artificial intelligence and machine learning in energy systems: A bibliographic perspective

Entezari, A., Aslani, A., Zahedi, R., & Noorollahi, Y. (2022) · Energy Strategy Reviews · doi:10.1016/j.esr.2022.101017 · Cited by 388 · see citing works

Machine learning: Accelerating materials development for energy storage and conversion

Chen, A., Zhang, X., & Zhou, Z. P. (2020) · InfoMat · doi:10.1002/inf2.12094 · Cited by 373 · see citing works

Toward Computational Materials Design: The Impact of Density Functional Theory on Materials Research

Häfner, J., Wolverton, C., & Ceder, G. (2006) · MRS Bulletin · doi:10.1557/mrs2006.174 · Cited by 370 · see citing works

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Nandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00347 · Cited by 358 · see citing works

Machine Learning-Assisted Discovery of Solid Li-Ion Conducting Materials

Sendek, A. D., Cubuk, E. D., Antoniuk, E. R., Cheon, G., Cui, Y., & Reed, E. J. (2018) · Chemistry of Materials · doi:10.1021/acs.chemmater.8b03272 · Cited by 329 · see citing works

Classification and Regression Trees(CART)Theory and Applications

Timofeev, R. V. (2004) · Cited by 265 · see citing works

Progress in flexible energy storage and conversion systems, with a focus on cable-type lithium-ion batteries

Lee, S., Choi, K.-H., Choi, W. S., Kwon, Y. H., Jung, H., Shin, H., & Kim, J. Y. (2013) · Energy & Environmental Science · doi:10.1039/c3ee24260a · Cited by 264 · see citing works

Machine learning for advanced energy materials

Yun, L., Esan, O. C., Pan, Z., & An, L. (2021) · Energy and AI · doi:10.1016/j.egyai.2021.100049 · Cited by 215 · see citing works

A Comprehensive Review of Microgrid Energy Management Strategies Considering Electric Vehicles, Energy Storage Systems, and AI Techniques

Khan, M. R., Haider, Z. M., Malik, F. H., Almasoudi, F. M., Alatawi, K. S. S., & Bhutta, M. S. (2024) · Processes · doi:10.3390/pr12020270 · Cited by 193 · see citing works

A Data-Driven Method for Extracting Aging Features to Accurately Predict the Battery Health

Xiong, R., Sun, Y., Wang, C., Tian, J., Chen, X., Li, H., & Zhang, Q. (2023) · Energy storage materials · doi:10.1016/j.ensm.2023.02.034 · Cited by 190 · see citing works

Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects

Liu, Y., Tan, X., Liang, J., Han, H., Xiang, P., & Yan, W. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202214271 · Cited by 174 · cited 1× · see citing works

Current State and Future Prospects for Electrochemical Energy Storage and Conversion Systems

Abbas, Q., Mirzaeian, M., Hunt, M., Hall, P. J., & Raza, R. (2020) · Energies · doi:10.3390/en13215847 · Cited by 142 · see citing works

Performance prediction and analysis of a PEM fuel cell operating on pure oxygen using data-driven models: A comparison of artificial neural network and support vector machine

Han, I.-S., & Chung, C.-B. (2016) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2016.04.247 · Cited by 141 · see citing works

Battery management solutions for li-ion batteries based on artificial intelligence

Khawaja, Y., Shankar, N., Qiqieh, I., Alzubi, J. A. A., Alzubi, O. A., Nallakaruppan, M. K., & Padmanaban, S. (2023) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2023.102213 · Cited by 140 · see citing works

Batteries temperature prediction and thermal management using machine learning: An overview

Miaari, A. A., & Ali, H. M. (2023) · Energy Reports · doi:10.1016/j.egyr.2023.08.043 · Cited by 135 · see citing works

Machine learning for battery systems applications: Progress, challenges, and opportunities

Nozarijouybari, Z., & Fathy, H. K. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.234272 · Cited by 135 · see citing works

AI methods in materials design, discovery and manufacturing: A review

Papadimitriou, I., Gialampoukidis, I., Vrochidis, S., & Kompatsiaris, I. (2024) · Computational materials science · doi:10.1016/j.commatsci.2024.112793 · Cited by 133 · see citing works

Recent advances in artificial intelligence boosting materials design for electrochemical energy storage

Liu, X.-B., Fan, K., Huang, X., Ge, J., Liu, Y., & Kang, H. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.151625 · Cited by 132 · see citing works

A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries

Wang, S., Ren, P., Takyi‐Aninakwa, P., Jin, S., & Fernández, C. (2022) · Energies · doi:10.3390/en15145053 · Cited by 128 · see citing works

A Generative Approach to Materials Discovery, Design, and Optimization

Menon, D., & Ranganathan, R. (2022) · ACS Omega · doi:10.1021/acsomega.2c03264 · Cited by 127 · see citing works

Artificial intelligence in support of the circular economy: ethical considerations and a path forward

Roberts, H., Zhang, J., Bariach, B., Cowls, J., Gilburt, B., Juneja, P., Tsamados, A., Ziosi, M., Taddeo, M., & Floridi, L. (2024) · AI & SOCIETY · doi:10.1007/s00146-022-01596-8 · Cited by 123 · see citing works

Machine learning assisted advanced battery thermal management system: A state-of-the-art review

Li, A., Weng, J., Yuen, A. C. Y., Wang, W., Liu, H., Lee, E. W. M., Wang, J., Kook, S., & Yeoh, G. H. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.106688 · Cited by 121 · see citing works

Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation

Fuhr, A., & Sumpter, B. G. (2022) · Frontiers in Materials · doi:10.3389/fmats.2022.865270 · Cited by 120 · see citing works

Perspective: Composition–structure–property mapping in high-throughput experiments: Turning data into knowledge

Hattrick-Simpers, J. R., Gregoire, J. M., & Kusne, A. G. (2016) · APL Materials · doi:10.1063/1.4950995 · Cited by 111 · see citing works

First-principles study of alkali-metal intercalation in disordered carbon anode materials

Huang, J., Cśanyi, G., Zhao, J., Cheng, J. J., & Deringer, V. L. (2019) · Journal of Materials Chemistry A · doi:10.1039/c9ta05453g · Cited by 102 · see citing works

Machine learning assisted synthesis of lithium-ion batteries cathode materials

Liow, C. H., Kang, H., Kim, S.-G., Na, M., Lee, Y., Baucour, A., Bang, K., Shim, Y., Choe, J., Hwang, G., et al. (2022) · Nano Energy · doi:10.1016/j.nanoen.2022.107214 · Cited by 102 · see citing works

Machine Learning Modeling for Accelerated Battery Materials Design in the Small Data Regime

Sendek, A. D., Ransom, B., Cubuk, E. D., Pellouchoud, L. A., Nanda, J., & Reed, E. J. (2022) · Advanced Energy Materials · doi:10.1002/aenm.202200553 · Cited by 100 · see citing works

Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis

Lin, J., Liu, Z., Guo, Y., Wang, S., Zhang, T., Xue, X., Li, R., Feng, S., Wang, L., Liu, J., et al. (2023) · Nano Today · doi:10.1016/j.nantod.2023.101802 · Cited by 99 · see citing works

Machine-Learning Approach for Predicting the Discharging Capacities of Doped Lithium Nickel–Cobalt–Manganese Cathode Materials in Li-Ion Batteries

Wang, G., Fearn, T., Wang, T., & Choy, K. (2021) · ACS Central Science · doi:10.1021/acscentsci.1c00611 · Cited by 95 · see citing works

Capacity estimation of batteries: Influence of training dataset size and diversity on data driven prognostic models

Nagulapati, V. M., Lee, H., Jung, D., ́, B. B., Choi, Y., & Lim, H. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.108048 · Cited by 94 · see citing works

Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization

Xu, L., Wu, F., Chen, R., & Li, L. (2023) · Energy storage materials · doi:10.1016/j.ensm.2023.102785 · Cited by 89 · see citing works

Machine learning in energy storage material discovery and performance prediction

Huang, G., Huang, F., & Dong, W. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.152294 · Cited by 81 · see citing works

Ab Initio Calculations of the Redox Potentials of Additives for Lithium-Ion Batteries and Their Prediction through Machine Learning

Okamoto, Y., & Kubo, Y. (2018) · ACS Omega · doi:10.1021/acsomega.8b00576 · Cited by 77 · see citing works

Machine-Learning-Driven Advanced Characterization of Battery Electrodes

Finegan, D. P., Squires, I., Dahari, A., Kench, S., Jungjohann, K., & Cooper, S. J. (2022) · ACS Energy Letters · doi:10.1021/acsenergylett.2c01996 · Cited by 76 · see citing works

Machine learning enabled customization of performance-oriented hydrogen storage materials for fuel cell systems

Zhou, P., Xiao, X., Zhu, X., Chen, Y., Lü, W., Piao, M., Cao, Z., Lu, M., Fang, F., Li, Z., et al. (2023) · Energy storage materials · doi:10.1016/j.ensm.2023.102964 · Cited by 76 · see citing works

Semi-supervised adversarial deep learning for capacity estimation of battery energy storage systems

Yao, J., Chang, Z., Han, T., & Tian, J. (2024) · Energy · doi:10.1016/j.energy.2024.130882 · Cited by 75 · see citing works

Simulation and design of energy materials accelerated by machine learning

Wang, H., Ji, Y., & Li, Y. (2019) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1421 · Cited by 74 · see citing works

Predictive descriptors in machine learning and data-enabled explorations of high-entropy alloys

Roy, A., & Balasubramanian, G. (2021) · Computational Materials Science · doi:10.1016/j.commatsci.2021.110381 · Cited by 74 · see citing works

Knowledge-driven design of solid-electrolyte interphases on lithium metal via multiscale modelling

Wagner, J., Kuai, D., Gerasimov, M., Röder, F., Balbuena, P. B., & Krewer, U. (2023) · Nature Communications · doi:10.1038/s41467-023-42212-7 · Cited by 74 · see citing works

Application of machine learning algorithms in predicting the photocatalytic degradation of perfluorooctanoic acid

Navidpour, A. H., Hosseinzadeh, A., Huang, Z., Li, D., & Zhou, J. L. (2022) · Catalysis Reviews · doi:10.1080/01614940.2022.2082650 · Cited by 71 · see citing works

A machine learning-assisted study on organic solvents in electrolytes for expanding the electrochemical stable window of zinc-ion batteries

Xu, G., Zhang, Y., Jiang, M., Li, J., Sun, H., Li, J., Lu, T., Wang, C., Yang, G., & Pan, L. (2023) · Chemical Engineering Journal · doi:10.1016/j.cej.2023.146676 · Cited by 69 · see citing works

Discovery of Energy Storage Molecular Materials Using Quantum Chemistry-Guided Multiobjective Bayesian Optimization

Agarwal, G., Doan, H. A., Robertson, L. A., Zhang, L., & Assary, R. S. (2021) · Chemistry of Materials · doi:10.1021/acs.chemmater.1c02040 · Cited by 68 · see citing works

Machine Learning Boosting the Development of Advanced Lithium Batteries

Liu, Y., Zhou, Q., & Cui, G. (2021) · Small Methods · doi:10.1002/smtd.202100442 · Cited by 66 · see citing works

Machine learning for flow batteries: opportunities and challenges

Li, T., Zhang, C., & Li, X. (2022) · Chemical Science · doi:10.1039/d2sc00291d · Cited by 60 · see citing works

Comparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric Vehicles

Oyucu, S., Doğan, F., Aksöz, A., & Biçer, E. (2024) · Applied Sciences · doi:10.3390/app14062306 · Cited by 54 · see citing works

Enhancing Iterative Dichotomiser 3 algorithm for classification decision tree

Khedr, A. E., Idrees, A. M., & Seddawy, A. I. E. (2016) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1177 · Cited by 52 · see citing works

Towards Sustainable Fuel Cells and Batteries with an AI Perspective

Ramasubramanian, B., Rao, R. P., Chellappan, V., & Ramakrishna, S. (2022) · Sustainability · doi:10.3390/su142316001 · Cited by 52 · see citing works

A review of the use of AI in the mining industry: Insights and ethical considerations for multi-objective optimization

Corrigan, C. C., & Ikonnikova, S. A. (2024) · The Extractive Industries and Society · doi:10.1016/j.exis.2024.101440 · Cited by 52 · see citing works

A Survey of Datasets, Preprocessing, Modeling Mechanisms, and Simulation Tools Based on AI for Material Analysis and Discovery

Imran, I., Qayyum, F., Kim, D., Bong, S.-J., Chi, S., & Choi, Y. (2022) · Materials · doi:10.3390/ma15041428 · Cited by 49 · see citing works

Ionic Transport through the Solid Electrolyte Interphase in Lithium-Ion Batteries: A Review from First-Principles Perspectives

Kulathuvayal, A. S., & Su, Y.-Q. (2023) · ACS Applied Energy Materials · doi:10.1021/acsaem.3c00287 · Cited by 49 · see citing works

Artificial Intelligence‐Based Material Discovery for Clean Energy Future

Maleki, R., Asadnia, M., & Razmjou, A. (2022) · Advanced Intelligent Systems · doi:10.1002/aisy.202200073 · Cited by 48 · see citing works

Machine learning (ML)‐assisted optimization doping of KI in MAPbI 3 solar cells

Jiang, S., Wu, C., Li, F., Zhang, Y., Zhang, Z.-H., Zhang, Q., Chen, Z., Qu, B., Xiao, L., & Jiang, M. (2020) · Rare Metals · doi:10.1007/s12598-020-01579-y · Cited by 40 · see citing works

Heterogeneous intercalated metal-organic framework active materials for fast-charging non-aqueous Li-ion capacitors

Ogihara, N., Hasegawa, M., Kumagai, H., Mikita, R., & Nagasako, N. (2023) · Nature Communications · doi:10.1038/s41467-023-37120-9 · Cited by 33 · see citing works

Material design and mechanism study for zinc ion batteries: Applications of density functional theory calculations and molecular dynamic simulations

Cao, J., Zhao, F., Yang, X.-X., Yan, L.-J., Zhao, Q., Gao, L.-G., Ma, T.-L., Ren, X.-F., Wu, G., & Liu, A.-M. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.153239 · Cited by 33 · see citing works

Hierarchical porous N/S-doped carbon with machine learning to predict advanced potassium-ion batteries

Bi, K., Wang, Y., & Zhou, G. (2023) · Journal of Materials Chemistry A · doi:10.1039/d3ta00247k · Cited by 30 · see citing works

Na3VMn0.5Ti0.5(PO4)3/C with active Na+ hopping sites for high-rate and durable sodium-ion batteries

Soundharrajan, V., Lee, S., Park, S., Piao, J., Mathew, V., & Kim, J. (2023) · Chemical Engineering Journal · doi:10.1016/j.cej.2023.144994 · Cited by 30 · see citing works

Process optimisation for NASICON-type solid electrolyte synthesis using a combination of experiments and bayesian optimisation

Takeda, H., Fukuda, H., Nakano, K., Hashimura, S., Tanibata, N., Nakayama, M., Ono, Y., & Natori, T. (2022) · Materials Advances · doi:10.1039/d2ma00731b · Cited by 29 · see citing works

Generation of Hydro Energy by Using Data Mining Algorithm for Cascaded Hydropower Plant

Parvez, I., Shen, J., Hassan, I., & Zhang, N. (2021) · Energies · doi:10.3390/en14020298 · Cited by 28 · see citing works

Modeling the 4D discharge of lithium-ion batteries with a multiscale time-dependent deep learning framework

Marcato, A., Santos, J. E., Liu, C., Boccardo, G., Marchisio, D. L., & Franco, A. A. (2023) · Energy storage materials · doi:10.1016/j.ensm.2023.102927 · Cited by 28 · see citing works

Improving Ionic Conductivity of Garnet Solid-State Electrolytes Using Gradient Boosting Regression Optimized Machine Learning

Ma, Y., Han, S., Sun, Y. N., Cui, Z., Liu, P., Wang, X., & Wang, Y. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.234492 · Cited by 28 · see citing works

Artificial Intelligence in Support of the Circular Economy: Ethical Considerations and a Path ForwardPreprint

Roberts, H., Zhang, J., Bariach, B., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4080909 · Cited by 23 · see citing works

State-of-Charge Estimation for Lithium-Ion Batteries Using Residual Convolutional Neural Networks

Wang, Y.-C., Shao, N.-C., Chen, G., Hsu, W.-S., & Wu, S. (2022) · Sensors · doi:10.3390/s22166303 · Cited by 23 · see citing works

Machine-learning-assisted discovery of boron-doped graphene with high work function as an anode material for Li/Na/K-ion batteries

Luo, Y., Chen, H., Wang, J., & Niu, X. (2023) · Physical Chemistry Chemical Physics · doi:10.1039/d3cp00669g · Cited by 23 · see citing works

A comprehensive machine learning strategy for designing high-performance photoanode catalysts

Huang, M., Wang, S., & Zhu, H. (2023) · Journal of Materials Chemistry A · doi:10.1039/d3ta04148d · Cited by 21 · see citing works

An Artificial Intelligence Solution for Predicting Short-Term Degradation Behaviors of Proton Exchange Membrane Fuel Cell

Yang, Z., Wang, B., Sheng, X., Wang, Y., Ren, Q., He, S., Xuan, J., & Jiao, K. (2021) · Applied Sciences · doi:10.3390/app11146348 · Cited by 19 · see citing works

Unraveling the phase transition and electrochemical application of MoSe2 material for energy conversion and storage devices

Velpandian, M., Gupta, P., Kuttasseri, A., Mahata, A., & Basu, S. (2024) · Applied Surface Science · doi:10.1016/j.apsusc.2024.160990 · Cited by 17 · see citing works

Machine learning-based evaluation of functional characteristics of Li-rich layered oxide cathode materials using the data of XPS and XRD spectra

Kireeva, N. V., Pervov, V. S., & Tsivadze, A. Y. (2023) · Computational Materials Science · doi:10.1016/j.commatsci.2023.112591 · Cited by 16 · see citing works

Assessment of ionic liquid electrolytes for high‐performance lithium‐sulfur batteries using machine learning

Kilic, A., Yıldırım, R., & Eroğlu, D. (2022) · International Journal of Energy Research · doi:10.1002/er.8611 · Cited by 15 · see citing works

Understanding urea electro synthesis using layered perovskite NdBa0.25Sr0.75Co2O5+ δ cathode material

Velpandian, M., Dhongde, V., Singh, K., Gupta, P., Sarma, D., Mahata, A., & Basu, S. (2023) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2023.08.035 · Cited by 15 · see citing works

A Capacity-Prediction Model for Exploration of Organic Anodes: Discovery of 5-Formylsalicylic Acid as a High-Performance Anode Active Material

Komura, T., Sakano, K., Igarashi, Y., Numazawa, H., Imai, H., & Oaki, Y. (2022) · ACS Applied Energy Materials · doi:10.1021/acsaem.2c01472 · Cited by 14 · see citing works

Artificial intelligence and machine learning in energy storage and conversion

Seh, Z. W., Jiao, K., & Castelli, I. E. (2023) · Energy Advances · doi:10.1039/d3ya90022c · Cited by 14 · see citing works

Environmental and economic benefits of single-atom catalysts in energy conversion and storage

Velpandian, M., Sinhamahapatra, A., & Basu, S. (2023) · Oxford Open Energy · doi:10.1093/ooenergy/oiad015 · Cited by 14 · see citing works

Machine learning-assisted precision inverse design research of ternary cathode materials: A new paradigm for material design

Wang, Y., Wu, C., Ji, W., Wu, Y., Zhao, S., Yang, X., Li, Y., & Zhou, N. (2024) · Journal of Colloid and Interface Science · doi:10.1016/j.jcis.2024.11.104 · Cited by 14 · see citing works

Metal-solvent interaction contribution on voltage for metal ion battery: An interpretable machine learning approach

Manna, S., Manna, S. S., Das, S., & Pathak, B. (2023) · Electrochimica Acta · doi:10.1016/j.electacta.2023.143148 · Cited by 10 · see citing works

Exploring the impact of structural modification of double perovskite composite cathode material on oxygen reduction reaction in intermediate temperature solid oxide fuel cell

Dhongde, V., Velpandian, M., & Basu, S. (2024) · Ionics · doi:10.1007/s11581-024-05847-0 · Cited by 10 · see citing works

Unconstrained Machine Learning Screening for New Li‐Ion Cathode Materials Enhanced by Class Balancing

Dinic, F., & Voznyy, O. (2023) · Advanced Theory and Simulations · doi:10.1002/adts.202300081 · Cited by 9 · see citing works

Application of machine learning-based selective sampling to determine BaZrO3 grain boundary structures

Bondevik, T., Kuwabara, A., & Løvvik, O. M. (2019) · Computational Materials Science · doi:10.1016/j.commatsci.2019.03.054 · Cited by 7 · see citing works

A 3D metallic porous sulfurized carbon anode identified by global structure search for Na-ion batteries with fast diffusion kinetics

Obeid, M. M., Liu, J., Du, P., Liu, T., & Sun, Q. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.110587 · Cited by 7 · see citing works

Comprehensive Machine Learning Pipeline for Prediction of Power Conversion Efficiency in Perovskite Solar Cells

Mohanty, D., & Palai, A. K. (2023) · Advanced Theory and Simulations · doi:10.1002/adts.202300309 · Cited by 6 · see citing works

Effect of hydrated shell layers on surface tension of electrolyte solutions: Insights from interpretable machine learning

Deng, J., & Jia, G. (2022) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2022.120887 · Cited by 5 · see citing works

Machine Learning for Predicting Gas Adsorption Capacities of Metal Organic Framework

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