Ziyuan Wang
ABSTRACT
As global energy demand continues to rise and environmental concerns become more pressing, the exploration and advancement of innovative energy materials have emerged as a paramount subject in contemporary societies. AI, being a cutting-edge technology, is displaying immense potential and future applications across diverse domains. The increasing popularity of artificial intelligence technology has caught the attention of many in this particular setting. AI has enormous potential when it comes to studying new energy materials and environmental conservation. As AI continues to advance, it is revealing immense potential in the realm of new energy materials, driven by the expanding need for sustainable energy in society, amidst the rapid progress of science and technology. AI makes contributions to sustainable energy encompass more than just expediting the exploration and creation of fresh energy materials; it also enhances their efficacy while mitigating expenses, expediting the realization of sustainable energy.
- Yin, Zhigang, Jiajun Wei, and Qingdong Zheng. "Interfacial materials for organic solar cells: recent advances and perspectives. " Advanced Science 3. 8. 2016: 1500362.Google Scholar
- Appleby, A. J. "Fuel cell technology: Status and future prospects. " Energy 21. 7-8 (1996): 521-653. De Luna, Phil, "Use machine learning to find energy materials. " Nature 552. 7683. 2017: 23-27.Google Scholar
- egmark, Max. Life 3. 0: Being human in the age of artificial intelligence. Vintage, 2018.Google Scholar
- Andrews-Speed, Philip, "The global resource nexus: the struggles for land, energy, food, water, and minerals. ". 2012.Google Scholar
- McKibben, Bill. The end of nature. Random House Trade Paperbacks, 2006.Google Scholar
- Domingos, Pedro. The master algorithm: How the quest for the ultimate learning machine will remake our world. Basic Books, 2015.Google Scholar
- Brynjolfsson, Erik, and Andrew McAfee. The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company, 2014.Google ScholarDigital Library
- Tegmark, Max. Life 3. 0: Being human in the age of artificial intelligence. Vintage, 2018.Google Scholar
- Pandey, Adarsh Kumar, "Recent advances in solar photovoltaic systems for emerging trends and advanced applications. " Renewable and Sustainable Energy Reviews 53. 2016: 859-884.Google Scholar
- Sathyajith, Mathew, and Geeta Susan Philip, eds. Advances in wind energy conversion technology. Springer Science & Business Media, 2011.Google ScholarCross Ref
- Yue, Meiling, "Hydrogen energy systems: A critical review of technologies, applications, trends and challenges. " Renewable and Sustainable Energy Reviews 146. 2021: 111180.Google Scholar
- Zimmermann, Iwan, "High‐efficiency perovskite solar cells using molecularly engineered, thiophene‐rich, hole‐transporting materials: influence of alkyl chain length on power conversion efficiency. " Advanced Energy Materials 7. 6. 2017: 1601674.Google Scholar
- Panwar, N. Lꎬ, S. Cꎬ Kaushik, and Surendra Kothari. "Role of renewable energy sources in environmental protection: A review. " Renewable and sustainable energy reviews 15. 3. 2011: 1513-1524.Google Scholar
- Campos-Guzmán, Verónica, "Life Cycle Analysis with Multi-Criteria Decision Making: A review of approaches for the sustainability evaluation of renewable energy technologies. " Renewable and Sustainable Energy Reviews 104. 2019: 343-366.Google Scholar
- Fu, Rui, "Enhanced long-term stability of perovskite solar cells by 3-hydroxypyridine dipping. " Chemical Communications 53. 11. 2017: 1829-1831.Google Scholar
- Dai, Qiang, "Life cycle analysis of lithium-ion batteries for automotive applications. " Batteries 5. 2. 2019: 48.Google Scholar
- Palomares, Verónica, "Na-ion batteries, recent advances and present challenges to become low cost energy storage systems. " Energy & Environmental Science 5. 3. 2012: 5884-5901.Google Scholar
- Østergaard, Poul Alberg, "Sustainable development using renewable energy technology. " Renewable energy 146. 2020: 2430-2437.Google Scholar
- Torres, José F. , "Deep learning for time series forecasting: a survey. " Big Data 9. 1. 2021: 3-21.Google Scholar
- Adewumi, Aderemi O. , and Andronicus A. Akinyelu. "A survey of machine-learning and nature-inspired based credit card fraud detection techniques. " International Journal of System Assurance Engineering and Management 8. 2017: 937-953.Google Scholar
- Tabor, Daniel P. , "Accelerating the discovery of materials for clean energy in the era of smart automation. " Nature reviews materials 3. 5. 2018: 5-20.Google Scholar
- Gomes, Carla P. , Bart Selman, and John M. Gregoire. "Artificial intelligence for materials discovery. " MRS Bulletin 44. 7. 2019: 538-544.Google Scholar
- Pilania, Ghanshyam, "Accelerating materials property predictions using machine learning. " Scientific reports 3. 1. 2013: 2810.Google Scholar
- Ren, Fang, "Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments. " Science advances 4. 4. 2018: eaaq1566.Google Scholar
- Raccuglia, Paul, "Machine-learning-assisted materials discovery using failed experiments. " Nature 533. 7601. 2016: 73-76.Google Scholar
- Vasudevan, Rama, Ghanshyam Pilania, and Prasanna V. Balachandran. "Machine learning for materials design and discovery. " Journal of Applied Physics 129. 7. 2021.Google Scholar
- Nasiri, Sara, and Mohammad Reza Khosravani. "Machine learning in predicting mechanical behavior of additively manufactured parts. " Journal of materials research and technology 14. 2021: 1137-1153.Google Scholar
- Fujimura, Koji, "Accelerated materials design of lithium superionic conductors based on first‐principles calculations and machine learning algorithms. " Advanced Energy Materials 3. 8. 2013: 980-985.Google Scholar
- Tarascon, J. M. , and Michel Armand. "Materials for sustainable energy: a collection of peer-reviewed research and review articles from Nature Publishing Group. " World Scientific 414. 2011: 171-179.Google Scholar
- Chu, Steven, Yi Cui, and Nian Liu. "The path towards sustainable energy. " Nature materials 16. 1. 2017: 16-22.Google Scholar
- Dincer, Ibrahim. "Renewable energy and sustainable development: a crucial review. " Renewable and sustainable energy reviews 4. 2. 2000: 157-175.Google Scholar
- Peake, Stephen. Renewable energy-power for a sustainable future. No. Ed. 4. OXFORD university press, 2018.Google Scholar
- Ahmad, Tanveer, "Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. " Journal of Cleaner Production 289. 2021: 125834.Google Scholar
- Gómez-Bombarelli, Rafael, "Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. " Nature materials 15. 10. 2016: 1120-1127.Google Scholar
- Zhuo, Ya, Aria Mansouri Tehrani, and Jakoah Brgoch. "Predicting the band gaps of inorganic solids by machine learning. " The journal of physical chemistry letters 9. 7. 2018: 1668-1673.Google Scholar
- Anderson, Ryther, "Role of pore chemistry and topology in the CO2 capture capabilities of MOFs: from molecular simulation to machine learning. " Chemistry of Materials 30. 18. 2018: 6325-6337.Google Scholar
- Muraoka, Koki, "Linking synthesis and structure descriptors from a large collection of synthetic records of zeolite materials. " Nature communications 10. 1. 2019: 4459.Google Scholar
- Han, Yanqiang, "Machine learning accelerates quantum mechanics predictions of molecular crystals. " Physics Reports 934. 2021: 1-71.Google Scholar
- Xie, Tian, and Jeffrey C. Grossman. "Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. " Physical review letters 120. 14. 2018: 145301.Google Scholar
- Ozerdem, Mehmet Sirac, and Sedat Kolukisa. "Artificial Neural Network approach to predict mechanical properties of hot rolled, nonresulfurized, AISI 10xx series carbon steel bars. " Journal of Materials Processing Technology 199. 1-3. 2008: 437-439.Google Scholar
- Naser, M. Z. "Deriving temperature-dependent material models for structural steel through artificial intelligence. " Construction and Building Materials 191. 2018: 56-68.Google Scholar
- Persson, Magnus, "Predicting the dielectric constant–water content relationship using artificial neural networks. " Soil Science Society of America Journal 66. 5. 2002: 1424-1429.Google Scholar
- Carrete, Jesús, "Finding unprecedentedly low-thermal-conductivity half-Heusler semiconductors via high-throughput materials modeling. " Physical Review X 4. 1. 2014: 011019.Google Scholar
- Ahmadloo, Ebrahim, and Sadra Azizi. "Prediction of thermal conductivity of various nanofluids using artificial neural network. " International Communications in Heat and Mass Transfer 74. 2016: 69-75.Google Scholar
Index Terms
- Application of Artificial Intelligence in New Energy Materials
Recommendations
Analysis on the Application of Artificial Intelligence Technology in Electronic Music Composition Creation
ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationWith the introduction and rise of current artificial intelligence technology as a national development strategy, how to apply artificial intelligence technology to music has developed into an emerging scientific research topic. In this field, using ...
Artificial intelligence
AbstractArtificial intelligence (AI) is the Science and Engineering domain concerned with the theory and practice of developing systems that exhibit the characteristics we associate with intelligence in human behavior. Starting with a brief history of ...
Comments