AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS)

被引:37
作者
Chen, Honghao [1 ]
Zheng, Yingzhe [2 ]
Li, Jiali [2 ]
Li, Lanyu [1 ]
Wang, Xiaonan [1 ,2 ]
机构
[1] Tsinghua Univ, Dept Chem Engn, Beijing 100084, Peoples R China
[2] Natl Univ Singapore, Dept Chem & Biomol Engn, Singapore 117585, Singapore
基金
国家重点研发计划;
关键词
artificial intelligence; machine learning; nanomaterials; nanocatalysts; renewable energy; battery material; carbon capture; carbon utilization; sustainable development; METAL-ORGANIC FRAMEWORKS; MACHINE LEARNING-MODELS; PEROVSKITE SOLAR-CELLS; LITHIUM-ION BATTERY; HYDROGEN-PRODUCTION; RENEWABLE ENERGY; 2-DIMENSIONAL MATERIALS; NEURAL-NETWORKS; CO2; CAPTURE; LIFE-CYCLE;
D O I
10.1021/acsnano.3c01062
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Zero-carbon energy and negative emission technologiesare crucialfor achieving a carbon neutral future, and nanomaterials have playedcritical roles in advancing such technologies. More recently, dueto the explosive growth in data, the adoption and exploitation ofartificial intelligence (AI) as part of the materials research frameworkhave had a tremendous impact on the development of nanomaterials.AI has enabled revolutionary next-generation paradigms to significantlyaccelerate all stages of material discovery and facilitate the explorationof the enormous design space. In this review, we summarize recentadvancements of AI applications in nanomaterials discovery, with aspecial emphasis on the selected applications of AI and nanotechnologyfor the net-zero emission future including the development of solarcells, hydrogen energy, battery materials for renewable energy, andCO(2) capture and conversion materials for carbon capture,utilization and storage (CCUS) technologies. In addition, we discussthe limitations and challenges of current AI applications in thisarea by identifying the gaps that exist in current development. Finally,we present the prospect for future research directions in order tofacilitate the large-scale applications of artificial intelligencefor advancements in nanomaterials.
引用
收藏
页码:9763 / 9792
页数:30
相关论文
共 224 条
  • [1] Transition to a new era with light-based hydrogen production for a carbon-free society: An overview
    Acar, Canan
    Bicer, Yusuf
    Demir, Murat Emre
    Dincer, Ibrahim
    [J]. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2019, 44 (47) : 25347 - 25364
  • [2] Comparative assessment of hydrogen production methods from renewable and non-renewable sources
    Acar, Canan
    Dincer, Ibrahim
    [J]. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2014, 39 (01) : 1 - 12
  • [3] Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes
    Ahmad, Zeeshan
    Xie, Tian
    Maheshwari, Chinmay
    Grossman, Jeffrey C.
    Viswanathan, Venkatasubramanian
    [J]. ACS CENTRAL SCIENCE, 2018, 4 (08) : 996 - 1006
  • [4] Renewable energy harvesting with the application of nanotechnology: A review
    Ahmadi, Mohammad H.
    Ghazvini, Mahyar
    Nazari, Mohammad Alhuyi
    Ahmadi, Mohammad Ali
    Pourfayaz, Fathollah
    Lorenzini, Giulio
    Ming, Tingzhen
    [J]. INTERNATIONAL JOURNAL OF ENERGY RESEARCH, 2019, 43 (04) : 1387 - 1410
  • [5] Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries
    Allam, Omar
    Cho, Byung Woo
    Kim, Ki Chul
    Jang, Seung Soon
    [J]. RSC ADVANCES, 2018, 8 (69) : 39414 - 39420
  • [6] Density Functional Theory - Machine Learning Approach to Analyze the Bandgap of Elemental Halide Perovskites and Ruddlesden-Popper Phases
    Allam, Omar
    Holmes, Colin
    Greenberg, Zev
    Kim, Ki Chul
    Jang, Seung Soon
    [J]. CHEMPHYSCHEM, 2018, 19 (19) : 2559 - 2565
  • [7] Machine Learning Assisted Prediction of Power Conversion Efficiency of All-Small Molecule Organic Solar Cells: A Data Visualization and Statistical Analysis
    Alwadai, Norah
    Khan, Salah Ud-Din
    Elqahtani, Zainab Mufarreh
    Khan, Shahab Ud-Din
    [J]. MOLECULES, 2022, 27 (18):
  • [8] Machine-Learning Guided Quantum Chemical and Molecular Dynamics Calculations to Design Novel Hole-Conducting Organic Materials
    Antono, Erin
    Matsuzawa, Nobuyuki N.
    Ling, Julia
    Saal, James Edward
    Arai, Hideyuki
    Sasago, Masaru
    Fujii, Eiji
    [J]. JOURNAL OF PHYSICAL CHEMISTRY A, 2020, 124 (40) : 8330 - 8340
  • [9] Catalyst design for dry reforming of methane: Analysis review
    Aramouni, Nicolas Abdel Karim
    Touma, Jad G.
    Abu Tarboush, Belal
    Zeaiter, Joseph
    Ahmad, Mohammad N.
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2018, 82 : 2570 - 2585
  • [10] Thick Electrode Design for Facile Electron and Ion Transport: Architectures, Advanced Characterization, and Modeling
    Arnot, David J.
    Mayilvahanan, Karthik S.
    Hui, Zeyu
    Takeuchi, Kenneth J.
    Marschilok, Amy C.
    Bock, David C.
    Wang, Lei
    West, Alan C.
    Takeuchi, Esther S.
    [J]. ACCOUNTS OF MATERIALS RESEARCH, 2022, 3 (04): : 472 - 483