Machine learning-assisted development of organic photovoltaics via high-throughput in situ formulation

被引：0
|
作者：
An, Na Gyeong [1 ,2 ]
Kim, Jin Young [2 ]
Vak, Doojin [1 ]
机构：
[1] Commonwealth Scientific and Industrial Research Organisation (CSIRO) Manufacturing, Clayton,Victoria,3168, Australia
[2] School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan,44919, Korea, Republic of
来源：
Energy and Environmental Science | 2021年 / 14卷 / 06期
关键词：
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Efficiency
引用
收藏
页码：3438 / 3446
相关论文
共 50 条
  • [31] Machine learning-assisted high-throughput exploration of interface energy space in multi-phase-field model with CALPHAD potential
    Vahid Attari
    Raymundo Arroyave
    Materials Theory, 6 (1):
  • [32] Machine learning assisted designing of organic semiconductors for organic solar cells: High-throughput screening and reorganization energy prediction
    Katubi, Khadijah Mohammedsaleh
    Saqib, Muhammad
    Maryam, Momina
    Mubashir, Tayyaba
    Tahir, Mudassir Hussain
    Sulaman, Muhammad
    Alrowaili, Z. A.
    Al-Buriahi, M. S.
    INORGANIC CHEMISTRY COMMUNICATIONS, 2023, 151
  • [33] High-throughput and machine learning approaches for the discovery of metal organic frameworks
    Zhang, Xiangyu
    Xu, Zezhao
    Wang, Zidi
    Liu, Huiyu
    Zhao, Yingbo
    Jiang, Shan
    APL MATERIALS, 2023, 11 (06)
  • [34] Discovery of New Plasmonic Metals via High-Throughput Machine Learning
    Shapera, Ethan P.
    Schleife, Andre
    ADVANCED OPTICAL MATERIALS, 2022, 10 (18)
  • [35] Machine Learning-Assisted High-Throughput Screening of Metal-Organic Frameworks for CO2 Separation from CO2-Rich Natural Gas
    Zhou, Yinjie
    Ji, Sibei
    He, Songyang
    Fan, Wei
    Zan, Liang
    Zhou, Li
    Ji, Xu
    He, Ge
    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2024, 63 (38) : 16497 - 16508
  • [36] Machine Learning-Assisted High-Throughput Strategy for Real-Time Detection of Spermine Using a Triple-Emission Ratiometric Probe
    Wu, Chun
    Tan, Ping
    Chen, Xianjin
    Chang, Hongrong
    Chen, Yuhui
    Su, Gehong
    Liu, Tao
    Lu, Zhiwei
    Sun, Mengmeng
    Wang, Yanying
    Zou, Yuanfeng
    Wang, Jian
    Rao, Hanbing
    ACS APPLIED MATERIALS & INTERFACES, 2023, 15 (41) : 48506 - 48518
  • [37] Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks
    Miyake, Yuta
    Saeki, Akinori
    JOURNAL OF PHYSICAL CHEMISTRY LETTERS, 2021, 12 (51): : 12391 - 12401
  • [38] Machine Learning Assisted Bad Data Detection for High-throughput Substation Communication
    Sourav, Suman
    Biswas, Partha P.
    Mohanraj, Vyshnavi
    Chen, Binbin
    Mashima, Daisuke
    ICC 2023-IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, 2023, : 4131 - 4137
  • [39] Machine Learning-Assisted Discovery of High-Voltage Organic Materials for Rechargeable Batteries
    Xu, Shangqian
    Liang, Jiechun
    Yu, Yunduo
    Liu, Rulin
    Xu, Yao
    Zhu, Xi
    Zhao, Yu
    JOURNAL OF PHYSICAL CHEMISTRY C, 2021, 125 (39): : 21352 - 21358
  • [40] Machine learning and high-throughput quantum chemistry methods for the discovery of organic materials
    Aspuru-Guzik, Alan
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2016, 251
12345