Recent Advance of Machine Learning in Selecting New Materials

Article Properties
  • Language
    English
  • DOI (url)
    10.6023/a22110446
  • Publication Date
    2023/01/01
  • Journal
    Acta Chimica Sinica
  • Indian UGC (journal)
  • Refrences
    147
  • Xingyi Qi
  • Yaofeng Hu
  • Ruoyu Wang
  • Yaqing Yang
  • Yufei Zhao
Cite
Qi, Xingyi, et al. “Recent Advance of Machine Learning in Selecting New Materials”. Acta Chimica Sinica, vol. 81, no. 2, 2023, p. 158, https://doi.org/10.6023/a22110446.
Qi, X., Hu, Y., Wang, R., Yang, Y., & Zhao, Y. (2023). Recent Advance of Machine Learning in Selecting New Materials. Acta Chimica Sinica, 81(2), 158. https://doi.org/10.6023/a22110446
Qi X, Hu Y, Wang R, Yang Y, Zhao Y. Recent Advance of Machine Learning in Selecting New Materials. Acta Chimica Sinica. 2023;81(2):158.
Journal Categories
Science
Chemistry
Science
Chemistry
General
Including alchemy
Refrences
Title Journal Journal Categories Citations Publication Date
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  • Science: Chemistry: Physical and theoretical chemistry
  • Technology: Chemical technology
  • Science: Chemistry
  • Technology: Chemical technology
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials
 274 2017
10.6023/A20070306
Accelerated discovery of CO2 electrocatalysts using active machine learning Nature
  • Science: Science (General)
 800 2020
Doping-Induced Charge Localization Suppresses Electron–Hole Recombination in Copper Zinc Tin Sulfide: Quantum Dynamics Combined with Deep Neural Networks Analysis The Journal of Physical Chemistry Letters
  • Science: Chemistry: Physical and theoretical chemistry
  • Technology: Chemical technology
  • Science: Chemistry
  • Science: Physics: Atomic physics. Constitution and properties of matter
  • Science: Chemistry: Physical and theoretical chemistry
  • Science: Chemistry
 15 2021
Machine Learning Band Gaps of Doped-TiO2 Photocatalysts from Structural and Morphological Parameters ACS Omega
  • Science: Chemistry
  • Science: Chemistry: General. Including alchemy
  • Science: Chemistry
 116 2020