Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and GW

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• Finished

  LEARNSOLAR: Rinke-LearnSolar

  Rinke, P. (Principal investigator)

  01/09/2020 → 31/08/2024

  Project: Academy of Finland: Other research funding

  • Machine Learning 100%
  • Density 97%
  • Material 70%
  • Perovskites 50%
  • Density Functional Theory 46%

• -: Formation of CO, CH4 and CH3OH by electrochemical reduction of CO2

  Caro, M. (Principal investigator)

  01/01/2020 → 31/12/2023

  Project: Academy of Finland: Other research funding

  • Density 100%
  • Machine Learning 80%
  • Carbon Dioxide 80%
  • Density Functional Theory 77%
  • Surface 55%

• COMPEX: Towards accurate computational experimentation: machine-learning-driven simulation of nanocarbon synthesis

  Caro, M. (Principal investigator)

  01/09/2019 → 31/08/2023

  Project: Academy of Finland: Other research funding

  • Density 100%
  • Machine Learning 89%
  • Density Functional Theory 54%
  • Carbon 52%
  • Approximation 52%

• New frontiers in computational spectroscopy: Advancing the GW method for molecular core excitations

  Golze, D. (Principal investigator)

  01/09/2018 → 31/08/2021

  Project: Academy of Finland: Other research funding

  • Binding Energy 100%
  • Cores 100%
  • Energy 72%
  • Atom 71%
  • Procedure 68%

• Accurate computational electrochemistry from density functional theory and multiscale

  Caro, M. (Principal investigator)

  01/09/2017 → 31/08/2020

  Project: Academy of Finland: Other research funding

  • Density 100%
  • Density Functional Theory 94%
  • Carbon 80%
  • Machine Learning 78%
  • Material 77%