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Abstract Carbon nanomaterials are promising metal‐free catalysts for energy conversion and storage, but the catalysts are usually developed via traditional trial‐and‐error methods. To rationally design and accelerate the search for the highly efficient catalysts, it is necessary to establish design principles for the carbon‐based catalysts. Here, theoretical analysis and material design of metal‐free carbon nanomaterials as efficient photo‐/electrocatalysts to facilitate the critical chemical reactions in clean and sustainable energy technologies are reviewed. These reactions include the oxygen reduction reaction in fuel cells, the oxygen evolution reaction in metal–air batteries, the iodine reduction reaction in dye‐sensitized solar cells, the hydrogen evolution reaction in water splitting, and the carbon dioxide reduction in artificial photosynthesis. Basic catalytic principles, computationally guided design approaches and intrinsic descriptors, catalytic material design strategies, and future directions are discussed for the rational design and synthesis of highly efficient carbon‐based catalysts for clean energy technologies.
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Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning
Abstract Brønsted‐Evans‐Polanyi (BEP) relationships, i. e., a linear scaling between reaction and activation energies, lie at the core of computational design of heterogeneous catalysts. However, BEPs are not general and often require reparameterization for each class of reactions. Here we construct generalized BEPs (gBEPs), which can predict activation energies for a diverse dataset of reactions of C, O, N and H containing molecules on metal surfaces. In a first step we develop a set of descriptors based on scaling relationships that can capture the change in chemical identity of reactants during the reaction. Subsequently, we use the reaction energy, these descriptors and a single descriptor for the surface structure to parameterize machine learning based regression approaches for the prediction of activation energies. The best approach we developed shows a Mean Absolute Error (MAE) of 0.11 eV for the training set (80 % of the data set) and 0.23 eV for the test set (20 % of the data set). The methodology presented here allows to calculate activation energies within fractions of seconds on a typical personal computer and due to its generality, accuracy and simplicity in application it might prove to be useful in transition metal catalyst design.
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- Award ID(s):
- 1800284
- PAR ID:
- 10380748
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- ChemCatChem
- Volume:
- 14
- Issue:
- 24
- ISSN:
- 1867-3880
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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