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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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Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures
Abstract The prediction of temperature effects from first principles is computationally demanding and typically too approximate for the engineering of high-temperature processes. Here, we introduce a hybrid approach combining zero-Kelvin first-principles calculations with a Gaussian process regression model trained on temperature-dependent reaction free energies. We apply this physics-based machine-learning model to the prediction of metal oxide reduction temperatures in high-temperature smelting processes that are commonly used for the extraction of metals from their ores and from electronics waste and have a significant impact on the global energy economy and greenhouse gas emissions. The hybrid model predicts accurate reduction temperatures of unseen oxides, is computationally efficient, and surpasses in accuracy computationally much more demanding first-principles simulations that explicitly include temperature effects. The approach provides a general paradigm for capturing the temperature dependence of reaction free energies and derived thermodynamic properties when limited experimental reference data is available.
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- Award ID(s):
- 1940290
- PAR ID:
- 10360670
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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