2020
DOI: 10.1038/s41586-020-2242-8
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Accelerated discovery of CO2 electrocatalysts using active machine learning

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Cited by 880 publications
(693 citation statements)
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References 30 publications
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“…(41) In a typical application of ML, large datasets (often results from thousands of DFT calculations) are used for statistical regression analyses with ML methods to identify the most accurately parametrized model for the dataset. A well-trained ML model should successfully interpolate within the chemical/materials space of the training data and be useful for screening molecular/material properties for hypothetical homogeneous (42,43) and/or heterogeneous (44,45) catalyst active sites across larger regions of chemical and materials space than is accessible with DFT calculations alone. Complementary to ML approaches, theoretical schemes such as alchemical perturbation DFT allow rapid screening of adsorbate binding energies (46) with minimal pre-calculated reference data and low computational cost.…”
Section: The Origins Of Divergent Reactivity Between Eams and Pgmsmentioning
confidence: 99%
“…(41) In a typical application of ML, large datasets (often results from thousands of DFT calculations) are used for statistical regression analyses with ML methods to identify the most accurately parametrized model for the dataset. A well-trained ML model should successfully interpolate within the chemical/materials space of the training data and be useful for screening molecular/material properties for hypothetical homogeneous (42,43) and/or heterogeneous (44,45) catalyst active sites across larger regions of chemical and materials space than is accessible with DFT calculations alone. Complementary to ML approaches, theoretical schemes such as alchemical perturbation DFT allow rapid screening of adsorbate binding energies (46) with minimal pre-calculated reference data and low computational cost.…”
Section: The Origins Of Divergent Reactivity Between Eams and Pgmsmentioning
confidence: 99%
“… 131 133 On the other hand, a similar array of computational technologies including high-throughput and automated computational simulations and reaction modeling coupled with machine learning algorithms also start to enable the theoretical understanding and prediction of new catalysts. 134 136 The applicability of the above experimental and computational technologies in designing industrially relevant heterogeneous catalysts vary due to their greater complexity and stringent requirements. Nevertheless, these new technologies hold great potential in revolutionizing the way that industrial catalysts have been traditionally developed, and thus great progress can be expected in this research.…”
Section: Summary and Perspectivementioning
confidence: 99%
“…These optimizations ultimately result in high energy efficiencies, product selectivities, and a reduction to operational costs. [154][155][156][157][158][159][160] An additional benet of ow cell electrolyzers is the translatability of their results to modern industrial practices. Generalized ow cell setups include a gas diffusion layer (GDL) which is directly exposed to the electrolyte solution (Fig.…”
Section: Enhanced Heterogeneous Amine Molecular Catalysts Using Ow Cmentioning
confidence: 99%