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Amorphous Catalysis: Machine Learning Driven High-Throughput Screening of Superior Active Site for Hydrogen Evolution Reaction. This article is cited by Department of Physics, Technical University of Denmark, 2800 Kongens Lyngby, Denmark, Department of Chemical Engineering, SUNCAT Center for Surface Science and Catalysis, Stanford University, 443 Via Ortega, Stanford, California 94035, United States, SUNCAT Center for Surface Science and Catalysis, SLAC National Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, United States, Scaling Relations in Homogeneous Catalysis: Analyzing the Buchwald–Hartwig Amination Reaction. http://pubs.acs.org/page/copyright/permissions.html, https://doi.org/10.1021/acs.chemrev.0c00078, https://doi.org/10.1021/acs.jpclett.0c01991, https://doi.org/10.1021/acs.chemrev.0c00004, https://doi.org/10.1021/acs.jpclett.0c00214, https://doi.org/10.1021/acs.jpclett.9b01428, https://doi.org/10.1038/s41467-020-14969-8, https://doi.org/10.1038/s41524-020-00371-x, https://doi.org/10.1038/s41467-020-19267-x, https://doi.org/10.1038/s41578-020-00255-y, https://doi.org/10.1016/j.chempr.2020.09.001, https://doi.org/10.1016/j.mlwa.2020.100010, https://doi.org/10.1007/s11244-020-01380-2, https://doi.org/10.1016/j.cplett.2020.137772, https://doi.org/10.1007/s11244-020-01283-2, https://doi.org/10.1016/j.commatsci.2020.109690, https://doi.org/10.1080/01614940.2020.1770402, https://doi.org/10.1016/j.commatsci.2019.109474, https://doi.org/10.1016/j.matt.2019.09.011, https://doi.org/10.1038/s41467-019-12709-1, https://doi.org/10.1038/s41929-019-0298-3. • Computational approach illustrated by NH 3 synthesis and CO hydrogenation reactions. Stephen R. Xie, Parker Kotlarz, Richard G. Hennig, Juan C. Nino. Machine learning prediction of monatomic adsorption energies with non-first-principles calculated quantities. Apart from comparing different properties or different systems, we are now also searching for correlations between the same systems and properties but measured using different methods. Electronic Supporting Information files are available without a subscription to ACS Web Editions. Using statistical learning to predict interactions between single metal atoms and modified MgO(100) supports. Chun-Yen Liu, Shijia Zhang, Daniel Martinez, Meng Li, Thomas P. Senftle. from the ACS website, either in whole or in part, in either machine-readable form or any other form Such relationships are ultimately derived from bond order conservation principles that were first introduced several decades ago. Scaling Relations for Adsorption Energies on Doped Molybdenum Phosphide Surfaces. Temperature-Scanning Reaction Protocol Offers Insights into Activation Parameters in the Buchwald–Hartwig Pd-Catalyzed Amination of Aryl Halides. Through the growing power of computational surface science and catalysis, these concepts and their … Rohit Batra, Le Song, Rampi Ramprasad. Computational catalyst screening is limited primarily by the efficiency with which accurate predictions can be made. • Bond strength in Sabatier principle is given quantitatively from scaling relations. These metrics are regularly updated to reflect usage leading up to the last few days. Computational screening for new and improved catalyst materials relies on accurate and low-cost predictions of key parameters such as adsorption energies. Jacques A. Esterhuizen, Bryan R. Goldsmith, Suljo Linic. Find more information about Crossref citation counts. Progress in Accurate Chemical Kinetic Modeling, Simulations, and Parameter Estimation for Heterogeneous Catalysis. • Implications of quantitative metal catalysis theory for the search of new catalysts. Performance of Metal-Catalyzed Hydrodebromination of Dibromomethane Analyzed by Descriptors Derived from Statistical Learning. Scaling relationships are theoretical constructs that relate the binding energies of a wide variety of catalytic intermediates across a range of catalyst surfaces. Chaofang Deng, Yang Su, Fuhua Li, Weifeng Shen, Zhongfang Chen, Qing Tang. Metal catalysis theory from scaling relations, activity maps, and d-band model. Understanding activity origin for the oxygen reduction reaction on bi-atom catalysts by DFT studies and machine-learning. Catalytic activation of a non-noble intermetallic surface through nanostructuration under hydrogenation conditions revealed by atomistic thermodynamics. 7 publications. Design of an Accurate Machine Learning Algorithm to Predict the Binding Energies of Several Adsorbates on Multiple Sites of Metal Surfaces. ... generally assume the task of highly efficient catalysis and are more active than the single ones for catalyzing complex reactions [21], [22]. Enhancing both selectivity and activity of CO 2 conversion by breaking scaling relations with bimetallic active sites anchored in covalent organic frameworks. CheKiPEUQ Intro 1: Bayesian Parameter Estimation Considering Uncertainty or Error from both Experiments and Theory**. This article is cited by http://pubs.acs.org/page/copyright/permissions.html. From a single density functional theory calculation of these properties, we predict adsorption energies at all potential surface sites, and thereby also the most stable geometry. Takashi Toyao, Zen Maeno, Satoru Takakusagi, Takashi Kamachi, Ichigaku Takigawa.

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