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您的检索式：作者名="Edvin Fako"
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| 1 | Single-atom heterogeneous catalysts based on distinct carbon nitride scaffolds显示文摘Carbon nitrides integrating macroheterocycles offer unique potential as hosts for stabilizing metal atoms due to their rich electronic structure. To date, only graphitic heptazine-based polymers have been studied.Here, we demonstrate that palladium atoms can be effectively isolated on other carbon nitride scaffolds including linear melem oligomers and poly(triazine/heptazine imides). Increased metal uptake was linked to the larger cavity size and the presence of chloride ions in the polyimide structures. Changing the host structure leads to significant variation in the average oxidation state of the metal, which can be tuned by exchange of the ionic species as evidenced by X-ray photoelectron spectroscopy and supported by density functional theory. Evaluation in the semi-hydrogenation of 2-methyl-3-butyn-2-ol reveals an inverse correlation between the activity and the degree of oxidation of palladium, with oligomers exhibiting the highest activity. These findings provide new mechanistic insights into the influence of the carbon nitride structure on metal stabilization. | Zupeng Chen Evgeniya Vorobyeva Sharon Mitchell Edvin Fako Núria López Sean M.Collins Rowan K.Leary Paul A.Midgley Roland Hauert Javier Pérez-Ramírez | 2018 | National Science Review2018，5，5： | 4 |
| 2 | Accurate energy barriers for catalytic reaction pathways: an automatic training protocol for machine learning force fields显示文摘We introduce a training protocol for developing machine learning force fields(MLFFs),capable of accurately determining energy barriers in catalytic reaction pathways.The protocol is validated on the extensively explored hydrogenation of carbon dioxide to methanol over indium oxide.With the help of active learning,the final force field obtains energy barriers within 0.05 eV of Density Functional Theory.Thanks to the computational speedup,not only do we reduce the cost of routine in-silico catalytic tasks,but also find an alternative path for the previously established rate-limiting step,with a 40%reduction in activation energy.Furthermore,we illustrate the importance of finite temperature effects and compute free energy barriers.The transferability of the protocol is demonstrated on the experimentally relevant,yet unexplored,top-layer reduced indium oxide surface.The ability of MLFFs to enhance our understanding of extensively studied catalysts underscores the need for fast and accurate alternatives to direct ab-initio simulations. | Lars L.Schaaf Edvin Fako Sandip De Ansgar Schäfer Gábor Csányi | 2023 | npj Computational Materials2023，，1： | 0 |
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