Thermodynamics and Catalytic Activity of Ruthenium Oxides Grown on Ruthenium Metal from a Machine Learning Atomic Simulation

2021 ◽  
Author(s):  
Ze-Yi Zhu ◽  
Ye-Fei Li ◽  
Cheng Shang ◽  
Zhi-Pan Liu
Keyword(s):  
Machine Learning ◽  
Catalytic Activity ◽  
Atomic Simulation ◽  
Ruthenium Oxides

Comparison of different machine learning models for the prediction of forces in copper and silicon dioxide

2018 ◽  
Vol 20 (47) ◽  
pp. 30006-30020 ◽  
Author(s):  
Wenwen Li ◽  
Yasunobu Ando
Keyword(s):  
Machine Learning ◽  
Silicon Dioxide ◽  
Force Field ◽  
Computational Cost ◽  
High Accuracy ◽  
Simulation Approach ◽  
Learning Models ◽  
Atomic Simulation ◽  
Low Computational Cost ◽  
Machine Learning Models

Recently, the machine learning (ML) force field has emerged as a powerful atomic simulation approach because of its high accuracy and low computational cost.

A machine learning framework for the analysis and prediction of catalytic activity from experimental data

2020 ◽  
Vol 263 ◽  
pp. 118257 ◽  
Author(s):  
Alexander Smith ◽  
Andrea Keane ◽  
James A. Dumesic ◽  
George W. Huber ◽  
Victor M. Zavala
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Catalytic Activity ◽  
Learning Framework

scholarly journals Scanning electrochemical microscopy screening of CO2 electroreduction activities and product selectivities of catalyst arrays

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Francis D. Mayer ◽  
Pooya Hosseini-Benhangi ◽  
Carlos M. Sánchez-Sánchez ◽  
Edouard Asselin ◽  
Előd L. Gyenge
Keyword(s):  
Machine Learning ◽  
Cyclic Voltammetry ◽  
Catalytic Activity ◽  
Scanning Electrochemical Microscopy ◽  
Spatially Resolved ◽  
Co2 Electroreduction ◽  
Further Development ◽  
Activity Information ◽  
Electrochemical Microscopy

Abstract The electroreduction of CO2 is one of the most investigated reactions and involves testing a large number and variety of catalysts. The majority of experimental electrocatalysis studies use conventional one-sample-at-a-time methods without providing spatially resolved catalytic activity information. Herein, we present the application of scanning electrochemical microscopy (SECM) for simultaneous screening of different catalysts forming an array. We demonstrate the potential of this method for electrocatalytic assessment of an array consisting of three Sn/SnOx catalysts for CO2 reduction to formate (CO2RF). Simultaneous SECM scans with fast scan (1 V s−1) cyclic voltammetry detection of products (HCOO−, CO and H2) at the Pt ultramicroelectrode tip were performed. We were able to consistently distinguish the electrocatalytic activities of the three compositionally and morphologically different Sn/SnOx catalysts. Further development of this technique for larger catalyst arrays and matrices coupled with machine learning based algorithms could greatly accelerate the CO2 electroreduction catalyst discovery.

scholarly journals Polymer Magnetically Separable Catalyst for Supercritical Deoxygenation of Fatty Acids

2019 ◽  
Vol 5 (11) ◽  
pp. 10-17
Author(s):  
A. Stepacheva ◽  
A. Semenova ◽  
N. Yablokova ◽  
E. Kupriyanova ◽  
D. Rud
Keyword(s):  
Fatty Acids ◽  
Catalytic Activity ◽  
High Specific Surface Area ◽  
Hypercrosslinked Polystyrene ◽  
Biodiesel Fuel ◽  
Polymeric Support ◽  
Ruthenium Oxides ◽  
Magnetically Separable Catalyst ◽  
Catalytically Active ◽  
Magnetically Separable

In this paper, the possibility of using a magnetically separated ruthenium-containing catalyst based on a polymer matrix of hypercrosslinked polystyrene in the supercritical deoxygenation of stearic acid to produce a second-generation biodiesel fuel is studied. The catalyst was synthesized by a successive deposition of iron and ruthenium oxides to the polymeric support. The resulting catalytically active Ru-Fe3O4-HPS system is characterized by high specific surface area (364 m2/g) and magnetization (4.5 emu/g). This catalyst allows obtaining a high (up to 86%) yield of hydrocarbons C17+ and exhibits high activity in the process of deoxygenation in supercritical n-hexane. It was found that the selected catalytic system retains its catalytic activity for at least 10 consecutive cycles.

A machine learning scheme for the catalytic activity of alloys with intrinsic descriptors

2020 ◽  
Vol 8 (34) ◽  
pp. 17507-17515
Author(s):  
Ze Yang ◽  
Wang Gao ◽  
Qing Jiang
Keyword(s):  
Machine Learning ◽  
Catalytic Activity ◽  
Phase Space ◽  
Universal Design ◽  
Rapid Screening ◽  
Machine Learning Method ◽  
Learning Method ◽  
Intrinsic Properties ◽  
Design Scheme ◽  
Large Phase

We develop a universal design scheme based on the machine learning method and the intrinsic properties of substrates and adsorbates, allowing accurate prediction and rapid screening through a large phase space of alloys and multiple adsorbates.

scholarly journals hcp → ω phase transition mechanisms in shocked zirconium: A machine learning based atomic simulation study

2019 ◽  
Vol 162 ◽  
pp. 126-135 ◽  
Author(s):  
Hongxiang Zong ◽  
Yufei Luo ◽  
Xiangdong Ding ◽  
Turab Lookman ◽  
Graeme J. Ackland
Keyword(s):  
Machine Learning ◽  
Phase Transition ◽  
Simulation Study ◽  
Atomic Simulation ◽  
Ω Phase
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