DSpace Community:https://hdl.handle.net/2440/1158792024-03-28T13:33:24Z2024-03-28T13:33:24ZDiscovery of Graphene Growth Alloy Catalysts Using High-Throughput Machine LearningLi, X.Shi, J.Q.Page, A.J.https://hdl.handle.net/2440/1404272024-02-22T07:02:33Z2023-01-01T00:00:00ZTitle: Discovery of Graphene Growth Alloy Catalysts Using High-Throughput Machine Learning
Author: Li, X.; Shi, J.Q.; Page, A.J.
Abstract: Despite today’s commercial-scale graphene production using chemical vapor deposition (CVD), the growth of high-quality single-layer graphene with controlled morphology and crystallinity remains challenging. Considerable effort is still spent on designing improved CVD catalysts for producing high-quality graphene. Conventionally, however, catalyst design has been pursued using empirical intuition or trial-and-error approaches. Here, we combine high-throughput density functional theory and machine learning to identify new prospective transition metal alloy catalysts that exhibit performance comparable to that of established graphene catalysts, such as Ni(111) and Cu(111). The alloys identified through this process generally consist of combinations of early- and late-transition metals, and a majority are alloys of Ni or Cu. Nevertheless, in many cases, these conventional catalyst metals are combined with unconventional partners, such as Zr, Hf, and Nb. The approach presented here therefore highlights an important new approach for identifying novel catalyst materials for the CVD growth of low-dimensional nanomaterials.
Description: Published: October 27, 20232023-01-01T00:00:00ZPredicting progression of Parkinson’s disease motor outcomes using a multimodal combination of baseline clinical measures, neuroimaging and biofluid markersMcNamara, A.Ellul, B.Baetu, I.-I.Lau, S.Jenkinson, M.Collins-Praino, L.https://hdl.handle.net/2440/1402652023-12-21T02:25:10Z2023-01-01T00:00:00ZTitle: Predicting progression of Parkinson’s disease motor outcomes using a multimodal combination of baseline clinical measures, neuroimaging and biofluid markers
Author: McNamara, A.; Ellul, B.; Baetu, I.-I.; Lau, S.; Jenkinson, M.; Collins-Praino, L.
Abstract: Abstract not available
Description: Abstract #P34.112023-01-01T00:00:00ZActive-learning accelerated computational screening of A₂B@NG catalysts for CO₂ electrochemical reductionLi, X.Li, H.Zhang, Z.Shi, J.Q.Jiao, Y.Qiao, S.-Z.https://hdl.handle.net/2440/1399732023-11-28T00:28:28Z2023-01-01T00:00:00ZTitle: Active-learning accelerated computational screening of A₂B@NG catalysts for CO₂ electrochemical reduction
Author: Li, X.; Li, H.; Zhang, Z.; Shi, J.Q.; Jiao, Y.; Qiao, S.-Z.
Abstract: Few-atom catalysts, due to the unique coordination structure compared to metal particles and single-atom catalysts, have the potential to be applied for efficient electrochemical CO2 reduction (CRR). In this study, we designed a class of triple-atom A2B catalysts, with two A metal atoms and one B metal atom either horizontally or vertically embedded in the nitrogen-doped graphene plane. Metals A and B were selected from 17 elements across 3d to 5d transition metals. The structural stability and CRR activity of the 257 constructed A2B catalysts were evaluated. The active-learning approach was applied to predict the adsorption site of key reaction intermediate *CO, which only used 40% computing resources in comparison to “brute force” calculation and greatly accelerated the large amount of computation brought by the large number of A2B catalysts. Our results reveal that these triple atom catalysts can selectively produce more valuable hydrocarbon products while preserving high reactivity. Additionally, six triple-atom catalysts were proposed as potential CRR catalysts. These findings provide a theoretical understanding of the experimentally synthesized Fe3 and Ru3-N4 catalysts and lay a foundation for future discovery of few-atom catalysts and carbon materials in other applications. A new machine learning method, masked energy model, was also proposed which outperforms existing methods by approximately 5% when predicting low-coverage adsorption sites.
Description: Available online 12 July 20232023-01-01T00:00:00ZVisual Place Recognition: A TutorialSchubert, S.Neubert, P.Garg, S.Milford, M.Fischer, T.https://hdl.handle.net/2440/1399652023-12-01T04:00:35Z2023-01-01T00:00:00ZTitle: Visual Place Recognition: A Tutorial
Author: Schubert, S.; Neubert, P.; Garg, S.; Milford, M.; Fischer, T.
Abstract: Localization is an essential capability for mobile robots, enabling them to build a comprehensive representation of their environment and interact with the environment effectively toward a goal. A rapidly growing field of research in this area is visual place recognition (VPR), which is the ability to recognize previously seen places in the world based solely on images.
Description: OnlinePubl2023-01-01T00:00:00Z