Browsing by Author "Huang, Yang"

Now showing 1 - 5 of 5
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    Bayesian-optimization-assisted discovery of stereoselective aluminum complexes for ring-opening polymerization of racemic lactide
    Wang, Xiaoqian; Huang, Yang; Xie, Xiaoyu; Liu, Yan; Huo, Ziyu; Lin, Maverick; Xin, Hongliang; Tong, Rong (Nature Research, 2023-06-20)
    Stereoselective ring-opening polymerization catalysts are used to produce degradable stereoregular poly(lactic acids) with thermal and mechanical properties that are superior to those of atactic polymers. However, the process of discovering highly stereoselective catalysts is still largely empirical.We aim to develop an integrated computational and experimental framework for efficient, predictive catalyst selection and optimization. As a proof of principle, we have developed a Bayesian optimization workflow on a subset of literature results for stereoselective lactide ring-opening polymerization, and using the algorithm, we identify multiple new Al complexes that catalyze either isoselective or heteroselective polymerization. In addition, feature attribution analysis uncovers mechanistically meaningful ligand descriptors, such as percent buried volume (%Vbur) and the highest occupied molecular orbital energy (Eₕₒₘₒ), that can access quantitative and predictivemodels for catalyst development.
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    Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights
    Gao, Qiang; Pillai, Hemanth Somarajan; Huang, Yang; Liu, Shikai; Mu, Qingmin; Han, Xue; Yan, Zihao; Zhou, Hua; He, Qian; Xin, Hongliang; Zhu, Huiyuan (Nature Portfolio, 2022-04-29)
    Machine learning is a powerful tool for screening electrocatalytic materials. Here, the authors reported a seamless integration of machine-learned physical insights with the controlled synthesis of structurally ordered intermetallic nanocrystals and well-defined catalytic sites for efficient nitrate reduction to ammonia. The electrochemical nitrate reduction reaction (NO3RR) to ammonia is an essential step toward restoring the globally disrupted nitrogen cycle. In search of highly efficient electrocatalysts, tailoring catalytic sites with ligand and strain effects in random alloys is a common approach but remains limited due to the ubiquitous energy-scaling relations. With interpretable machine learning, we unravel a mechanism of breaking adsorption-energy scaling relations through the site-specific Pauli repulsion interactions of the metal d-states with adsorbate frontier orbitals. The non-scaling behavior can be realized on (100)-type sites of ordered B2 intermetallics, in which the orbital overlap between the hollow *N and subsurface metal atoms is significant while the bridge-bidentate *NO3 is not directly affected. Among those intermetallics predicted, we synthesize monodisperse ordered B2 CuPd nanocubes that demonstrate high performance for NO3RR to ammonia with a Faradaic efficiency of 92.5% at -0.5 V-RHE and a yield rate of 6.25 mol h(-1) g(-1) at -0.6 V-RHE. This study provides machine-learned design rules besides the d-band center metrics, paving the path toward data-driven discovery of catalytic materials beyond linear scaling limitations.
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    Computational and Data-Driven Design of Perturbed Metal Sites for Catalytic Transformations
    Huang, Yang (Virginia Tech, 2024-05-23)
    We integrate theoretical, computational and data-driven approaches for the sake of understanding, design and discovery of metal based catalysts. Firstly, we develop theoretical frameworks for predicting electronic descriptors of transition and noble metal alloys, including a physics model of d-band center, and a tight-binding theory of d-band moments to systematically elucidate the distinct electronic structures of novel catalysts. Within this framework, the hybridization of semi-empirical theories with graph neural network and attribution analysis enables accurate prediction equipped with mechanistic insights. In addition, novel physics effect controlling surface reactivity beyond conventional understanding is uncovered. Secondly, we develop a computational and data-driven framework to model high entropy alloy (HEA) catalysis, incorporating thermodynamic descriptor-based phase stability evaluation, surface segregation modeling by deep learning potential-driven molecular simulation and activity prediction through machine learning-embedded electrokinetic model. With this framework, we successfully elucidate the experimentally observed improved activity of PtPdCuNiCo HEA in oxygen reduction reaction. Thirdly, a Bayesian optimization framework is employed to optimize racemic lactide polymerization by searching for stereoselective aluminum (Al) -complex catalysts. We identified multiple new Al-complex molecules that catalyzed either isoselective or heteroselective polymerization. In addition, feature attribution analysis uncovered mechanistically meaningful ligand descriptors that can access quantitative and predictive models for catalyst development.
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    Examining the Microclimate Pattern and Related Spatial Perception of the Urban Stormwater Management Landscape: The Case of Rain Gardens
    Ge, Mengting; Huang, Yang; Zhu, Yifanzi; Kim, Mintai; Cui, Xiaolei (MDPI, 2023-07-12)
    This study examines the microclimate pattern and related spatial perception of urban green stormwater infrastructure (GSI) and the stormwater management landscape, using rain gardens as a case study. It investigates the relationship between different rain garden design factors, such as scale, depth, and planting design, and their effects on microclimate patterns and human spatial perception. Taking an area in Blacksburg, Virginia, as the study site, twelve rain garden design scenarios are generated by combining different design factors. The potential air temperature, relative humidity, and wind speed/direction are analyzed through computational simulation. Additionally, feelings of comfort, the visual beauty of the landscape, and the overall favorite are used as an evaluation index to investigate people’s perception of various rain garden design options. The study found that a multilayer and complex planting design can add more areas with moderate temperature and higher humidity. It also significantly improves people’s subjective perception of a rain garden. Furthermore, a larger scale rain garden can make people feel more comfortable and improve the visual beauty of the landscape, highlighting the importance of designing larger and recreational bioretention cells in GSI systems. Regarding depth, a relatively flatter rain garden with a complex planting design can bring stronger air flow and achieve better visual comfort and visual beauty. Overall, by examining the microclimate pattern and related perception of rain gardens, this study provides insight into better rain garden design strategies for the urban stormwater management landscape. It explores the potential of rain garden design in urban GSI and responds to climate change.
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    How Virtual Reality Renderings Impact Scale and Distance Perception Compared to Traditional Representation
    Ge, Mengting; Huang, Yang; Kim, Mintai (Wichmann Verlag, 2023-05)
    Spatial scale and distance are essential attributes of physical space in landscape design. Individuals’ perceptions of spatial scale and distance reflect how well they understand a space, and decides how they design the space. This research studies how scale and distance perception in landscape design projects using Virtual Reality (VR) renderings can differ from traditional design representations. This study examines perception of space using three design representation methods: VR simple 3D model, VR realistic rendered model, and traditional representation with the illustrative plan. Fifty-four individuals with design education and practice experience participate in this research. Participants were divided into 3 groups, and every group used one design representation method to estimate the spatial scale of selected space and distance to selected objects. Participants’ perceptions are investigated through survey and statistically analysed. This research enriches VR-related studies from the perspective of spatial perception and awareness. It inspires diverse possibilities of future design representation in the design industry and education.