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Machine Learning‑Based Detection of Graphene Defects with Atomic Precision

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Nano-Micro Letters 2020年第12期12卷 331-343页

作者： Bowen Zheng Grace x.gu Department of Mechanical Engineering University of CaliforniaBerkeleyCA 94720USA

Defects in graphene can profoundly impact its extraordinary properties,ultimately influencing the performances of graphene-based *** to detect defects with atomic resolution in graphene can be technically demanding and involve complex sample *** alternative approach is to observe the thermal vibration properties of the graphene sheet,which reflects defect information but in an implicit *** learning,an emerging data-driven approach that offers solutions to learning hidden patterns from complex data,has been extensively applied in material design and discovery *** this paper,we propose a machine learning-based approach to detect graphene defects by discovering the hidden correlation between defect locations and thermal vibration *** prediction strategies are developed:an atom-based method which constructs data by atom indices,and a domain-based method which constructs data by domain *** show that while the atom-based method is capable of detecting a single-atom vacancy,the domain-based method can detect an unknown number of multiple vacancies up to atomic *** methods can achieve approximately a 90%prediction accuracy on the reserved data for testing,indicating a promising extrapolation into unseen future graphene *** proposed strategy offers promising solutions for the non-destructive evaluation of nanomaterials and accelerates new material discoveries.

关键词： Machine learning Graphene Defects Molecular dynamics Nanomaterials

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Designing mechanically tough graphene oxide materials using deep reinforcement learning

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npj Computational Materials 2022年第1期8卷 2139-2147页

作者： Bowen Zheng Zeyu Zheng Grace x.gu Department of Mechanical Engineering University of CaliforniaBerkeleyCA94720USA Department of Industrial Engineering and Operations Research University of CaliforniaBerkeleyCA94720USA

Graphene oxide(GO)is playing an increasing role in many ***,it remains unanswered how to strategically distribute the functional groups to further enhance *** utilize deep reinforcement learning(RL)to design mechanically tough *** design task is formulated as a sequential decision process,and policy-gradient RL models are employed to maximize the toughness of *** show that our approach can stably generate functional group distributions with a toughness value over two standard deviations above the mean of random *** addition,our RL approach reaches optimized functional group distributions within only 5000 rollouts,while the simplest design task has 2×10^(11)***,we show that our approach is scalable in terms of the functional group density and the GO *** present research showcases the impact of functional group distribution on GO properties,and illustrates the effectiveness and data efficiency of the deep RL approach.

关键词： toughness tough oxide

An adaptive machine learning-based optimization method in the aerodynamic analysis of a finite wing under various cruise conditions

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Theoretical & Applied Mechanics Letters 2024年第1期14卷 27-34页

作者： Zilan Zhang Yu Ao Shaofan Li Grace x.gu Department of Mechanical Engineering University of CaliforniaBerkeley 94720USA Department of Engineering Peking UniversityBeijing 100871China Department of Civil and Environmental Engineering University of CaliforniaBerkeley 94720USA

Conventional wing aerodynamic optimization processes can be time-consuming and imprecise due to the complexity of versatile flight *** of existing literature has considered two-dimensional infinite airfoil optimization,while three-dimensional finite wing optimizations are subject to limited study because of high computational *** we create an adaptive optimization methodology built upon digitized wing shape deformation and deep learning algorithms,which enable the rapid formulation of finite wing designs for specific aerodynamic performance demands under different cruise *** methodology unfolds in three stages:radial basis function interpolated wing generation,collection of inputs from computational fluid dynamics simulations,and deep neural network that constructs the surrogate model for the optimal wing *** has been demonstrated that the proposed methodology can significantly reduce the computational cost of numerical *** also has the potential to optimize various aerial vehicles undergoing different mission environments,loading conditions,and safety requirements.

关键词： Aerodynamic optimization Computational fluid dynamics Radial basis function Finite wing Deep learning neural network

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Developments in 4D-printing:a review on current smart materials,technologies,and applications

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International Journal of Smart and Nano Materials 2019年第3期10卷 205-224页

作者： Zhizhou Zhang Kahraman G.Demir Grace x.gu Department of Mechanical Engineering University of CaliforniaBerkeleyCAUSA

Recent advances in additive manufacturing(AM),commonly known as three-dimensional(3D)-printing,have allowed researchers to create complex shapes previously impossible using traditional fabrication methods.A research branch that originated from 3D-printing called four-dimensional(4D)-printing involves printing with smart materials that can respond to external stimuli.4Dprinting permits the creation of on-demand dynamically controllable shapes by integrating the dimension of *** achievements in synthetic smart materials,novel printers,deformation mechanism,and mathematical modeling have greatly expanded the feasibility of *** this paper,progress in the 4Dprinting field is reviewed with a focus on its practical *** discuss smart materials developed using 4D-printing with explanations of their morphing ***,case studies are presented on self-constructing structures,medical devices,and soft *** conclude with challenges and future opportunities in the field of 4D-printing.

关键词： 3D-printing 4D-printing smart materials multifunctional soft robotics

Deep learning framework for material design space exploration using active transfer learning and data augmentation

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npj Computational Materials 2021年第1期7卷 1261-1267页

作者： Yongtae Kim Youngsoo Kim Charles Yang Kundo Park Grace x.gu Seunghwa Ryu Department of Mechanical Engineering&KI for the NanoCentury Korea Advanced Institute of Science and Technology(KAIST)Yuseong-guDaejeonRepublic of Korea Department of Nature-Inspired System and Application Korea Institute of Machinery and MaterialsDaejeonRepublic of Korea Department of Mechanical Engineering University of CaliforniaBerkeleyCAUSA

Neural network-based generative models have been actively investigated as an inverse design method for finding novel materials in a vast design ***,the applicability of conventional generative models is limited because they cannot access data outside the range of training *** generative models that were devised to overcome the limitation also suffer from the weak predictive power on the unseen *** this study,we propose a deep neural network-based forward design approach that enables an efficient search for superior materials far beyond the domain of the initial training *** approach compensates for the weak predictive power of neural networks on an unseen domain through gradual updates of the neural network with active transfer learning and data augmentation *** demonstrate the potential of our framework with a grid composite optimization problem that has an astronomical number of possible design *** show that our proposed framework can provide excellent designs close to the global optima,even with the addition of a very small dataset corresponding to less than 0.5%of the initial training dataset size.

关键词： neural Deep network

Design of a cationic accelerator enabling ultrafast ion diffusion kinetics in aqueous zinc-ion batteries

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Journal of Energy Chemistry 2025年第1期100卷 377-384页

作者： Yawei xiao Qianqian gu Haoyu Li Mengyao Li Yude Wang National Center for International Research on Photoelectric and Energy Materials School of Materials and Energy Yunnan University Key Laboratory of Ministry of Education for Advanced Materials in Tropical Island Resources School of Chemistry and Chemical Engineering Hainan University College of Chemical Engineering Fuzhou University Yunnan Key Laboratory of Carbon Neutrality and Green Low-carbon Technologies Yunnan University

Aqueous zinc-ion batteries are highly favored for grid-level energy storage owing to their low cost and high safety,but their practical application is limited by slow ion *** address this,a strategy has been developed to create a cation-accelerating electric field on the surface of the cathode to achieve ultrafast Zn2+diffusion *** employing electrodeposition to coat MoS2on the surface of BaV6O16·3H2O nanowires,the directional built-in electric field generated at the heterointerface acts as a cation accelerator,continuously accelerating Zn2+diffusion into the active *** optimized Zn2+diffusion coefficient in CC@BaV-6O16·3H2O@MoS2(7.5×10～8cm2s-1) surpasses that of most reported V-based ***,MoS2serving as a cathodic armor extends the cycling life of the Zn-CC@BaV6O16·3H2O@MoS2full batteries to over 10000 *** work provides valuable insights into optimizing ion diffusion kinetics for high-performance energy storage devices.

关键词： Internal electric field Cationic accelerator Ion diffusion kinetics Cathode modification strategy Aqueous zinc ion battery

Fluorinated poly(p-triphenylene isatin) anion exchange membranes based on hydrophilic hydroxyl side chain modulation for fuel cells

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Journal of Energy Chemistry 2025年第1期100卷 702-709页

作者： Yiman gu Yanchao Zhang Zhanyu Li Yijia Lei Baozeng Sun xiaoyu Yu Zhe Wang College of Chemistry and Life Science Changchun University of Technology College of Chemical Engineering Changchun University of Technology School of Materials Science and Engineering Changchun University of Technology Key Laboratory of Advanced Functional Polymer Membrane Materials of Jilin Province

The development of alkaline fuel cells is moving forward at an accelerated pace,and the application of ether-free bonded polymers to anion exchange membranes （AEMs） has been widely ***,the question of the“trade-off”between AEM ionic conductivity and dimensional stability remains *** strategy of inducing microphase separation to improve the performance of AEM has attracted much attention recently,but the design of optimal molecular structures is still being ***,this work introduced different ratios of 3-bromo-1,1,1-trifluoroacetone （x=40,50,and 60） into the main chain of poly（p-terphenylene isatin）.Because fluorinated groups have excellent hydrophobicity,hydrophilic hydroxyl-containing side chains are introduced to jointly adjust the formation of phase separation *** results show that PTI-PTF50-NOH AEM with the appropriate fluorinated group ratio has the best ionic conductivity and alkali stability under the combined effect of *** has an ionic conductivity of 133.83 mS cm-1at 80°*** addition,the OH-conductivity remains at 89%of the initial value at 80°C and 3 M KOH for 1056 h of *** cell polarization curve based on PTI-PTF50-NOH shows a power density of 734.76 mW cm-2at a current density of 1807.7 mA cm-2.

关键词： Anion exchange membranes Poly(p-terphenylene isatin) Hydrophilic-hydrophobic phase separation Fluorinated group

Boosting hydrocarbon conversion via Cu-doping induced oxygen vacancies on CeO2 in CO2 electroreduction

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Journal of Energy Chemistry 2025年第1期100卷 66-76页

作者： Lei xue Tong Shi Chenhui Han Heng Zhang Fenrong Liu Haorun Li Yan Wang xiaojun gu Shanghong Zeng Inner Mongolia Key Laboratory of Chemistry and Physics of Rare Earth Materials School of Chemistry and Chemical Engineering Inner Mongolia University State Key Laboratory of Coal Conversion Institute of Coal Chemistry Chinese Academy of Sciences Institute of Catalysis Department of Chemistry Zhejiang University National Key Laboratory of Baiyunobo Rare Earth Resource Research and Comprehensive Utilization Baotou Research Institute of Rare Earths

Conversion of CO2back to hydrocarbons is the most direct way of closing the“carbon cycle”,and its significance is further enlarged if this process is driven by renewable energies such as ***,precisely controlling the product selectivity towards hydrocarbons against the competitive hydrogen evolution remains challenging,especially for Cu-based ***,we report a novel defect engineering strategy,by which Cu-doping-induced oxygen vacancies on CeO2nanorods were effectively created,with adjustable vacancy/Cu *** resulting optimum catalyst shows up to 79% catalytic current density to hydrocarbons （excluding CO）,with 49% faradaic efficiency to *** and density functional theory unveil that the ratio between oxygen vacancy and Cu affects significantly the formation of*CHO and activation of H2O,which leads to the following deep hydrogenation to *** findings may spur new insights for designing and developing more controllable chemical process relevant to CO2utilization.

关键词： Oxygen vacancy Hydrocarbon production Reaction mechanism

Rapid and scalable preparation of highly quaternized polymer networks to achieve efficient simultaneous adsorption of iodide and pertechnetate

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Science China Chemistry 2025年第01期 337-349页

作者： Meiyun xu Song gu Fulong Chen Zhe Zhao Peng Liu Daoben Hua State Key Laboratory of Radiation Medicine and Protection School of Radiological and Interdisciplinary Sciences (RAD-X) Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions Soochow University

The efficient separation of fission products,such as TcO4-and I-,holds strategic significance for the management of radioactive wastes and environmental *** this study,we propose an ultrafast strategy for scalable preparation of a highly quaternized organic network to facilitate efficient and synchronous separation of iodide and *** network can be prepared within a few minutes at room temperature,using polyethylenimine and 1,2,4,5-tetrakis（bromomethyl）benzene as building *** chlorine replacement,the network,abundantly decorated with quaternary ammonium groups （Cl@QPN）,exhibits an ultrahigh positive charge density of 7.6 mmol/*** enables the rapid and efficient enrichment of target anions through strong electrostatic Coulomb *** a result,Cl@QPN exhibits significantly higher adsorption rate constants of 0.830 g/（mg min） for ReO4-（a nonradioactive surrogate of TcO4-） and 0.677 g/（mg min） for I-compared to other ***,it possesses high adsorption capacities,reaching 1,681 mg/g for ReO4-and 917.4 mg/g for I-.Cl@QPN also demonstrates good selectivity towards target ions and shows efficient adsorption for99TcO4-.Additionally,Cl@QPN exhibits high dynamic processing capacities,handling up to 3,100 and 7,400 kg of simulated streams per kilogram of material for I-and ReO4-,respectively.

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