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Efficient Quasiparticle Determination beyond the Diagonal Approximation via Random Compression

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arXiv 2023年

作者： Canestraight, Annabelle Lei, Xiaohe Ibrahim, Khaled Z. Vlček, Vojtěch Department of Chemical Engineering University of California Santa BarbaraCA93106-9510 United States Department of Chemistry and Biochemistry University of California Santa BarbaraCA93106-9510 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Materials University of California Santa BarbaraCA93106-9510 United States

Calculations of excited states in Green’s function formalism often invoke the diagonal approximation, in which the quasiparticle states are taken from a mean-field calculation. Here, we extend the stochastic approaches applied in the many-body perturbation theory and overcome this limitation for large systems in which we are interested in a small subset of states. We separate the problem into a core subspace, whose coupling to the remainder of the system environment is stochastically sampled. This method is exemplified on computing hole injection energies into CO2 on an extended gold surface with nearly 3000 electrons. We find that in the extended system, the size of the problem can be compressed up to 95% using stochastic sampling. This result provides a way forward for self-consistent stochastic methods and determining Dyson orbitals in large systems. © 2023, CC BY.

关键词： Perturbation techniques

Convolutional neural network-based fast seizure detection from video electroencephalograms

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Biomedical Signal Processing and Control 2023年 80卷

作者： Chou, Chi-Hsiang Shen, Tsu-Wang Tung, Hsin Hsieh, Peiyuan F. Kuo, Chih-En Chen, Ting-Mao Yang, Chao-Wei Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan The Department of Neurological Institute Taichung Veterans General Hospital Taichung Taiwan Department of Neurology National Yang Ming Chiao Tung University School of Medicine Taipei Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Master's Program in Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Applied Mathematics National Chung Hsing University Taichung Taiwan

Objective: Ictal stage detection in electroencephalography (EEG) is important for epilepsy diagnosis. However, it's a laborious and time-consuming task for neurologists, especially with lengthy EEG recordings (such as video-EEG). Here we applied the current deep learning techniques to improve ictal stage detection efficiency. Methods: We enrolled epileptic patients at video-EEG examination beds of our neurology department between Jan 2016 and Apr 2020. Four stages of EEG (interictal, preictal, ictal and postictal) were labeled and confirmed by neurologists. Labeled EEG signals were transformed into second-order Poincaré difference plots. The whole dataset was divided into ‘Training’, ‘Validation’ and ‘Testing’ datasets with the proportions of 45%, 5% and 50% respectively. We applied 4 Convolutional Neural Networks (CNNs), namely, AlexNet, GoogLeNet, ResNet50 and DenseNet201, to learn classifying. The top three CNNs were put into an ensemble model for datasets classification. Results: 30 epileptic patients were included: Male/Female 16/14, mean age 42.8 ± 15.0 (range 22 ∼ 81) years old. Total recorded ictal duration was 47,444 s. Overall accuracies for each of the 4 CNN models (AlexNet, GoogLeNet, ResNet50 and DenseNet201) were 78.0%, 78.3%, 81.7% and 80.8%. We omitted the AlexNet, the other 3 CNNs were put into the ensemble model. The best performance was under test with 1% of the Testing dataset yielded the overall accuracies of 85.9%. The ictal stage accuracy was 97.7%. Conclusion: Our method is a promising computer-aid deep learning approach to detect seizures on video EEG. Significance: For testing phase, selected proportion of testing dataset may influence the performances of deep learning models. © 2022 The Author(s)

关键词： Electroencephalography

Self-surface heating membrane distillation for sustainable production of freshwater: A state of the art overview

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Progress in Materials Science 2024年 145卷

作者： Subrahmanya, T.M. Austria, Hannah Faye M. Chen, Yi-Yun Setiawan, Owen Widakdo, Januar Kurkuri, Mahaveer D. Hung, Wei-Song Hu, Chien-Chieh Lee, Kueir-Rarn Lai, Juin-Yih Advanced Membrane Materials Research Center Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei10607 Taiwan Department of Physics Faculty of Mathematics and Natural Sciences Universitas Indonesia Depok16424 Indonesia Jain Global Campus Karnataka Bengaluru562112 India R&D Center for Membrane Technology Chung Yuan University Chung-Li 32023 Taiwan Department of Chemical Engineering and Materials Science Yuan Ze University Taoyuan32023 Taiwan

Shortage of freshwater is a global challenge related to population growth, changes in climate conditions and industrial and agricultural needs. Thus, sustainable freshwater production through desalination and wastewater treatment is essential for various human purposes. Membrane distillation (MD) is a recent thermal driven membrane based purification technology with capability to eliminate the limitations of traditional desalination technologies by a synergistic exploitation of the nexus between water and energy. Though MD is recognized as an ecofriendly technology, input heat energy utilization and its efficient management remains a challenge influencing the economic viability of the technology and hindering its realistic applications. To solve this problem, it requires an integrative approach involving materials chemistry, physical chemistry, polymer science, and materials engineering. In addition to the use of robust wetting and fouling resistant membranes, employing the newly developed self-surface heating membranes such as photothermal, joule heating and induction heating membranes have not only minimized energy requirement and fouling issues of MD technology but also enabled it to be considered as potential and economically viable approach for producing high-quality freshwater with negligible carbon footprint. Specifically, recent studies on self-surface heating membranes, utilizing nanomaterials with photothermal, conductive, and magnetic properties, have revealed new possibilities for renewable energy utilization in MD technology. Through direct irradiation or photovoltaic energy conversion, nanomaterial-integrated membranes significantly enhance MD's energy efficiency and productivity without compromising cost-effectiveness, opening avenues for sustainable desalination and water purification technologies. Here, we furnish a comprehensive state of the art overview on (1) the progress of conventional antifouling MD membranes and (2) the opportunities, challen

关键词： Desalination

Nordgren Physics-Informed Neural Networks to Variational Quantum Eigensolver: Advancing Hydraulic Fracturing Simulations in Shale Reservoirs

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Research Square

Research Square 2025年

作者： Wayo, Dennis Delali Kwesi Irawan, Sonny Noor, Mohd Zulkifli Bin Mohamad Zafar, Mudasar Juziyeva, Shynar Saporetti, Camila Martins Goliatt, Leonardo Hazlett, Randy Department of Petroleum Engineering School of Mining and Geosciences Nazarbayev University Astana010000 Kazakhstan Faculty of Chemical and Process Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Kuantan26300 Malaysia School of Mathematics Actuarial and Quantitative Studies Asia Pacific University of Technology and Innovation Kuala Lumpur0000 Malaysia Department of Computational Modeling Polytechnic Institute Rio de Janeiro State University Nova Friburgo28625-570 Brazil Department of Computational and Applied Mechanics Federal University of Juiz de Fora Juiz de Fora36036-900 Brazil

This study advances hydraulic fracturing simulations in shale reservoirs using two computational paradigms, Physics-Informed Neural Networks (PINNs) and the Variational Quantum Eigensolver (VQE). PINNs are employed to solve Nordgren’s equation, which governs fracture width evolution, by embedding physical laws into the neural network architecture. Using TensorFlow on Google Colab, the PINN training process incorporates Adam, L-BFGS, and Newton-CG optimizers, achieving an accuracy of 1.23x10−9 for fluid viscosity µ = 8.0 Pa.s, fracture height H = 2.0m, and leak-off coefficient CL = 1.0. However, this approach demands significant computational resources, with training times exceeding 1454 seconds and memory usage of 1136 MB in some cases. Conversely, the VQE framework leverages Qiskit on Qbraid to optimize the Hamiltonian representing the fracture system. With qubit-based ansatz circuits and classical optimizers (SPSA, COBYLA, and L-BFGS), VQE achieves rapid energy minimization, converging to approximately -0.583+0j in under 2 seconds with negligible memory requirements. Spatiotemporal fracture width predictions from VQE align well with expected trends but exhibit slight oscillations due to quantum noise. This comparative study highlights the trade-offs where PINNs excel in accuracy and physical adherence, while VQE offers unparalleled efficiency for rapid computations. The hybrid application of PINN precision and VQE speed provides a robust toolkit for optimizing hydrocarbon recovery. Future research will explore integrating these paradigms for scalable, high-fidelity simulations across complex geological settings. © 2025, CC BY.

关键词： Hamiltonians

Emulating Complex Synapses Using Interlinked Proton Conductors

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arXiv 2024年

作者： Zhang, Lifu Li, Ji-An Hu, Yang Jiang, Jie Lai, Rongjie Benna, Marcus K. Shi, Jian Department of Materials Science and Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Neurosciences Graduate Program University of California San Diego La Jolla CA92093 United States Department of Mathematics Purdue University West LafayetteIN47907 United States Department of Neurobiology School of Biological Sciences University of California at San Diego La Jolla CA92093 United States Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute TroyNY12180 United States

In terms of energy efficiency and computational speed, neuromorphic electronics based on non-volatile memory devices is expected to be one of most promising hardware candidates for future artificial intelligence (AI). However, catastrophic forgetting, networks rapidly overwriting previously learned weights when learning new tasks, remains as a pivotal hurdle in either digital or analog AI chips for unleashing the true power of brain-like computing. To address catastrophic forgetting in the context of online memory storage, a complex synapse model (the Benna-Fusi model) has been proposed recently[1], whose synaptic weight and internal variables evolve following a diffusion dynamics. In this work, by designing a proton transistor with a series of charge-diffusion-controlled storage components, we have experimentally realized the Benna-Fusi artificial complex synapse. The memory consolidation from coupled storage components is revealed by both numerical simulations and experimental observations. Different memory timescales for the complex synapse are engineered by the diffusion length of charge carriers, the capacity and number of coupled storage components. The advantage of the demonstrated complex synapse in both memory capacity and memory consolidation is revealed by neural network simulations of face familiarity detection. Our experimental realization of the complex synapse suggests a promising approach to enhance memory capacity and to enable continual learning. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational efficiency

One pot synthesis of UiO-66-NH2 containing curcumin and cytotoxic evaluation toward liver cancer cells

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Materials Letters 2024年 365卷

作者： Susilowati, Oni Wahyu Lestari, Witri Handayani, Murni Sukowati, Caecilia Tiribelli, Claudio Crocè, Lory Saveria Masykur, Abu Rakhman Wibowo, Fajar Dwi Ardyanto, Tonang Adi Saputra, Ozi Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Sebelas Maret Central Java Surakarta57126 Indonesia Tangerang Selatan15314 Indonesia Jakarta Pusat10340 Indonesia Liver Cancer Unit Fondazione Italiana Fegato ONLUS AREA Science Park Basovizza34049 Italy Department of Medicine Surgery and Health Sciences University of Trieste Italy Clinical Pathology Department Faculty of Medicine Universitas Sebelas Maret Surakarta57126 Indonesia Department of Chemical Engineering College of Engineering National Taiwan University Taipei10617 Taiwan Institute of Chemistry and Taiwan International Graduate Program Sustainable Chemical Science and Technology Academia Sinica Taipei115 Taiwan

Curcumin, a potent natural compound known for its health benefits, encounters challenges such as low bioavailability and poor solubility. To overcome these obstacles, incorporating curcumin into metal–organic frameworks (MOFs) has emerged as a promising approach. This study explores the in-situ modification of UiO-66-NH2 (UiO stands for Universitetet i Oslo) with curcumin, aiming to assess its release and delivery potential. chemical, structural, morphological, and textural analyses confirmed the successful synthesis of the materials. Nitrogen sorption isotherm, field-emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM) revealed that curcumin addition led to decrease in surface area (from 829.6 m2/g to 679.92 m2/g) and porosity, as well as alterations in particle size (from 0.162 to 0.068 μm) and shape from octahedral to spherical. The release behavior of curcumin exhibited pH-dependent profiles, with cumulative releases (∼20 % at pH 4.8, and 30.56 % at pH 7.4) fitting Kosmeyer-Peppas model. Biological activity assays indicated non-toxicity, suggesting potential for curcumin delivery. © 2024

关键词： Bioactivity

Unravelling the Mechanics of Knitted Fabrics Through Hierarchical Geometric Representation

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arXiv 2023年

作者： Ding, Xiaoxiao Sanchez, Vanessa Bertoldi, Katia Rycroft, Chris H. Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Mathematics University of Wisconsin–Madison MadisonWI53706 United States Department of Chemical Engineering Stanford University 443 Via Ortega StanfordCA94305 United States Computational Research Division Lawrence Berkeley Laboratory 1 Cyclotron Road BerkeleyCA94720 United States

Knitting interloops one-dimensional yarns into three-dimensional fabrics that exhibit behaviour beyond their constitutive materials. How extensibility and anisotropy emerge from the hierarchical organisation of yarns into knitted fabrics has long been unresolved. We seek to unravel the mechanical roles of tensile mechanics, assembly and dynamics arising from the yarn level on fabric nonlinearity by developing a yarn-based dynamical model. This physically validated model captures the mechanical response of knitted fabrics, analogous to flexible metamaterials and biological fiber networks due to geometric nonlinearity within such hierarchical systems. Fabric anisotropy originates from observed yarn–yarn rearrangements during alignment dynamics and is topology-dependent. This yarn-based model also provides a design space of knitted fabrics to embed functionalities by varying geometric configuration and material property in instructed procedures compatible to machine manufacturing. Our hierarchical approach to build up a knitted fabrics computationally modernizes an ancient craft and represents a first step towards mechanical programmability of knitted fabrics in wide engineering applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Yarn

clotFoam: An Open-Source Framework to Simulate Blood Clot Formation Under Arterial Flow

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arXiv 2023年

作者： Montgomery, David Municchi, Federico Leiderman, Karin Department of Applied Mathematics and Statistics Colorado School of Mines 1500 Illinois St GoldenCO80401 United States Department of Mechanical Engineering Colorado School of Mines 1500 Illinois St GoldenCO80401 United States Department of Mathematics University of North Carolina at Chapel Hill 216 Lenoir Dr Chapel HillNC27599 United States Computational Medicine Program University of North Carolina at Chapel Hill 216 Lenoir Dr Chapel HillNC27599 United States

Blood clotting involves the coupled processes of platelet aggregation and coagulation. Simulating clotting under flow in complex geometries is challenging due to multiple temporal and spatial scales and high computational cost. clotFoam is an open-source software developed in OpenFOAM that employs a continuum model of platelet advection, diffusion, and aggregation in a dynamic fluid environment and a simplified coagulation model with proteins that advect, diffuse, and react within the fluid and with wall-bound species through reactive boundary conditions. Our framework provides the foundation on which one can build more complex models and perform reliable simulations in almost any computational domain. © 2023, CC BY.

关键词： Coagulation

Interfacial Layer Materials with a Truxene Core for Dopant-Free NiOx-Based Inverted Perovskite Solar Cells

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Small 2024年第33期20卷 e2310939页

作者： Ramanujam, Rajarathinam Hsu, Hsiang-Lin Shi, Zhong-En Lung, Chien-Yu Lee, Chin-Han Wubie, Gebremariam Zebene Chen, Chih-Ping Sun, Shih-Sheng Institute of Chemistry Academia Sinica Nankang Taipei11529 Taiwan Taiwan International Graduate Program Sustainable Chemical Science and Technology Academia Sinica Nankang Taipei11529 Taiwan Department of Applied Chemistry National Yang Ming Chiao Tung University Hsinchu30050 Taiwan Department of Materials Engineering Ming Chi University of Technology 84 Gunjuan Road Taishan New Taipei City24301 Taiwan College of Engineering and Center for Sustainability and Energy Technologies Chang Gung University Taoyuan33302 Taiwan

Nickel oxide (NiOx) is commonly used as a holetransporting material (HTM) in p-i-n perovskite solar cells. However, the weak chemical interaction between the NiOx and CH3NH3PbI3 (MAPbI3) interface results in poor crystallinity, ineffective hole extraction, and enhanced carrier recombination, which are the leading causes for the limited stability and power conversion efficiency (PCE). Herein, two HTMs, TRUX-D1 (N2,N7,N12-tris(9,9-dimethyl-9H-fluoren-2-yl)-5,5,10,10,15,15-hexaheptyl-N2,N7,N12-tris(4-methoxyphenyl)-10,15-dihydro-5H-diindeno[1,2-a:1′,2′-c]fluorene-2,7,12-triamine) and TRUX-D2 (5,5,10,10,15,15-hexaheptyl-N2,N7,N12-tris(4-methoxyphenyl)-N2,N7,N12-tris(10-methyl-10H-phenothiazin-3-yl)-10,15-dihydro-5H-diindeno[1,2-a:1′,2′-c]fluorene-2,7,12-triamine), are designed with a rigid planar C3 symmetry truxene core integrated with electron-donating amino groups at peripheral positions. The TRUX-D molecules are employed as effective interfacial layer (IFL) materials between the NiOx and MAPbI3 interface. The incorporation of truxene-based IFLs improves the quality of perovskite crystallinity, minimizes nonradiative recombination, and accelerates charge extraction which has been confirmed by various characterization techniques. As a result, the TRUX-D1 exhibits a maximum PCE of up to 20.8% with an impressive long-term stability. The unencapsulated device retains 98% of their initial performance following 210 days of aging in a glove box and 75.5% for the device after 80 days under ambient air condition with humidity over 40% at 25 °C. © 2024 Wiley-VCH GmbH.

关键词： Passivation