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Challenges for and Pathways toward Li-MetalBased All-Solid-State Batteries

ACS ENERGY LETTERS 2021年第4期6卷 1399-1404页

作者： Albertus, Paul Anandan, Venkataramani Ban, Chunmei Balsara, Nitash Belharouak, Ilias Buettner-Garrett, Josh Chen, Zonghai Daniel, Claus Doeff, Marca Dudney, Nancy J. Dunn, Bruce Harris, Stephen J. Herle, Subramanya Herbert, Eric Kalnaus, Sergiy Libera, Joesph A. Lu, Dongping Martin, Steve McCloskey, Bryan D. McDowell, Matthew T. Meng, Y. Shirley Nanda, Jagjit Sakamoto, Jeff Self, Ethan C. Tepavcevic, Sanja Wachsman, Eric Wang, Chunsheng Westover, Andrew S. Xiao, Jie Yersak, Thomas Department of Chemical and Biomolecular Engineering University of Maryland College Park Maryland 20742 United States Ford Motor Company Dearborn Michigan 48121 United States Mechanical Engineering Department University of Colorado Boulder Colorado 80309 United States Department of Chemical and Biomolecular Engineering and Lawrence Berkeley National Laboratory Materials Sciences Division University of California Berkeley California 94720 United States Energy and Transportation Science Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 United States Solid Power Inc. Louisville Colorado 80027 United States Chemical Sciences and Engineering Division Argonne National Laboratory 9700 South Cass Avenue Argonne Illinois 60439 United States Lawrence Berkeley National Laboratory Energy Storage and Distributed Resources Division University of California Berkeley California 94720 United States Department of Materials Science and Engineering University of California Los Angeles California 90095 United States Applied Materials Inc. 3225 Oakmead Village Drive B12_2F6 Santa Clara California 95054 United States Department of Materials Science and Engineering Michigan Technological University Houghton Michigan 49931 United States Energy and Environmental Directorate Pacific Northwest National Laboratory Richland Washington 99352 United States Department of Materials Science & Engineering Iowa State University of Science & Technology Ames Iowa 50011 United States Woodruff School of Mechanical Engineering and School of Materials Science and Engineering Georgia Institute of Technology Atlanta Georgia 30332 United States Department of NanoEngineering and Materials Science and Engineering Program University of California San Diego La Jolla California 92093 United States Department of Materials Science and Engineering and Department of Mechanical Engineering University of Michigan Ann Arbor Michigan 48109 United States Chemical and Materials Systems Laborat

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A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data

arXiv

引用

arXiv 2021年

作者： Lu, Lu Meng, Xuhui Cai, Shengze Mao, Zhiping Goswami, Somdatta Zhang, Zhongqiang Karniadakis, George Em Department of Chemical and Biomolecular Engineering University of Pennsylvania Division of Applied Mathematics Brown University School of Mathematical Sciences Xiamen University Department of Mathematical Sciences Worcester Polytechnic Institute School of Engineering Brown University

Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for realistic diverse applications as well as for system identification in science and engineering. Herein, we investigate the performance of two neural operators, which have shown promising results so far, and we develop new practical extensions that will make them more accurate and robust and importantly more suitable for industrial-complexity applications. The first neural operator, DeepONet, was published in 2019 [1], and its original architecture was based on the universal approximation theorem of Chen & Chen [2]. The second one, named Fourier Neural Operator or FNO, was published in 2020 [3], and it is based on parameterizing the integral kernel in the Fourier space. DeepONet is represented by a summation of products of neural networks (NNs), corresponding to the branch NN for the input function and the trunk NN for the output function;both NNs are general architectures, e.g., the branch NN can be replaced with a CNN or a ResNet. According to [4], FNO in its continuous form can be viewed as a DeepONet with a specific architecture of the branch NN and a trunk NN represented by a trigonometric basis. In order to compare FNO with DeepONet for realistic setups, we develop several extensions of FNO that can deal with complex geometric domains as well as mappings where the input and output function spaces are of different dimensions. We also endow DeepONet with special features that provide inductive bias and accelerate training, and we present a faster implementation of DeepONet with cost comparable to the computational cost of FNO, which is based on the Fast Fourier Transform. We consider 16 different benchmarks to demonstrate the relative performance of the two neural operators, including instability wave analysis in hypersonic boundary layers, prediction of the vorticity field of a flapping airfoil, porous media s

关键词： Porous materials

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PyEquIon: A python package for automatic speciation calculations of aqueous electrolyte solutions

arXiv

引用

arXiv 2021年

作者： Marcellos, Caio Felippe Curitiba da Silva Junior, Gerson Francisco do Amaral Soares, Elvis Ramos, Fabio Barreto, Amaro G. Department of Chemical Engineering Center of Technology Federal University of Rio de Janeiro RJ Rio de Janeiro21941-909 Brazil Department of Applied Mathematics Institute of Mathematics Federal University of Rio de Janeiro Brazil

In several industrial applications, such as crystallization, pollution control, and flow assurance, an accurate understanding of the aqueous electrolyte solutions is crucial. Electrolyte equilibrium calculation contributes with the design and optimization of processes by providing important information, such as species concentration, solution pH and potential for solid formation. In this work, a pure Python library distributed under BSD-3 license was developed for the calculation of aqueous electrolyte equilibrium. The package takes as inputs the feed components of a given solution, and it automatically identifies its composing ions and the chemical reactions involved to calculate equilibrium conditions. Moreover, there is no established electrolyte activity coefficient model for a broad range of operational conditions. Hence, in this package, built-in activity coefficient models are structured in a modular approach, so that the non-ideality calculation can be performed by a user provided function, which allows further research in the topic. The package can be used by researchers to readily identify the equilibrium reactions and possible solid phases in a user friendly language. © 2021, CC BY-NC-ND.

关键词： Python

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Autonomous Electron Tomography Reconstruction with Machine Learning

arXiv

引用

arXiv 2023年

作者： Millsaps, William Schwartz, Jonathan Di, Zichao Wendy Jiang, Yi Hovden, Robert Department of Nuclear Engineering & Radiological Sciences University of Michigan Ann ArborMI United States Department of Materials Science and Engineering University of Michigan Ann ArborMI United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Advanced Photon Source Facility Argonne National Laboratory LemontIL United States Applied Physics Program University of Michigan Ann ArborMI United States

This manuscript is published in Microscopy and Microanalysis (DOI: 10.1093/micmic/ozad083). Modern electron tomography has progressed to higher resolution at lower doses by leveraging compressed sensing methods that minimize total variation (TV). However, these sparsity-emphasized reconstruction algorithms introduce tunable parameters that greatly influence the reconstruction quality. Here, Pareto front analysis shows that high-quality tomograms are reproducibly achieved when TV minimization is heavily weighted. However, in excess, compressed sensing tomography creates overly smoothed 3D reconstructions. Adding momentum to the gradient descent during reconstruction reduces the risk of over-smoothing and better ensures that compressed sensing is well behaved. For simulated data, the tedious process of tomography parameter selection is efficiently solved using Bayesian optimization with Gaussian processes. In combination, Bayesian optimization with momentum-based compressed sensing greatly reduces the required compute time-an 80% reduction was observed for the 3D reconstruction of SrTiO3 nanocubes. Automated parameter selection is necessary for large scale tomographic simulations that enable the 3D characterization of a wider range of inorganic and biological materials. © 2023, CC BY.

关键词： Compressed sensing

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Ab initio quantum many-body description of superconducting trends in the cuprates

arXiv

引用

arXiv 2023年

作者： Cui, Zhi-Hao Yang, Junjie Tölle, Johannes Ye, Hong-Zhou Yuan, Shunyue Zhai, Huanchen Park, Gunhee Kim, Raehyun Zhang, Xing Lin, Lin Berkelbach, Timothy C. Chan, Garnet Kin-Lic Division of Chemistry and Chemical Engineering California Institute of Technology PasadenaCA91125 United States Department of Chemistry Columbia University New YorkNY10027 United States Division of Engineering and Applied Science California Institute of Technology PasadenaCA91125 United States Department of Mathematics University of California BerkeleyCA94720 United States Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

Using a systematic ab initio quantum many-body approach that goes beyond low-energy models, we directly compute the superconducting pairing order and estimate the pairing gap of several doped cuprate materials and structures within a purely electronic picture. We find that we can correctly capture two well-known trends: the pressure effect, where the pairing order and gap increase with intra-layer pressure, and the layer effect, where the pairing order and gap vary with the number of copper-oxygen layers. From these calculations, we observe that the strength of superexchange and the covalency at optimal doping are the best descriptors for these trends. Our microscopic analysis further identifies that strong short-range spin fluctuations and multi-orbital charge fluctuations drive the development of the pairing order. Our work illustrates the possibility of a material-specific ab initio understanding of unconventional high-temperature superconducting materials. Copyright © 2023, The Authors. All rights reserved.

关键词： High temperature superconductors

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COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

引用

Computers, Materials & Continua 2021年第7期68卷 391-407页

作者： Aqib Ali Wali Khan Mashwani Samreen Naeem Muhammad Irfan Uddin Wiyada Kumam Poom Kumam Hussam Alrabaiah Farrukh Jamal Christophe Chesneau Department of Computer Science Concordia College BahawalpurBahawalpur63100Pakistan Department of Computer Science&IT Glim Institute of Modern StudiesBahawalpur63100Pakistan Institute of Numerical Sciences Kohat University of Science&TechnologyKohat26000Pakistan Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Program in Applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiThanyaburi12110Thailand Departments of Mathematics Faculty of ScienceCenter of Excellence in Theoretical and Computational Science(TaCS-CoE)&KMUTT Fixed Point Research LaboratoryRoom SCL 802 Fixed Point LaboratoryScience Laboratory BuildingKing Mongkut’s University of Technology Thonburi(KMUTT)Bangkok10140Thailand Department of Medical Research China Medical University HospitalTaichung40402Taiwan College of Engineering Al Ain UniversityAl Ain64141United Arab Emirates Department of Mathematics Tafila Technical UniversityTafila66110Jordan Department of Statistics The Islamia University of BahawalpurBahawalpur63100Pakistan 11Department of MathematicsUniversitéde CaenLMNOCaen14032France

The purpose of this research is the segmentation of lungs computed tomography(CT)scan for the diagnosis of COVID-19 by using machine learning *** dataset contains data from patients who are prone to the *** contains three types of lungs CT images(Normal,Pneumonia,and COVID-19)collected from two different sources;the first one is the Radiology department of Nishtar Hospital Multan and Civil Hospital Bahawalpur,Pakistan,and the second one is a publicly free available medical imaging database known as *** the preprocessing,a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest(ROIs)and acquire 52 hybrid statistical features for each ***,12 optimized statistical features are selected via the chi-square feature reduction *** the classification,five machine learning classifiers named as deep learning J4,multilayer perceptron,support vector machine,random forest,and naive Bayes are deployed to optimize the hybrid statistical features *** is observed that the deep learning J4 has promising results(sensitivity and specificity:0.987;accuracy:98.67%)among all the deployed *** a complementary study,a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.

关键词： COVID-19 machine learning fuzzy c-mean deep learning J4

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An Immersed Interface Method for Incompressible Flows and Geometries with Sharp Features

arXiv

引用

arXiv 2024年

作者： Facci, Michael J. Kolahdouz, Ebrahim M. Griffith, Boyce E. Department of Mathematics University of North Carolina Chapel HillNC United States Flatiron Institute Simons Foundation New YorkNY United States Department of Biomedical Engineering University of North Carolina Chapel HillNC United States Carolina Center for Interdisciplinary Applied Mathematics University of North Carolina Chapel HillNC United States Computational Medicine Program University of North Carolina School of Medicine Chapel HillNC United States McAllister Heart Institute University of North Carolina School of Medicine Chapel HillNC United States

The immersed interface method (IIM) for models of fluid flow and fluid-structure interaction imposes jump conditions that capture stress discontinuities generated by forces that are concentrated along immersed boundaries. Most prior work using the IIM for fluid dynamic applications has focused on smooth interfaces, but boundaries with sharp features such as corners and edges can appear in practical analyses, particularly on engineered structures. The present study builds on our work to integrate finite element-type representations of interface geometries with the IIM. Initial realizations of this approach used a continuous Galerkin (CG) finite element discretization for the boundary, but as we show herein, these approaches generate large errors near sharp geometrical features. To overcome this difficulty, this study introduces an IIM approach using discontinuous Galerkin (DG) representation of the jump conditions. Numerical examples explore the impacts of different interface representations on accuracy for both smooth and sharp boundaries, particularly flows interacting with fixed interface configurations. We demonstrate that using a DG approach provides accuracy that is comparable to the CG method for smooth cases. Further, we identify a time step size restriction for the CG representation that is directly related to the sharpness of the geometry. In contrast, time step size restrictions imposed by DG representations are demonstrated to be insensitive to the presence of sharp *** Codes 76-10 (Primary) 76D05 (Secondary) © 2024, CC BY-NC-ND.

关键词： Incompressible flow

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Biomass waste as an alternative source of carbon and silicon-based absorbents for CO2 capturing application

引用

Chemosphere 2023年 343卷

作者： Suresh, R. Gnanasekaran, Lalitha Rajendran, Saravanan Jalil, A.A. Soto-Moscoso, Matias Khoo, Kuan Shiong Ma, Zengling Halimatul Munawaroh, Heli Siti Show, Pau Loke Departamento de Ingeniería Mecánica Facultad de Ingeniería Universidad de Tarapacá Avda. General Velásquez 1775 Arica Chile University Centre for Research & Development Department of Mechanical Engineering Chandigarh University Punjab Mohali140413 India Department of Chemical Engineering Lebanese American University Byblos Lebanon Centre of Hydrogen Energy Institute of Future Energy Johor UTM Johor Bahru81310 Malaysia Faculty of Chemical and Energy Engineering Universiti Teknologi Malaysia Johor UTM Johor Bahru81310 Malaysia Universidad Autónoma de Chile Chile Department of Chemical Engineering and Materials Science Yuan Ze University Taoyuan Taiwan Centre for Herbal Pharmacology and Environmental Sustainability Chettinad Hospital and Research Institute Chettinad Academy of Research and Education Tamil Nadu Kelambakkam603103 India Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection Wenzhou University Wenzhou325035 China Study Program of Chemistry Faculty of Mathematics and Science Education Universitas Pendidikan Indonesia Jalan Dr. Setiabudhi 229 Bandung40154 Indonesia Department of Chemical Engineering Khalifa University P.O. Box 127788 Abu Dhabi United Arab Emirates Department of Chemical and Environmental Engineering Faculty of Science and Engineering University of Nottingham Malaysia Jalan Broga Selangor Darul Ehsan Semenyih Malaysia

The production of low-cost solid adsorbents for carbon dioxide (CO2) capture has gained massive consideration. Biomass wastes are preferred as precursors for synthesis of CO2 solid adsorbents, due to their high CO2 adsorption efficiency, and ease of scalable low-cost production. This review particularly focuses on waste biomass-derived adsorbents with their CO2 adsorption performances. Specifically, studies related to carbon (biochar and activated carbon) and silicon (silicates and geopolymers)-based adsorbents were summarized. The impact of experimental parameters including nature of biomass, synthesis route, carbonization temperature and type of activation methods on the CO2 adsorption capacities of biomass-derived pure carbon and silicon-based adsorbents were evaluated. The development of various enhancement strategies on biomass-derived adsorbents for CO2 capture and their responsible factors that impact adsorbent's CO2 capture proficiency were also reviewed. The possible CO2 adsorption mechanisms on the adsorbent's surface were highlighted. The challenges and research gaps identified in this research area have also been emphasized, which will help as further research prospects. © 2023 Elsevier Ltd

关键词： Carbon dioxide

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Interacting models for twisted bilayer graphene: a quantum chemistry approach

arXiv

引用

arXiv 2022年

作者： Faulstich, Fabian M. Stubbs, Kevin D. Zhu, Qinyi Soejima, Tomohiro Dilip, Rohit Zhai, Huanchen Kim, Raehyun Zaletel, Michael P. Chan, Garnet Kin-Lic Lin, Lin Department of Mathematics University of California BerkeleyCA94720 United States Department of Physics University of California BerkeleyCA94720 United States Division of Chemistry and Chemical Engineering California Institute of Technology PasadenaCA91125 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

The nature of correlated states in twisted bilayer graphene (TBG) at the magic angle has received intense attention in recent years. We present a numerical study of an interacting Bistritzer-MacDonald (IBM) model of TBG using a suite of methods in quantum chemistry, including Hartree-Fock, coupled cluster singles, doubles (CCSD), and perturbative triples (CCSD(T)), as well as a quantum chemistry formulation of the density matrix renormalization group method (DMRG). Our treatment of TBG is agnostic to gauge choices, and hence we present a new gauge-invariant formulation to detect the spontaneous symmetry breaking in interacting models. To benchmark our approach, we focus on a simplified spinless, valleyless IBM model. At integer filling (ν = 0), all numerical methods agree in terms of energy and C2zT symmetry breaking. Additionally, as part of our benchmarking, we explore the impact of different schemes for removing "double-counting" in the IBM model. Our results at integer filling suggest that cross-validation of different IBM models may be needed for future studies of the TBG system. After benchmarking our approach at integer filling, we perform the first systematic study of the IBM model near integer filling (for |ν| Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum chemistry

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Fuzzy sets complement-based Gated Recurrent Unit 13

Fuzzy sets complement-based Gated Recurrent Unit

引用

13th International Workshop on Fuzzy Logic and Applications, WILF 2021

作者： Ferrero-Jaurrieta, Mikel Dimuro, Graçaliz Pereira Takáč, Zdenko Santiago, Regivan H.N. Fernández, Javier Bustince, Humberto Department of Statistics Computer Science and Mathematics Public University of Navarre Campus Arrosadía s/n Pamplona31006 Spain Centro de Ciências Computacionais Universidade Federal do Rio Grande Rio Grande 96044540 Brazil Institute of Information Engineering Automation and Mathematics Faculty of Chemical and Food Technology Slovak University of Technology in Bratislava Radlinského 9 Bratislava812 37 Slovakia Department of Computer Science and Applied Mathematics Universidade Federal do Rio Grande do Norte Natal1524 Brazil

Gated Recurrent Units (GRU) are neural network gated architectures that simplify other ones (such as, LSTM) by joining gates mainly. For this, instead of using two gates, if x is the first gate, standard operation 1 − x is used to generate the second one, optimizing the number of parameters. In this work, we interpret this information as a fuzzy set, and we generalize the standard operation using fuzzy negations, and improving the accuracy obtained with the standard one. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Fuzzy sets

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