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Front Cover: Transparent Conductive Encapsulants for Photoelectrochemical Applications (ChemElectroChem 19/2023)

ChemElectroChem 2023年第19期10卷

作者： Grace A. Rome Fry Intia Talysa R. Klein Dr. Zebulon G. Schichtl Dr. Adele C. Tamboli Dr. Emily L. Warren Dr. Ann L. Greenaway Materials Chemistry and Computational Science Directorate National Renewable Energy Laboratory Golden CO 80401 USA Department of Physics Materials Science Program Colorado School of Mines Golden CO 80401 USA

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Generation and structural characterization of Debye random media

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Physical Review E 2020年第4期102卷 043310-043310页

作者： Zheng Ma Salvatore Torquato Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials and Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

In their seminal paper on scattering by an inhomogeneous solid, Debye and coworkers proposed a simple exponentially decaying function for the two-point correlation function of an idealized class of two-phase random media. Such Debye random media, which have been shown to be realizable, are singularly distinct from all other models of two-phase media in that they are entirely defined by their one- and two-point correlation functions. To our knowledge, there has been no determination of other microstructural descriptors of Debye random media. In this paper, we generate Debye random media in two dimensions using an accelerated Yeong-Torquato construction algorithm. We then ascertain microstructural descriptors of the constructed media, including their surface correlation functions, pore-size distributions, lineal-path function, and chord-length probability density function. Accurate semianalytic and empirical formulas for these descriptors are devised. We compare our results for Debye random media to those of other popular models (overlapping disks and equilibrium hard disks) and find that the former model possesses a wider spectrum of hole sizes, including a substantial fraction of large holes. Our algorithm can be applied to generate other models defined by their two-point correlation functions, and their other microstructural descriptors can be determined and analyzed by the procedures laid out here.

关键词： Patterns in complex systems

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Deep potentials for materials science

材料展望（英文）

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材料展望（英文） 2022年第2期 89-115页

作者： Tongqi Wen Linfeng Zhang Han Wang Weinan E David J Srolovitz Department of Mechanical Engineering The University of Hong KongHong KongHong Kong Special Administrative Region of China DP Technology BeijingPeople's Republic of China AI for Science Institute BeijingPeople's Republic of China Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijingPeople's Republic of China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingPeople's Republic of China AI for Science Institute BeijingPeople's Republic of China School of Mathematical Sciences Peking UniversityBeijingPeople's Republic of China Department of Mathematics and Program in Applied and Computational Mathematics Princeton UniversityPrincetonNJUnited States of America Department of Mechanical Engineering The University of Hong KongHong KongHong Kong Special Administrative Region of China International Digital Economy Academy(IDEA) ShenzhenPeople's Republic of China

To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and been widely applied;*** learning potentials(MLPs).One recently developed type of MLP is the deep potential(DP)*** this review,we provide an introduction to DP methods in computational materials *** theory underlying the DP method is presented along with a step-by-step introduction to their development and *** also review materials applications of DPs in a wide range of materials *** DP Library provides a platform for the development of DPs and a database of extant *** discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.

关键词： deep potential atomistic simulation machine learning potential neural network

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Metapopulation models imply non-Poissonian statistics of interevent times

arXiv

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arXiv 2021年

作者： dos Reis, Elohim Fonseca Masuda, Naoki Department of Mathematics State University of New York at Buffalo BuffaloNY United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY United States

Interevent times in temporal contact data from humans and animals typically obey heavy-tailed distributions, and this property impacts contagion and other dynamical processes on networks. We theoretically show that distributions of interevent times heavier-tailed than exponential distributions are a consequence of the most basic metapopulation model used in epidemiology and ecology, in which individuals move from a patch to another according to the simple random walk. Our results hold true irrespectively of the network structure and also for more realistic mobility rules such as high-order random walks and the recurrent mobility patterns used for modeling human dynamics. Copyright © 2021, The Authors. All rights reserved.

关键词： Random processes

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Algal biomass based bio-refineries: Concurrent pre-treatment strategies and perspectives for sustainable feedstock

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Biomass and Bioenergy 2024年 187卷

作者： Shashikumar, Uday Ranjan, Kumar Rakesh Sharma, Ankit Naina Subramanian, Balaji Tsai, Pei-Chien Lin, Yuan-Chung Lay, Chyi-How Wang, Chin-Tsan Biswas, Shelly Ponnusamy, Vinoth Kumar Department of Medicinal and Applied Chemistry Kaohsiung Medical University Kaohsiung City 807 Taiwan Department of Chemistry Amity Institute of Applied Sciences Amity University Uttar Pradesh Noida201303 India Department of Mechanical Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Institute of Environmental Engineering National Sun Yat-Sen University Taiwan Master Program of Green Energy Science and Technology Feng Chia University Taiwan Department of Mechanical and Electro-Mechanical Engineering National I-Lan University I Lan Taiwan Department of Space Engineering and Rocketry Birla Institute of Technology Mesra Jharkhand Ranchi835215 India 807 Kaohsiung City Taiwan 807 Kaohsiung City Taiwan Department of Computational Biology Institute of Bioinformatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai602105 India Institute of Aquatic Science and Technology National Kaohsiung University of Science and Technology Kaohsiung City81157 Taiwan Department of Food Science National Pingtung University of Science and Technology Pingtung91201 Taiwan

The quest for viable and scalable biofuel sources has been at the forefront of scientific innovation for the past three decades. Due to its rich chemical constituents, microalgal biomass has emerged as a pivotal sustainable and scalable feedstock for biorefineries. This comprehensive review critically analyzes the different types of microalgae feedstock, concurrent extraction technologies, bio-pre-treatment procedures, and the key chemical and physical parameters influencing lipid formation and algal biofuel production. We propose a novel approach of photo-initiated culturing of algal biomass using photobioreactors (PBRs) to address the limitations of concurrent space and time-related constraints. The innovative photo bio-refinery strategy presented herein aims to enhance sustainability factors while minimizing emissions, catering to the needs of futuristic non-electric vehicles. A comparative quality analysis of microalgae-derived biofuel against conventional fossil fuels and other biofuels is conducted, considering chemical, environmental, economic, and social perspectives. Furthermore, we elucidate the efficacy of bio-pre-treatment strategies such as dehydration, hydrothermal liquefaction, pyrolysis, and gasification in optimizing biofuel production. The proposed photo biorefineries exhibit the potential to yield a diverse range of value-added products, including biodiesel, biogases, bio-fertilizers, bio-pesticides, bio-alcohols, dyes, proteins, carotenoids, and drug vitals. This review provides a comprehensive framework for the development of sustainable and efficient microalgae-based biorefineries, paving the way for a greener and more economically viable future in the biofuel industry. © 2024 Elsevier Ltd

关键词： Feedstocks

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Modeling texts with networks: comparing five approaches to sentence representation

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The European Physical Journal B 2024年第6期97卷 1-12页

作者： Oliveira, Davi Alves Pereira, Hernane Borges de Barros Department of Human Sciences Campus IV University of Bahia State (UNEB) Jacobina Brazil Programa de Pós-Graduação em Difusão do Conhecimento (PPGDC) Federal University of Bahia (UFBA) University of Bahia State (UNEB) Bahia Federal Institute of Education Science and Technology (IFBA) State University of Feira de Santana (UEFS) National Scientific Computing Laboratory (LNCC) SENAI CIMATEC University Center Salvador Brazil Department of Education Campus I University of Bahia State (UNEB) Salvador Brazil Computational Modeling Program SENAI CIMATEC University Center Salvador Brazil

Complex networks offer a powerful framework for modeling linguistic phenomena. This study compares five distinct methods for representing sentences as networks, each with unique edge definitions: (1) a lines approach, where edges represent token (e.g., word) adjacency; (2) a close-range co-occurrence approach, where edges are based on the probability of tokens co-occurring at distance one or two; (3) a cliques approach, where edges connect tokens co-occurring within the same sentence; (4) a dependency-based approach, where edges are defined by syntactic dependencies extracted by a parser; (5) an IF-trimmed-subgraphs approach, where edges are determined by the Incidence-Fidelity (IF) Index. While the first four approaches are well established in the literature, the last one is a novel proposal. We also examined the effects of limiting the vertices to lemmas (i.e., words with inflections removed) and to lexical lemmas (i.e., nouns, adjectives, verbs, and adverbs) as opposed to the unaltered words. Our results reveal that these approaches yield networks with varying average minimal path lengths and degrees, influencing the interpretation of results. While small-world behavior remains consistent across networks, scale-free behavior analysis is affected. Notably, excluding functional words significantly alters degree distributions. We suggest, in order of relevance and according to the resources available, the dependency-based, the close-range co-occurrence, and the lines approaches for cases in which syntactic relations are central, and the IF-trimmed-subgraphs and the cliques approaches for cases in which semantic relations are central. Representation of the sentence “we calculated two sets of adjusted values as follows” using five approaches - (1) the lines approach, (2) the close-range cooccurrence approach, (3) the cliques approach, (4) the dependency-based approach, and (5) the IF-trimmed-subgraphs approach - and three vertex definitions - (1) vertices representing

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Randomizing hypergraphs preserving degree correlation and local clustering

arXiv

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arXiv 2021年

作者： Nakajima, Kazuki Shudo, Kazuyuki Masuda, Naoki Department of Mathematical and Computing Science Tokyo Institute of Technology Japan Department of Mathematics State University of New York Buffalo United States Computational and Data-Enabled Science and Engineering Program State University of New York Buffalo United States Faculty of Science and Engineering Waseda University Japan

Many complex systems involve direct interactions among more than two entities and can be represented by hypergraphs, in which hyperedges encode higher-order interactions among an arbitrary number of nodes. To analyze structures and dynamics of given hypergraphs, a solid practice is to compare them with those for randomized hypergraphs that preserve some specific properties of the original hypergraphs. In the present study, we propose a family of such reference models for hypergraphs, called the hyper dk-series, by extending the so-called dk-series for dyadic networks to the case of hypergraphs. The hyper dk-series preserves up to the individual node’s degree, node’s degree correlation, node’s redundancy coefficient, and/or the hyperedge’s size depending on the parameter values. Furthermore, we numerically find that higher-order hyper dk-series more accurately preserves the shortest path length and degree distribution of the one-mode projection of the original hypergraph, which the method does not intend to preserve. We also apply the hyper dk-series to numerical simulations of epidemic spreading and evolutionary game dynamics on empirical social hypergraphs. We find that the hyperedge’s size affects these dynamics more than any of the node’s properties and that the node’s degree correlation and redundancy in the empirical hypergraphs promote cooperation. Copyright © 2021, The Authors. All rights reserved.

关键词： Redundancy

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On a Dynamic Variant of the Iteratively Regularized Gauss-Newton Method with Sequential Data

arXiv

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arXiv 2022年

作者： Chada, Neil K. Iglesias, Marco A. Lu, Shuai Werner, Frank Applied Mathematics and Computational Science Program King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia School of Mathematical Sciences University of Nottingham NottinghamNG72RD United Kingdom School of Mathematical Sciences Fudan University Shanghai200433 China Institut für Mathematik University of Wuerzburg Emil–Fischer–Str. 30 Würzburg97074 Germany

For numerous parameter and state estimation problems, assimilating new data as they become available can help produce accurate and fast inference of unknown quantities. While most existing algorithms for solving those kind of ill-posed inverse problems can only be used with a single instance of the observed data, in this work we propose a new framework that enables existing algorithms to invert multiple instances of data in a sequential fashion. Specifically we will work with the well-known iteratively regularized Gauss–Newton method (IRGNM), a variational methodology for solving nonlinear inverse problems. We develop a theory of convergence analysis for a proposed dynamic IRGNM algorithm in the presence of Gaussian white noise. We combine this algorithm with the classical IRGNM to deliver a practical (hybrid) algorithm that can invert data sequentially while producing fast estimates. Our work includes the proof of well-definedness of the proposed iterative scheme, as well as various error bounds that rely on standard assumptions for nonlinear inverse problems. We use several numerical experiments to verify our theoretical findings, and to highlight the benefits of incorporating sequential data. The context of the numerical experiments comprises various parameter identification problems including a Darcy flow PDE, and that of electrical impedance tomography. © 2022, CC BY-NC-SA.

关键词： Inverse problems

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Theoretical insights into the chemical bonding, electronic structure, and spectroscopic properties of the lanarkite Pb2SO5 structure

arXiv

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arXiv 2023年

作者： Fabris, Guilherme S.L. Ferrer, Mateus M. Sambrano, Julio R. Almeida, Cláudio R.R. Paskocimas, Carlos A. La Porta, Felipe A. Graduate Program in Materials Science and Engineering Technological Development Center Universidade Federal de Pelotas RS Pelotas96010-610 Brazil Modeling and Molecular Simulation Group São Paulo State University Bauru São Paulo17033-360 Brazil Department of Materials Engineering Federal University of Rio Grande do Norte RN Natal59078-970 Brazil Postgraduate Program in Science and Engineering of Materials Federal University of Rio Grande do Norte RN Natal59078-970 Brazil Federal University of Technology - Paraná Nanotechnology and Computational Chemistry Laboratory Avenida dos Pioneiros 3131 CEP PR Londrina86036-370 Brazil State University of Londrina Post-Graduation Program in Chemistry Rodovia Celso Garcia Cid 445 km 380 CEP PR Londrina86057-970 Brazil

Lanarkite is a mineral formed by a combination of lead, sulphur, and oxygen atoms arranged in the general chemical formula Pb2SO5 (PSO) that crystallises with monoclinic symmetry (belonging to the C2/m space group, No. 12). This mineral was first discovered in Lanarkshire, Scotland and was named after its location. PSO has a unique structure comprising alternating penta-coordinated lead [PbO5] and tetra-coordinated sulphur [SO4] clusters. This lanarkite-type structure has recently attracted significant scientific interest and has been the focus of the superconducting material research community. However, its chemistry needs to be explored further. This article presents a comprehensive investigation on the chemical bonding, electronic structure, and spectroscopic properties of the lanarkite-type PSO structure from a computational perspective. Thus, different functionals in the DFT (e.g., PBE, PBE0, PBESOL, PBESOL0, BLYP, WC1LYP37, and B3LYP) were assessed to accurately predict their fundamental properties. All the DFT calculations were performed using a triple-zeta valence plus polarisation basis set. Among all the DFT functionals tested in this study, PBE showed the best agreement with the experimental data available in the literature. Our results also reveal that the [PbO5] clusters are formed with three Pb–O bond lengths, with values of about 2.32, 2.59, and 2.84 Å, respectively, while the [SO4] clusters have the same S–O bond length of 1.57 Å. We performed a complete topological analysis of this system to comprehend these structural differences. Additionally, the PSO structure has an indirect band gap energy of 2.9 eV and an effective mass ratio (mℎ∗ /me∗) of about 0.415 (using PBE calculations) which may, in principle, indicate a low recombination of electron-hole pairs in the lanarkite structure. Therefore, we believe that a detailed understanding of their electronic structures, spectroscopic properties as well as their chemical bonding is critically important

关键词： Lead compounds

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Reconstructing Suspended Sediment Concentrations in the Mekong River Basin Via Semi-Supervised-Based Deep Neural Networks

SSRN

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SSRN 2024年

作者： Nguyen, Thi-Thu-Ha Vu, Duc Quang Doan, Ngoc Phu Thi Khanh Chi, Huynh Li, Peixin Van Binh, Doan An, Yimeng Tuan Dung, Pham Hoang, Tuan A. Thai Son, Mai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City70000 Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City70000 Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat Town820000 Viet Nam

The Mekong River Basin (MRB) is one of the highest biodiversity areas in the world and is the vital source of livelihoods for more than 60 millions of people over six countries in Southeast Asia. However, it is suffering from a severe sediment starvation problem caused by climate change and human activities, particularly dam constructions. Understanding long-term sediment changes thus plays a crucial role for management and contingency plans. Current sediment concentration data in the MRB are extremely sporadic, making it a non-trivial task to analyze them. This paper focuses on the sediment reconstruction to provide long-term reliable suspended sediment concentration (SSC) data using Machine Learning (ML) perspectives. In particular, a novel semi-supervised ML model, which can effectively deal with data sparsity, is introduced to predict/reconstruct missing sediment data. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the performances of the proposed method compared to other state-of-the-art supervised techniques and traditional simulation methods commonly used in previous sediment forecasting/reconstruction studies. To the best of our knowledge, this approach is the first semi-supervised method to reconstruct sediment data in the field. Though this study focuses on the MRB, sediment sparsity is a very common problem in other river systems worldwide. Thus, this method can be widely applied for them for more effective sediment reconstruction/prediction in general. © 2024, The Authors. All rights reserved.

关键词： Biodiversity

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