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"Unlocking Electrocatalytic Dynamics with Anti-Mxene Borides Monolayers for Nitrate Reduction"

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SSRN

SSRN 2024年

作者： Ho, Thi H. Nguyen, Quynh Anh T. Le, Bich-Tram Truong Kim, Seong-Gon Bui, Viet Q. Laboratory for Computational Physics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam Advanced Institute of Science and Technology The University of Danang 41 Le Duan Danang Viet Nam Department of Science and International Cooperation The University of Danang Danang551000 Viet Nam Department of Physics & Astronomy Center for Computational Sciences Mississippi State University MS39762 United States

In this comprehensive study, we explore the electrocatalytic behavior of novel two-dimensional (2D) monolayers composed of anti-MXene borides (TMB) towards the nitrate reduction reaction (NO3RR) using density functional theory (DFT). The analysis reveals that these TMB monolayers emerge as promising candidates for electrochemical NO3RR, characterized by their exceptional stability, advantageous selectivity, and effective activation properties. Notably, our results highlight the preferential progression of the NO3-to-N2 pathway over the NO3-to-NH3 pathway in these TMB systems, presenting a significant insight into the mechanism of nitrate reduction. Among the monolayers, MnB distinguishes itself with a limiting potential of 0.79 eV, underscoring its substantial potential for application in the electroreduction of nitrate. Additionally, this study sheds light on the broader applicability of these innovative two-dimensional anti-MXene TMB monolayers, suggesting their versatility as electrocatalysts in a variety of potential applications, thereby opening new avenues for future research and development in electrocatalysis. © 2024, The Authors. All rights reserved.

关键词： Electrocatalysis

Curating corpora with classifiers: A case study of clean energy sentiment online

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arXiv 2023年

作者： Arnold, Michael V. Dodds, Peter Sheridan Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States Department of Mathematics & Statistics University of Vermont BurlingtonVT United States

Well curated, large-scale corpora of social media posts containing broad public opinion offer an alternative data source to complement traditional surveys. While surveys are effective at collecting representative samples and are capable of achieving high accuracy, they can be both expensive to run and lag public opinion by days or weeks. Both of these drawbacks could be overcome with a real-time, high volume data stream and fast analysis pipeline. A central challenge in orchestrating such a data pipeline is devising an effective method for rapidly selecting the best corpus of relevant documents for analysis. Querying with keywords alone often includes irrelevant documents that are not easily disambiguated with bag-of-words natural language processing methods. Here, we explore methods of corpus curation to filter irrelevant tweets using pre-trained transformer-based models, fine-tuned for our binary classification task on hand-labeled tweets. We are able to achieve F1 scores of up to 0.95. The low cost and high performance of fine-tuning such a model suggests that our approach could be of broad benefit as a pre-processing step for social media datasets with uncertain corpus boundaries. © 2023, CC BY.

关键词： Tweets

Unusual Nonlinear Optical Responses in Layered Ferroelectric Niobium Oxide Dihalides: Origin and Manipulation

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arXiv 2023年

作者： Ye, Liangting Zhou, Wenju Huang, Dajian Jiang, Xiao Jia, Donghan Guo, Qiangbing Jiang, Dequan Wang, Yonggang Wu, Xiaoqiang Li, Yang Gou, Huiyang Huang, Bing Beijing Computational Science Research Center Beijing100193 China Center for High Pressure Science and Technology Advanced Research Beijing100193 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore School of Mechanical Engineering Chengdu University Chengdu610106 China Department of Physics Beijing Normal University Beijing100875 China

Realization of large and highly tunable second-order nonlinear optical (NLO) responses, e.g., second-harmonic generation (SHG) and bulk photovoltaic effect (BPVE), is critical for developing modern optical and optoelectronic devices. Very recently, the two-dimensional van der Waals ferroelectric NbOX2 (X = Cl, Br or I) are discovered to exhibit unusually large and anisotropic SHG. However, the physical origin and possible tunability of NLO responses in NbOX2 remain to be unclear. In this article, we reveal that the large SHG in NbOCl2 is dominated by the synergy between large transition dipole moment and band-nesting-induced large intensity of electron-hole pairs. Remarkably, the NbOCl2 can exhibit dramatically different strain-dependent BPVE under different polarized light, originating from the interesting light-polarization-dependent orbital transition. Importantly, we successfully achieve a reversible ferroelectric-to-antiferroelectric phase transition via controlling ambient temperature or external pressure, accompanied by the greatly tunable NLO responses. Furthermore, we discover that the evolutions of SHG and BPVE in NbOX2 with variable X obey different rules. Our study provides a deep understanding on the novel NLO physics in NbOX2 and establishes great external-field tunability for device applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Photovoltaic effects

DPA-2:a large atomic model as a multitask learner

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npj computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

Clinically Interpretable Machine Learning Models for Early Prediction of Mortality in Older Patients with Multiple Organ Dysfunction Syndrome: An International Multicenter Retrospective Study

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Journals of Gerontology - Series A Biological sciences and Medical sciences 2023年第4期78卷 718-726页

作者： Liu, Xiaoli DuMontier, Clark Hu, Pan Liu, Chao Yeung, Wesley Mao, Zhi Ho, Vanda Thoral, Patrick J. Kuo, Po-Chih Hu, Jie Li, Deyu Cao, Desen Mark, Roger G. Zhou, FeiHu Zhang, Zhengbo Celi, Leo Anthony Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China Laboratory for Computational Physiology Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA United States Center for Artificial Intelligence in Medicine General Hospital of PLA Beijing China New England Geriatric Research Education and Clinical Center VA Boston Healthcare System Boston MA United States Division of Aging Brigham and Women's Hospital Boston MA United States Department of Anesthesiology 920 Hospital of Joint Logistic Support Force of Chinese PLA Kunming Yunnan China Department of Critical Care Medicine First Medical Center General Hospital of PLA Beijing China Department of Medicine National University Hospital Singapore Division of Geriatric Medicine Department of Medicine National University Hospital Singapore Department of Intensive Care Medicine Amsterdam Netherlands Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Biomedical Engineering General Hospital of PLA Beijing China Elderly Center General Hospital of PLA Beijing China Department of Medicine Beth Israel Deaconess Medical Center Boston MA United States Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA United States

BACKGROUND: Multiple organ dysfunction syndrome (MODS) is associated with a high risk of mortality among older patients. Current severity scores are limited in their ability to assist clinicians with triage and management decisions. We aim to develop mortality prediction models for older patients with MODS admitted to the ICU. methods: The study analyzed older patients from 197 hospitals in the United States and 1 hospital in the Netherlands. The cohort was divided into the young-old (65-80 years) and old-old (≥80 years), which were separately used to develop and evaluate models including internal, external, and temporal validation. Demographic characteristics, comorbidities, vital signs, laboratory measurements, and treatments were used as predictors. We used the XGBoost algorithm to train models, and the SHapley Additive exPlanations (SHAP) method to interpret predictions. RESULTS: Thirty-four thousand four hundred and ninety-seven young-old (11.3% mortality) and 21 330 old-old (15.7% mortality) patients were analyzed. Discrimination AUROC of internal validation models in 9 046 U.S. patients was as follows: 0.87 and 0.82, respectively;discrimination of external validation models in 1 905 EUR patients was as follows: 0.86 and 0.85, respectively;and discrimination of temporal validation models in 8 690 U.S. patients: 0.85 and 0.78, respectively. These models outperformed standard clinical scores like Sequential Organ Failure Assessment and Acute Physiology Score III. The Glasgow Coma Scale, Charlson Comorbidity Index, and Code Status emerged as top predictors of mortality. CONCLUSIONS: Our models integrate data spanning physiologic and geriatric-relevant variables that outperform existing scores used in older adults with MODS, which represents a proof of concept of how machine learning can streamline data analysis for busy ICU clinicians to potentially optimize prognostication and decision making. © The Author(s) 2022. Published by Oxford University Press on behalf

关键词： International multicenter Interpretable models Machine learning Mortality Multiple organ dysfunction syndrome

Coulomb-corrected quantum-trajectories method for recollision-impact double ionization in an intense laser field

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Physical Review A 2023年第1期108卷 013115-013115页

作者： Yuxing Bai Xiaolei Hao Weidong Li Weifeng Yang Jing Chen Institute of Theoretical Physics and Department of Physics State Key Laboratory of Quantum Optics and Quantum Optics Devices Collaborative Innovation Center of Extreme Optics Shanxi University Taiyuan 030006 China Department of Computer Science and Technology Taiyuan University Taiyuan 237016 China Shenzhen Key Laboratory of Ultraintense Laser and Advanced Material Technology Center for Advanced Material Diagnostic Technology and College of Engineering Physics Shenzhen Technology University Shenzhen 518118 China Department of Physics School of Science Hainan University Haikou 570228 China Institute of Applied Physics and Computational Mathematics P. O. Box 8009 Beijing 100088 China

By incorporating the quantum effects and the Coulomb interaction between the first ionized electron and the residual ion in a uniform theory, the Coulomb-corrected quantum-trajectories (CCQT) method is developed to describe the recollision-impact ionization (RII) process in nonsequential double ionization of atoms in intense laser field. The results simulated by CCQT are found to be in better agreement with the experimental results, compared with those calculated by the classical-trajectory Monte Carlo (CTMC) and strong-field approximation (SFA) methods. Considering its advantage of small computational cost and back-trajectory analysis, the CCQT method will be an efficient theoretical tool to quantitatively investigate the underlying physics of the complex and coherent multielectron dynamics in intense laser field.

关键词： Atomic & molecular processes in external fields Electronic excitation & ionization Strong field excitation Atomic systems Electronic structure High-harmonic generation Optical techniques Quantum Monte Carlo Semiclassical methods

Advantages and Limitations of Quantum Routing

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PRX Quantum 2023年第1期4卷 010313-010313页

作者： Aniruddha Bapat Andrew M. Childs Alexey V. Gorshkov Eddie Schoute Lawrence Berkeley National Laboratory Berkeley California 94720 USA Joint Center for Quantum Information and Computer Science NIST/University of Maryland College Park Maryland 20742 USA Joint Quantum Institute NIST/University of Maryland College Park Maryland 20742 USA Department of Physics University of Maryland College Park Maryland 20742 USA Institute for Advanced Computer Studies University of Maryland College Park Maryland 20742 USA Department of Computer Science University of Maryland College Park Maryland 20742 USA Computer Computational and Statistical Sciences Division Los Alamos National Laboratory Los Alamos New Mexico 87545 USA

The swap gate is a ubiquitous tool for moving information on quantum hardware, yet it can be considered a classical operation because it does not entangle product states. Genuinely quantum operations could outperform swap for the task of permuting qubits within an architecture, which we call routing. We consider quantum routing in two models: (i) allowing arbitrary two-qubit unitaries, or (ii) allowing Hamiltonians with norm-bounded interactions. We lower bound the circuit depth or time of quantum routing in terms of spectral properties of graphs representing the architecture interaction constraints, and give a generalized upper bound for all simple connected n-vertex graphs. In particular, we give conditions for a superpolynomial classical-quantum routing separation, which exclude graphs with a small spectral gap and graphs of bounded degree. Finally, we provide examples of a quadratic separation between gate-based and Hamiltonian routing models with a constant number of local ancillas per qubit and of an Ω(n) speedup if we also allow fast local interactions.

关键词： Quantum algorithms

A blind spot for large language models: Supradiegetic linguistic information

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arXiv 2023年

作者： Zimmerman, Julia Witte Hudon, Denis Cramer, Kathryn Onge, Jonathan St. Fudolig, Mikaela Trujillo, Milo Z. Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab Vermont Advanced Computing Center University of Vermont BurlingtonVT05405 United States Vermont Complex Systems Center MassMutual Center of Excellence in Complex Systems and Data Science University of Vermont BurlingtonVT05405 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05405 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States

Large Language Models (LLMs) like ChatGPT reflect profound changes in the field of Artificial Intelligence, achieving a linguistic fluency that is impressively, even shockingly, human-like. The extent of their current and potential capabilities is an active area of investigation by no means limited to scientific researchers. It is common for people to frame the training data for LLMs as "text" or even "language". We examine the details of this framing using ideas from several areas, including linguistics, embodied cognition, cognitive science, mathematics, and history. We propose that considering what it is like to be an LLM like ChatGPT, as Nagel might have put it, can help us gain insight into its capabilities in general, and in particular, that its exposure to linguistic training data can be productively reframed as exposure to the diegetic information encoded in language, and its deficits can be reframed as ignorance of extradiegetic information, including supradiegetic linguistic information. Supradiegetic linguistic information consists of those arbitrary aspects of the physical form of language that are not derivable from the one-dimensional relations of context—frequency, adjacency, proximity, co-occurrence—that LLMs like ChatGPT have access to. Roughly speaking, the diegetic portion of a word can be thought of as its function, its meaning, as the information in a theoretical vector in a word embedding, while the supradiegetic portion of the word can be thought of as its form, like the shapes of its letters or the sounds of its syllables. We use these concepts to investigate why LLMs like ChatGPT have trouble handling palindromes, the visual characteristics of symbols, translating Sumerian cuneiform, and continuing integer sequences. Copyright © 2023, The Authors. All rights reserved.

关键词： computational linguistics

Local-to-Global Cross-Modal Attention-Aware Fusion for HSI-X Semantic Segmentation

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arXiv 2024年

作者： Zhang, Xuming Yokoya, Naoto Gu, Xingfa Tian, Qingjiu Bruzzone, Lorenzo The International Institute for Earth System Science Nanjing University Nanjing210023 China The Department of Complexity Science and Engineering Graduate School of Frontier Sciences The University of Tokyo Chiba277-8561 Japan The RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan The Key Laboratory of Computational Optical Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The Department of Information Engineering and Computer Science University of Trento Trento38050 Italy

Hyperspectral image (HSI) classification has recently reached its performance bottleneck. Multimodal data fusion is emerging as a promising approach to overcome this bottleneck by providing rich complementary information from the supplementary modality (X-modality). However, achieving comprehensive cross-modal interaction and fusion that can be generalized across different sensing modalities is challenging due to the disparity in imaging sensors, resolution, and content of different modalities. In this study, we propose a Local-to-Global Cross-modal Attention-aware Fusion (LoGoCAF) framework for HSI-X classification that jointly considers efficiency, accuracy, and generalizability. LoGoCAF adopts a pixel-to-pixel two-branch semantic segmentation architecture to learn information from HSI and X modalities. The pipeline of LoGoCAF consists of a local-to-global encoder and a lightweight multilayer perceptron (MLP) decoder. In the encoder, convolutions are used to encode local and high-resolution fine details in shallow layers, while transformers are used to integrate global and low-resolution coarse features in deeper layers. The MLP decoder aggregates information from the encoder for feature fusion and prediction. In particular, two cross-modality modules, the feature enhancement module (FEM) and the feature interaction and fusion module (FIFM), are introduced in each encoder stage. The FEM is used to enhance complementary information by combining the feature from the other modality across direction-aware, position-sensitive, and channel-wise dimensions. With the enhanced features, the FIFM is designed to promote cross-modality information interaction and fusion for the final semantic prediction. Extensive experiments demonstrate that our LoGoCAF achieves superior performance and generalizes well on various multimodal fusion and classification tasks. The code will be made publicly available. Copyright © 2024, The Authors. All rights reserved.

关键词： Remote sensing