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DeePMD-kit v2: A software package for Deep Potential models

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

Lattice Misorientation Evolution and Grain Refinement in Al-Si Alloys under High-Strain Shear Deformation

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SSRN

SSRN 2021年

作者： Gwalani, Bharat Fu, Wenkai Olszta, Mathew Silverstein, Joshua Yadav, Digvijay R. Manimunda, Praveena Guzman, Anthony Xie, Kelvin Rohatgi, Aashish Mathaudhu, Suveen Powell, Cynthia A. Sushko, Peter V. Li, Yulan Devaraj, Arun Physical and Computational Sciences Directorate Pacific Northwest National Laboratory RichlandWA99354 United States Energy and Environment Directorate Pacific Northwest National Laboratory RichlandWA99354 United States Applied Process Engineering Laboratory Pacific Northwest National Laboratory RichlandWA99354 United States Department of Materials Science and Engineering Texas A&M University College StationTX77843 United States Bruker Nano Surfaces Division 9625 West 76th Street Eden PrairieMN55344 United States Material Science and Engineering Program University of California Riverside RiversideCA United States

The starting alloy microstructure can be tailored to achieve varying degrees of grain refinement and enhance mechanical properties through severe plastic shear deformation during solid-phase processing. Crystal plasticity-based grain misorientation modeling, coupled with systematic pin-on-disk tribometry-based subsurface shear deformation experiments on as-cast Al-xSi alloys (x = 0, 1, 4 at %), was conducted. The post-deformation microstructural analysis, through a combined computational and experimental approach, conclusively shows that the initial volume fraction of the hard Si phase enhances the evolution of local lattice misorientation, leading to efficient grain refinement during severe plastic shear deformation. The shear-deformation–induced nanostructure resulted in more than double the nanoindentation hardness in the processed alloy. © 2021, The Authors. All rights reserved.

关键词： Aluminum alloys

Analysis of Moisture and Sebum of the Skin for Monitoring Wound Healing in Older Nursing Home Residents

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Analysis of Moisture and Sebum of the Skin for Monitoring Wo...

AHFE Virtual Conference on Physical Ergonomics and Human Factors, the Virtual Conference on Social and Occupational Ergonomics, and the Virtual Conference on Cross-Cultural Decision Making, 2020

作者： Shen, Wei-Cheng Jan, Yih-Kuen Lung, Chi-Wen Anastian, Aqo Hsieh, Chang-Wei Cheng, Hsu-Tang Liao, Yin-Yin Liau, Ben-Yi Department of Digital Media Design Asia University Taichung Taiwan Rehabilitation Engineering Lab University of Illinois at Urbana-Champaign ChampaignIL United States Kinesiology and Community Health University of Illinois at Urbana-Champaign ChampaignIL United States Computational Science and Engineering University of Illinois at Urbana-Champaign ChampaignIL United States Department of Creative Product Design Asia University Taichung Taiwan Information Systems Study Program Department of Mathematics Universitas Airlangga Surabaya Indonesia Department of Photonics and Communication Engineering Asia University Taichung Taiwan Division of Plastic Surgery Asia University Hospital Taichung Taiwan Department of Biomedical Engineering Hungkuang University Taichung Taiwan

ISBN: (纸本)9783030515485

Pressure ulcers are a common problem among older nursing home residents. Wound healing monitoring in these older residents causes intensive significant workload for nurses. Moisture and sebum of the skin are critical factors during wound healing which can observations for nurses quick and handy. The purpose of this study was to investigate the relation between moisture and sebum of the skin during wound healing in older people. The data of moisture and sebum was observed for four times in sequence biweekly from the sacrum region of eighteen subjects. Based on the wound healing status, the subjects were grouped into the wound healing, wound non-healing, and non-wound groups. The wound area was calculated by manually tracing the wound area in captured images using the Image-J. Ulcer area were median correlation with moisture (r =.61) and sebum (r = −.60) in the wound healing group. The moisture was also significantly correlated with sebum (r =.89, p © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Moisture

Nuclear Neural Networks: Emulating Late Burning Stages in Core Collapse Supernova Progenitors

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arXiv 2025年

作者： Grichener, Aldana Renzo, Mathieu Kerzendorf, Wolfgang E. Farmer, Rob de Mink, Selma E. Bellinger, Earl Patrick Chan, Chi-Kwan Chen, Nutan Farag, Ebraheem Justham, Stephen Steward Steward Observatory Department of Astronomy University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Max Planck Institute for Astrophysics Karl-Schwarzschild-Str. 1 Garching85748 Germany Department of Physics Technion Haifa3200003 Israel Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Ludwig-Maximilians-Universitat Munchen Geschwister-Scholl-Platz 1 Munchen80539 Germany Department of Astronomy Yale University New HavenCT06511 United States Steward Observatory Department of Astronomy University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Avenue TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 North Santa Rita TucsonAZ85721 United States Machine Learning Research Lab Volkswagen AG Munich38440 Germany

One of the main challenges in modeling massive stars to the onset of core collapse is the computational bottleneck of nucleosynthesis during advanced burning stages. The number of isotopes formed requires solving a large set of fully-coupled stiff ordinary differential equations (ODEs), making the simulations computationally intensive and prone to numerical instability. To overcome this barrier, we design a nuclear neural network (NNN) framework with multiple hidden layers to emulate nucleosynthesis calculations and conduct a proof-of-concept to evaluate its performance. The NNN takes the temperature, density and composition of a burning region as input and predicts the resulting isotopic abundances along with the energy generation and loss rates. We generate training sets for initial conditions corresponding to oxygen core depletion and beyond using large nuclear reaction networks, and compare the predictions of the NNNs to results from a commonly used small net. We find that the NNNs improve the accuracy of the electron fraction by 280 − 660 % and the nuclear energy generation by 250 − 750 %, consistently outperforming the small network across all timesteps. They also achieve significantly better predictions of neutrino losses on relatively short timescales, with improvements ranging from 100 − 106 %. While further work is needed to enhance their accuracy and applicability to different stellar conditions, integrating NNN trained models into stellar evolution codes is promising for facilitating large-scale generation of core-collapse supernova (CCSN) progenitors with higher physical fidelity. © 2025, CC BY.

关键词： Nucleosynthesis

The Japanese Vision for the Black Hole Explorer Mission

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arXiv 2024年

作者： Akiyama, Kazunori Niinuma, Kotaro Hada, Kazuhiro Doi, Akihiro Hagiwara, Yoshiaki Higuchi, Aya E. Honma, Mareki Kawashima, Tomohisa Kolev, Dimitar Koyama, Shoko Masui, Sho Ohsuga, Ken Sano, Hidetoshi Takami, Hideki Tsunetoe, Yuh Uzawa, Yoshinori Akahori, Takuya Akiyama, Yuto Galison, Peter Hayashi, Takayuki J. Hirota, Tomoya Inoue, Makoto Iwata, Yuhei Johnson, Michael D. Kino, Motoki Kofuji, Yutaro Mizuno, Yosuke Moriyama, Kotaro Nagai, Hiroshi Nakamura, Kenta Notsu, Shota Ono, Fumie Oya, Yoko Oyama, Tomoaki Rana, Hannah Saida, Hiromi Saito, Ryo Saito, Yoshihiko Sasada, Mahito Sawada-Satoh, Satoko Takahashi, Mikiya M. Takamura, Mieko Tong, Edward Tsuji, Hiroyuki Yoshioka, Shogo Watanabe, Yoshimasa Massachusetts Institute of Technology Haystack Observatory WestfordMA01886 United States Mizusawa VLBI Observatory National Astronomical Observatory of Japan Iwate023-0861 Japan Black Hole Initiative Harvard University CambridgeMA02138 United States Graduate School of Sciences and Technology for Innovation Yamaguchi University Yamaguchi753-8512 Japan The Graduate School of Science Nagoya City University Aichi467-0001 Japan Institute of Space and Astronautical Science Japan Aerospace Exploration Agency Kanagawa252-5210 Japan Department of Space and Astronautical Science The Graduate University for Advanced Studies SOKENDAI Kanagawa229-8510 Japan Natural Science Laboratory Toyo University Tokyo112-8606 Japan Division of Science School of Science and Engineering Tokyo Denki University Saitama350-0394 Japan Department of Astronomy Graduate School of Science The University of Tokyo Tokyo113-0033 Japan Institute for Cosmic Ray Research The University of Tokyo Chiba277-8582 Japan Space Communication Systems Laboratory Wireless Networks Research Center National Institute of Information and Communications Technology Tokyo184-8795 Japan Graduate School of Science and Technology Niigata University Niigata950-2181 Japan Advanced Technology Center National Astronomical Observatory of Japan Tokyo181-8588 Japan Center for Computational Sciences University of Tsukuba Ibaraki305-8577 Japan Faculty of Engineering Gifu University Gifu501-1193 Japan Harvard University CambridgeMA02138 United States Azabu Junior and Senior High School Tokyo106-0046 Japan Astronomical Science Program The Graduate University for Advanced Studies SOKENDAI Tokyo181-8588 Japan Institute of Astronomy and Astrophysics Academia Sinica Taipei10617 Taiwan Center for Astrophysics Harvard & Smithsonian CambridgeMA02138 United States Kogakuin University of Technology & Engineering Academic Support Center 2665-1 Nakano Hachioji Tokyo192-0015 Japan Tsung-Dao Lee Institut

The Black Hole Explorer (BHEX) is a next-generation space very long baseline interferometry (VLBI) mission concept that will extend the ground-based millimeter/submillimeter arrays into space. The mission, closely aligned with the science priorities of the Japanese VLBI community, involves an active engagement of this community in the development of the mission, resulting in the formation of the Black Hole Explorer Japan Consortium. Here we present the current Japanese vision for the mission, ranging from scientific objectives to instrumentation. The Consortium anticipates a wide range of scientific investigations, from diverse black hole physics and astrophysics studied through the primary VLBI mode, to the molecular universe explored via a potential single-dish observation mode in the previously unexplored 50-70 GHz band that would make BHEX the highest-sensitivity explorer ever of molecular oxygen. A potential major contribution for the onboard instrument involves supplying essential elements for its high-sensitivity dual-band receiving system, which includes a broadband 300 GHz SIS mixer and a space-certified multi-stage 4.5K cryocooler akin to those used in the Hitomi and XRISM satellites by the Japan Aerospace Exploration Agency. Additionally, the Consortium explores enhancing and supporting BHEX operations through the use of millimeter/submillimeter facilities developed by the National Astronomical Observatory of Japan, coupled with a network of laser communication stations operated by the National Institute of Information and Communication Technology. Copyright © 2024, The Authors. All rights reserved.

关键词： Radio astronomy

A cross-sectional study on gut microbiota and inflammation in patients with chronic kidney disease

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The American journal of the medical sciences 2025年

作者： Bruna R Paiva Júnia Schultz Fluvio Modolon Jessyca S de Brito Julie A Kemp Marcia Ribeiro Marcelo Ribeiro-Alves Lia S Nakao Drielly Vargas Beatriz G Baptista Larissa Fonseca Livia Alvarenga Intikhab Alam Roberto Incitti Alexandre Soares Rosado Ludmila F M F Cardozo Denise Mafra Graduate Program in Cardiovascular Sciences Fluminense Federal University (UFF) Niterói RJ Brazil. Bioscience Program Biological and Environmental Science and Engineering (BESE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Makkah 23955 Saudi Arabia. Oceanographic Institute University of São Paulo São Paulo SP Brazil. Graduate Program in Biological Sciences - Physiology Federal University of Rio de Janeiro (UFRJ) Rio de Janeiro RJ Brazil. Graduate Program in Nutrition Sciences Fluminense Federal University (UFF) Niterói RJ Brazil. HIV/AIDS Clinical Research Center National Institute of Infectology Evandro Chagas (INI/Fiocruz) Rio de Janeiro RJ Brazil. Department of Basic Pathology Federal University of Paraná Curitiba PR Brazil. Graduate Program in Medical Sciences Fluminense Federal University (UFF) Niterói RJ Brazil. Graduate Program in Biological Sciences - Physiology Federal University of Rio de Janeiro (UFRJ) Rio de Janeiro RJ Brazil Computational Bioscience Research Center Biological and Environmental Science and Engineering (BESE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Makkah 23955 Saudi Arabia. Graduate Program in Cardiovascular Sciences Fluminense Federal University (UFF) Niterói RJ Brazil Graduate Program in Nutrition Sciences Fluminense Federal University (UFF) Niterói RJ Brazil Graduate Program in Medical Sciences Fluminense Federal University (UFF) Niterói RJ Brazil. Electronic address: dmafra30@***.

BACKGROUND:This study investigated the inflammatory and gut microbiota profile in chronic kidney disease (CKD) patients undergoing hemodialysis (HD) and peritoneal dialysis (PD). METHODS:A total of 249 patients undergoing HD and 61 patients on PD participated in the study. The mRNA expressions of nuclear factor erythroid 2-related factor-2 (NRF2), nuclear factor-κappa B (NF-κB), mitochondrial transcription factor A (TFAM), peroxisome proliferator-activated receptor-gamma coactivator 1-alpha (PGC-1α) were evaluated in peripheral blood mononuclear cells (PBMCs) by quantitative real-time PCR. Malondialdehyde (MDA), interleukin 6 (IL-6), and routine biochemical parameters were also analyzed. The fecal DNA extraction was performed, and the V4 regions of the bacterial 16S ribosomal RNA gene were sequenced. Uremic toxins such as p-cresyl sulfate (p-CS), indoxyl sulfate (IS), and indole-3-acetic acid (IAA) plasma levels were determined by HPLC. RESULTS:MDA, IS, and p-CS levels were lower in PD than in HD patients. The mRNA expression of the transcription factors was not different between groups. Gut microbial α-diversity indices showed no significant difference between groups, but the β-diversity was different in PD patients. Members of the genera Meditarraneibacter, Roseburia, Agathobacter, Anaerobutyricum, Collinsella, Streptococcus, Clostridium, and Bacteroides, as well as the families Lachnospiraceae and Enterobacteriaceae, appear to be positively correlated with most dietary factors, particularly lipid and phosphorus intake. CONCLUSION:Our findings indicate that in patients with CKD on HD, there is increased plasma retention of uremic toxins and reduced gut microbial diversity compared to PD patients.

关键词： chronic kidney disease gut microbiota inflammation mitochondrial dysfunction oxidative stress

Corrigendum: Moving Next-Generation Phase-Field Models to BMS Applications -A Case Study that Confirms Professor Uzi Landau's Foresight [J. Electrochem. Soc., 171, 063507 (2024)]

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Journal of The Electrochemical Society 2024年第8期171卷 089002-089002页

作者： Tushar K. Telmasre Anthony César Concepción Suryanarayana Kolluri Lubhani Mishra Raghav S. Thiagarajan Aditya Naveen Matam Akshay Subramaniam Taylor R. Garrick Venkat R. Subramanian Materials Science and Engineering Program Texas Materials Institute The University of Texas at Austin Austin TX 78712 United States of America Walker Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 United States of America Battery Materials and Systems Research General Motors Global Research and Development Center Warren MI 48090 United States of America Chandra Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX 78712 United States of America Energy Storage and Propulsion R&D and Manufacturing Engineering General Motors Technical Center Warren MI 48092 United States of America Affiliate Faculty Oden Institute for Computational Science and Engineering The University of Texas at Austin Austin TX 78712 United States of America

Clustering in Networks Via Kernel-ARMA Modeling and the Grassmannian: The Brain-Network Case

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arXiv 2020年

作者： Ye, Cong Slavakis, Konstantinos Patil, Pratik V. Nakuci, Johan Muldoon, Sarah F. Medaglia, John NY14260 United States Neuroscience Program University at Buffalo BuffaloNY14260 United States Department of Mathematics and the Computational and Data-Enabled Science and Engineering Program University at Buffalo SUNYNY14260 United States Department of Psychology Drexel University Perelman School of Medicine University of Pennsylvania PA19104 United States

This paper introduces a clustering framework for networks with nodes are annotated with time-series data. The framework addresses all types of network-clustering problems: State clustering, node clustering within states (a.k.a. topology identification or community detection), and even subnetwork-state-sequence identification/tracking. Via a bottom-up approach, features are first extracted from the raw nodal time-series data by kernel autoregressive-moving-average modeling to reveal non-linear dependencies and low-rank representations, and then mapped onto the Grassmann manifold (Grassmannian). All clustering tasks are performed by leveraging the underlying Riemannian geometry of the Grassmannian in a novel way. To validate the proposed framework, brain-network clustering is considered, where extensive numerical tests on synthetic and real functional Magnetic Resonance Imaging (fMRI) data demonstrate that the advocated learning framework compares favorably versus several state-of-the-art clustering schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Time series

Phase equilibrium of water with hexagonal and cubic ice using the SCAN functional

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arXiv 2021年

作者： Piaggi, Pablo Miguel Panagiotopoulos, Athanassios Z. Debenedetti, Pablo G. Car, Roberto Department of Chemistry Princeton University PrincetonNJ08544 United States Department of Chemical and Biological Engineering Princeton University PrincetonNJ08544 United States Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States Department of Physics Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Machine learning models are rapidly becoming widely used to simulate complex physicochemical phenomena with ab initio accuracy. Here, we use one such model as well as direct density functional theory (DFT) calculations to investigate the phase equilibrium of water, hexagonal ice (Ih), and cubic ice (Ic), with an eye towards studying ice nucleation. The machine learning model is based on deep neural networks and has been trained on DFT data obtained using the SCAN exchange and correlation functional. We use this model to drive enhanced sampling simulations aimed at calculating a number of complex properties that are out of reach of DFT-driven simulations and then employ an appropriate reweighting procedure to compute the corresponding properties for the SCAN functional. This approach allows us to calculate the melting temperature of both ice polymorphs, the driving force for nucleation, the heat of fusion, the densities at the melting temperature, the relative stability of ice Ih and Ic, and other properties. We find a correct qualitative prediction of all properties of interest. In some cases, quantitative agreement with experiment is better than for state-of-the-art semiempirical potentials for water. Our results also show that SCAN correctly predicts that ice Ih is more stable than ice Ic. Copyright © 2021, The Authors. All rights reserved.

关键词： Ice