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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Characterization of halotolerant phosphate-solubilizing rhizospheric bacteria from mangrove (Avicennia sp.) with biotechnological potential in agriculture and pollution mitigation

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Biocatalysis and Agricultural Biotechnology 2024年 55卷

作者： Dey, Gobinda Maity, Jyoti Prakash Banerjee, Pritam Sharma, Raju Kumar Etesami, Hassan Bastia, Tapan Kumar Rath, Prasanta Sukul, Uttara Huang, Hsien-Bin Huang, Kuo-Wei Chen, Chien-Yen Department of Agricultural Chemistry National Taiwan University Taipei 106319 Taiwan Department of Biomedical Sciences Graduate Institute of Molecular Biology National Chung Cheng University 168 University Road Chiayi County Ming-Shung 62102 Taiwan Doctoral Program in Science Technology Environment and Mathematics Department of Earth and Environmental Sciences National Chung Cheng University 168 University Rd. Chiayi County Min-Hsiung 62102 Taiwan Environmental Science Laboratory Department of Chemistry Biological Laboratory School of Applied Sciences KIIT Deemed to Be University Odisha Bhubaneswar 751024 India Department of Environmental Science Policy and Management University of California Berkeley CA United States Department of Chemistry and Biochemistry National Chung Cheng University 168 University Road Chiayi County Min-Hsiung 62102 Taiwan Department of Soil Science University of Tehran Tehran 31587-77871 Iran Environmental Science Laboratory Department of Chemistry School of Applied Sciences KIIT Deemed to Be University Odisha Bhubaneswar 751024 India Department of Otolaryngology Ditmanson Medical Foundation Chia-Yi Christian Hospital Taiwan Center for Nano Bio-Detection Center for Innovative Research on Aging Society AIM-HI National Chung Cheng University Chiayi 62102 Taiwan

Mangroves represent intricate and ever-changing ecosystems, exhibiting fluctuations in water level, salinity, and nutrient (such as NPK) availability as well as a wide array of unique bacterial communities. Microbial interactions in different components (e.g., tree roots) of the mangrove ecosystem are crucial to understand the ecosystem functioning for potential application in pollution mitigation and agricultural production. This study aimed to isolate phosphate-solubilizing bacteria (PSB) from the rhizosphere sediment of mangrove (Avicennia sp.) in terms of heavy metals (HMs) and salinity tolerance as well as plant growth promoting (PGP) traits, where the effective PSB were used on Brassica chinensis for their seed priming and growth under salinity stress. The effective two PSB isolates were identified by 16S rRNA, where JKD01 and JKD02 were closely related (99%) to Enterobacter cloacae (OQ271412) and Kocuria rhizophila (OQ271413), respectively. Both the strains exhibited phosphate solubilization, IAA, NH_3, and EPS production ability as well as HMs resistant ability where, E. cloacae (OQ271412) and K. rhizophila (OQ271413), are effectively remove the Cu from the water with 33.23% and 27.54%, respectively. The FTIR results showed functional group shifts (carboxyl, phosphate, and amino) in Cu-treated bacterial biomass and intracellular Cu presence, indicating the involvement of Cu in the bio-sorption and intracellular bioaccumulation process, respectively. The findings suggested PSB tolerance to salinity and HMs are present in mangroves, making them a valuable source for isolating effective bacteria to reduce stress in plants, lower HMs accumulation in mangroves, and aid in the bioremediation of HMs-contaminated environments. © 2023

关键词： Bio-priming Copper remediation Heavy metal tolerant Mangrove Phosphate-solubilizing bacteria Salt tolerant

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Universal mechanical response of metallic glasses during strain-rate-dependent uniaxial compression

arXiv

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arXiv 2022年

作者： Jin, Weiwei Datye, Amit Schwarz, Udo D. Shattuck, Mark D. O'Hern, Corey S. Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Chemical and Environmental Engineering Yale University New HavenCT06520 United States Benjamin Levich Institute Physics Department The City College of New York New YorkNY10031 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

Experimental data on the compressive strength σmax versus strain rate Ε eng for metallic glasses undergoing uniaxial compression shows significantly different behavior for different alloys. For some metallic glasses, σmax decreases with increasing Ε eng, for others, σmax increases with increasing Ε eng, and for others σmax versus Ε eng is nonmonotonic. Using numerical simulations of metallic glasses undergoing uniaxial compression at nonzero strain rate and temperature, we show that they obey a universal relation for the compressive strength versus temperature, which determines their mechanical response. At low Ε eng, increasing strain rate leads to increases in temperature and decreases in σmax∗, whereas at high Ε eng, increasing strain rate leads to decreases in temperature and increases in σmax∗. This non-monotonic behavior of σmax∗ versus temperature causes the nonmonotonic behavior of σmax∗ versus Ε eng. Variations in the internal dissipation change the characteristic strain rate at which the nonmonotonic behavior occurs. These results are general for a wide range of metallic glasses with different atomic interactions, damping coefficients, and chemical compositions. © 2022, CC BY.

关键词： Strain rate

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Isotope effects on molecular structures and electronic properties of liquid water via deep potential molecular dynamics based on SCAN functional

arXiv

引用

arXiv 2020年

作者： Xu, Jianhang Zhang, Chunyi Zhang, Linfeng Chen, Mohan Santra, Biswajit Wu, Xifan Department of Physics Temple University PhiladelphiaPA19122 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States CAPT HEDPS College of Engineering Peking University Beijing100871 China Institute for Computational Molecular Science Temple University PhiladelphiaPA19122 United States

Feynman path-integral deep potential molecular dynamics (PI-DPMD) calculations have been employed to study both light (H2O) and heavy water (D2O) within the isothermal–isobaric ensemble. In particular, the deep neural network is trained based on ab initio data obtained from the strongly constrained and appropriately normed (SCAN) exchange-correlation functional. Because of the lighter mass of hydrogen than deuteron, the properties of light water is more influenced by nuclear quantum effect than those of heavy water. Clear isotope effects are observed and analyzed in terms of hydrogen-bond structure and electronic properties of water that are closely associated with experimental observables. The molecular structures of both liquid H2O and D2O agree well with the data extracted from scattering experiments. The delicate isotope effects on radial distribution functions and angular distribution functions are well reproduced as well. Our approach demonstrates that deep neural network combined with SCAN functional based ab initio molecular dynamics provides an accurate theoretical tool for modeling water and its isotope effects. Copyright © 2020, The Authors. All rights reserved.

关键词： Electronic properties

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Overview of the Head and Neck Tumor Segmentation for Magnetic Resonance Guided Applications (HNTS-MRG) 2024 Challenge

arXiv

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arXiv 2024年

作者： Wahid, Kareem A. Dede, Cem El-Habashy, Dina M. Kamel, Serageldin Rooney, Michael K. Khamis, Yomna Abdelaal, Moamen R.A. Ahmed, Sara Corrigan, Kelsey L. Chang, Enoch Dudzinski, Stephanie O. Salzillo, Travis C. McDonald, Brigid A. Mulder, Samuel L. McCullum, Lucas Alakayleh, Qusai Sjogreen, Carlos He, Renjie Mohamed, Abdallah S.R. Lai, Stephen Y. Christodouleas, John P. Schaefer, Andrew J. Naser, Mohamed A. Fuller, Clifton D. Department of Radiation Oncology The University of Texas MD Anderson Cancer HoustonTX United States Department of Imaging Physics The University of Texas MD Anderson Cancer HoustonTX United States Transitional Year Program Corewell Health Wiliam Beaumont Royal OakMI United States Department of Radiation Oncology University of Maryland School of Medicine BaltimoreMD United States Department of Clinical Oncology and Nuclear Medicine Faculty of Medicine Alexandria University Alexandria Egypt UT MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences Houston United States Department of Radiation Oncology Baylor College of Medicine HoustonTX United States Department of Head and Neck Surgery The University of Texas MD Anderson Cancer HoustonTX United States Elekta AtlantaGA United States Department of Computational Applied Mathematics and Operations Research Rice University HoustonTX United States

Magnetic resonance (MR)-guided radiation therapy (RT) is enhancing head and neck cancer (HNC) treatment through superior soft tissue contrast and longitudinal imaging capabilities. However, manual tumor segmentation remains a significant challenge, spurring interest in artificial intelligence (AI)-driven automation. To accelerate innovation in this field, we present the Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Challenge, a satellite event of the 27th International Conference on Medical Image Computing and Computer Assisted Intervention. This challenge addresses the scarcity of large, publicly available AI-ready adaptive RT datasets in HNC and explores the potential of incorporating multi-timepoint data to enhance RT auto-segmentation performance. Participants tackled two HNC segmentation tasks: automatic delineation of primary gross tumor volume (GTVp) and gross metastatic regional lymph nodes (GTVn) on pre-RT (Task 1) and mid-RT (Task 2) T2-weighted scans. The challenge provided 150 HNC cases for training and 50 for testing, hosted on *** using a Docker submission framework. In total, 19 independent teams from across the world qualified by submitting both their algorithms and corresponding papers, resulting in 18 submissions for Task 1 and 15 submissions for Task 2. Evaluation using the mean aggregated Dice Similarity Coefficient showed top-performing AI methods achieved scores of 0.825 in Task 1 and 0.733 in Task 2. These results surpassed clinician interobserver variability benchmarks, marking significant strides in automated tumor segmentation for MR-guided RT applications in HNC. © 2024, CC BY.

关键词： Magnetic resonance imaging

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

arXiv

引用

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

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Convergence acceleration of ensemble Kalman inversion in nonlinear settings

arXiv

引用

arXiv 2019年

作者： Chada, Neil K. Tong, Xin T. Applied Mathematics and Computational Science Program King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Department of Mathematics National University of Singapore 119077 Singapore

Many data-science problems can be formulated as an inverse problem, where the parameters are estimated by minimizing a proper loss function. When complicated black-box models are involved, derivative-free optimization tools are often needed. The ensemble Kalman filter (EnKF) is a particle-based derivative-free Bayesian algorithm originally designed for data assimilation. Recently, it has been applied to inverse problems for computational efficiency. The resulting algorithm, known as ensemble Kalman inversion (EKI), involves running an ensemble of particles with EnKF update rules so they can converge to a minimizer. In this article, we investigate EKI convergence in general nonlinear settings. To improve convergence speed and stability, we consider applying EKI with non-constant step-sizes and covariance inflation. We prove that EKI can hit critical points with finite steps in non-convex settings. We further prove that EKI converges to the global minimizer polynomially fast if the loss function is strongly convex. We verify the analysis presented with numerical experiments on two inverse problems. Copyright © 2019, The Authors. All rights reserved.

关键词： Inverse problems

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A computational fluid dynamics model for the small-scale dynamics of wave, ice floe and interstitial grease ice interaction

arXiv

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arXiv 2021年

作者： Marquart, Rutger Bogaers, Alfred Skatulla, Sebastian Alberello, Alberto Toffoli, Alessandro Nisters, Carina Vichi, Marcello Computational Continuum Mechanics Research Group Department of Civil Engineering University of Cape Town South Africa Ex Mente Technologies Pretoria South Africa School of Computer Science and Applied Mathematics University of Witwatersrand South Africa Graduate School of Frontier Sciences University of Tokyo Japan Department of Infrastructure Engineering University of Melbourne Australia Institute of Mechanics University of Duisburg-Essen Germany Department of Oceanography University of Cape Town South Africa

The marginal ice zone is a highly dynamical region where sea ice and ocean waves interact. Large-scale sea ice models only compute domain-averaged responses. As the majority of the marginal ice zone consists of mobile ice floes surrounded by grease ice, finer-scale modelling is needed to resolve variations of its mechanical properties, wave-induced pressure gradients and drag forces acting on the ice floes. A novel computational fluid dynamics approach is presented, that considers the heterogeneous sea ice material composition and accounts for the wave-ice interaction dynamics. Results show, after comparing three realistic sea ice layouts with similar concentration and floe diameter, that the discrepancy between the domain-averaged temporal stress and strain rate evolutions increases for decreasing wave period. Furthermore, strain rate and viscosity are mostly affected by the variability of ice floe shape and diameter. Copyright © 2021, The Authors. All rights reserved.

关键词： Drag

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Testing Biased Randomization Assumptions and Quantifying Imperfect Matching and Residual Confounding in Matched Observational Studies

arXiv

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arXiv 2021年

作者： Chen, Kan Heng, Siyu Long, Qi Zhang, Bo Graduate Group of Applied Mathematics and Computational Science School of Arts and Sciences University of Pennsylvania PhiladelphiaPA United States Department of Biostatistics School of Global Public Health New York University New York CityNY United States Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Vaccine and Infectious Disease Division Fred Hutchinson Cancer Center SeattleWA United States

One central goal of design of observational studies is to embed non-experimental data into an approximate randomized controlled trial using statistical matching. Despite empirical researchers' best intention and effort to create high-quality matched samples, residual imbalance due to observed covariates not being well matched often persists. Although statistical tests have been developed to test the randomization assumption and its implications, few provide a means to quantify the level of residual confounding due to observed covariates not being well matched in matched samples. In this article, we develop two generic classes of exact statistical tests for a biased randomization assumption. One important by-product of our testing framework is a quantity called residual sensitivity value (RSV), which provides a means to quantify the level of residual confounding due to imperfect matching of observed covariates in a matched sample. We advocate taking into account RSV in the downstream primary analysis. The proposed methodology is illustrated by re-examining a famous observational study concerning the effect of right heart catheterization (RHC) in the initial care of critically ill patients. Code implementing the method can be found in the supplementary materials. © 2021, CC BY.

关键词： Statistical tests

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ABACUS: An Electronic Structure Analysis Package for the AI Era

arXiv

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arXiv 2025年

作者： Zhou, Weiqing Zheng, Daye Liu, Qianrui Lu, Denghui Liu, Yu Lin, Peize Huang, Yike Peng, Xingliang Bao, Jie J. Cai, Chun Jin, Zuxin Wu, Jing Zhang, Haochong Jin, Gan Ji, Yuyang Shen, Zhenxiong Liu, Xiaohui Sun, Liang Cao, Yu Sun, Menglin Liu, Jianchuan Chen, Tao Liu, Renxi Li, Yuanbo Han, Haozhi Liang, Xinyuan Bao, Taoni Chen, Nuo Ren, Hongxu Zhang, Xiaoyang Liu, Zhaoqing Fu, Yiwei Liu, Maochang Li, Zhuoyuan Wen, Tongqi Tang, Zechen Xu, Yong Duan, Wenhui Wang, Xiaoyang Gu, Qiangqiang Dai, Fu-Zhi Zheng, Qijing Zhao, Jin Zhang, Yuzhi Ou, Qi Jiang, Hong Liu, Shi Xu, Ben Xu, Shenzhen Ren, Xinguo He, Lixin Zhang, Linfeng Chen, Mohan AI for Science Institute Beijing100080 China HEDPS CAPT School of Physics College of Engineering Peking University Beijing100871 China College of Engineering Peking University Beijing100871 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China Supercomputing Center University of Science and Technology of China Anhui Hefei230026 China Institute of Physics Chinese Academy of Sciences Beijing100190 China School of Materials Science and Engineering Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China College of Chemistry and Molecular Engineering Peking University Beijing100871 China International Research Center for Renewable Energy State Key Laboratory of Multiphase Flow Xi’an Jiaotong University Shaanxi Xi’an710049 China Suzhou Academy of Xi’an Jiaotong University Jiangsu Suzhou215123 China Center for Structural Materials Department of Mechanical Engineering The University of Hong Kong Hong Kong The University of Hong Kong Shenzhen China State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Frontier Science Center for Quantum Information Beijing China Saitama Wako351-0198 Japan Institute for Advanced Study Tsinghua University Beijing100084 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China School of Materials Science and Engineering University of Science and Technology Beijing Beijing100083 China Department of Physics University of Science and

ABACUS (Atomic-orbital Based Ab-initio Computation at USTC) is an open-source software for first-principles electronic structure calculations and molecular dynamics simulations. It mainly features density functional theory (DFT) and is compatible with both plane-wave basis sets and numerical atomic orbital basis sets. ABACUS serves as a platform that facilitates the integration of various electronic structure methods, such as Kohn-Sham DFT, stochastic DFT, orbital-free DFT, and real-time time-dependent DFT, etc. In addition, with the aid of high-performance computing, ABACUS is designed to perform efficiently and provide massive amounts of first-principles data for generating general-purpose machine learning potentials, such as DPA models. Furthermore, ABACUS serves as an electronic structure platform that interfaces with several AI-assisted algorithms and packages, such as DeePKS-kit, DeePMD, DP-GEN, DeepH, DeePTB, etc. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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