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Artificial intelligence enabled smart design and manufacturing of advanced materials:The endless Frontier in AI^(+) era

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Materials Genome Engineering Advances 2024年第3期2卷 18-37页

作者： William Yi Wang Suyang Zhang Gaonan Li Jiaqi Lu Yong Ren Xinchao Wang Xingyu Gao Yanjing Su Haifeng Song Jinshan Li State Key Laboratory of Solidification Processing Northwestern Polytechnical UniversityXi'anShaanxiChina Innovation Center NPU ChongqingChongqingChina Institute of Applied Physics and Computational Mathematics BeijingChina Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijingChina

Future-oriented science&Technology(S&T)Strategies trigger the innovative developments of advanced materials,providing an envision to the significant progress of leading-/cutting-edge science,engineering,and technologies for the next few *** by Made in China 2025 and New Material Power Strategy by 2035,several key viewpoints about automated research workflows for accelerated discovery and smart manufacturing of advanced materials in terms of AI for science and main respective of big data,database,standards,and ecosys-tems are *** to classical toolkits at various spatial and temporal scales,AI-based toolkits and AI-enabled computations for material design are compared,highlighting the dominant role of the AI agent *** recent developed ProME platform together with its functions is introduced briefly.A case study of AI agent assistant welding is presented,which is consisted of the large language model,auto-coding via AI agent,image processing,image mosaic,and machine learning for welding defect ***,more duties are called to educate the next generation workforce with creative minds and *** is believed that the transformation of knowledge-enabled data-driven integrated computational material engineering era to AI^(+) era promotes the transformation of smart design and manufacturing paradigm from“designing the materials”to“designing with materials.”

关键词： AI agent AI for materials science auto-coding high-throughput investigations workflow

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Assessing the spectral characteristics of band splitting type II radio bursts observed by CALLISTO spectrometers

arXiv

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arXiv 2023年

作者： Minta, Felix N. Nozawa, Satoshi I. Kozarev, Kamen Elsaid, Ahmed Mahrous, Ayman Department of Space Environment Institute of Basic and Applied Sciences Egypt-Japan University of Science and Technology Alexandria21934 Egypt Department of Science Ibaraki University 2-1-1 Bunkyo Ibaraki Mito310-8512 Japan Institute of Astronomy with National Astronomical Observatory Bulgarian Academy of Sciences Sofia Bulgaria Department of Applied and Computational Mathematics Institute of Basic and Applied Sciences Egypt-Japan University of Science and Technology Alexandria Egypt Mathematics & Engineering Physics Department Faculty of Engineering Mansoura University MansouraPO 35516 Egypt

Metric type II radio bursts are usually early indicators of CME-driven shocks and other space weather phenomena in the solar corona. This paper presents a detailed investigation of the spectral properties of band splitting type II radio bursts and their association with sunspot number. Using type II radio bursts in a frequency range [20MHz -200MHz] observed by CALLISTO from 2010 to 2017, it was discovered that the analyzed type II shock height, magnetic field strength, CME/shock speed, and Alfvén speed synchronize with the trajectory of the solar cycle 24. Also, the study revealed that the onset of the declining phase of solar cycle 24 has the highest electron density. The analysis ascertained that the frequency of type II bursts depicts a bimodal distribution during the study period, with peaks in 2012 and 2015. Further, a good correlation (with correlation factor R = 0.87) was obtained between the estimated CME/shock speeds from the dynamic spectra and the associated CME speeds from SOHO/LASCO. Moreover, the study confirmed a significant correlation (R= ~0.8) between the absolute drift rates and the plasma frequency. Additionally, the study explored that ~60% of the type II radio bursts considered in this study emanated from the western longitudes. Hence, these findings emphasize that the temporal dynamics of the physical conditions of band splitting type II radio are essential parameters in space weather monitoring and forecasting. © 2023, CC BY-NC-ND.

关键词： Solar energy

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Mathematical Analysis of a Non-Smooth Mosquitoes Control Model

Mathematical Analysis of a Non-Smooth Mosquitoes Control Mod...

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International Conference on Fractional Differentiation and Its Applications (ICFDA)

作者： Doaa M. Fawzy Ayman A. Arafa A. Elsaid W. K. Zahra Institute of Basic and Applied Sciences Egypt-Japan University of Science and Technology Alexandria Egypt Department of Engineering Mathematics and Physics Faculty of Engineering Fayoum University Fayoum Egypt Department of Mathematics Faculty of Science Sohag University Sohag Egypt Department of Mathematics and Engineering Physics Faculty of Engineering Mansoura University Mansoura Egypt Department of Engineering Physics and Mathematics Faculty of Engineering Tanta University Tanta Egypt

The sterilization method (SM) is a promising strategy for preventing the escalation of dengue fever, zika and malaria through controlling mosquitoes. A fractional-order SM non-smooth Filippov system with threshold policy control is proposed, which entails sterile mosquitoes releasing to control the population of wild mosquitoes. If the density of the wild population falls below a certain threshold, sterile release is prohibited. However, if the wild population exceeds the threshold, sterile mosquitoes are released. The dynamics of this model, including various types of equilibria existence and their stability, are investigated analytically and numerically. Sliding mode dynamics and pseudo-equilibrium are obtained by using the Filippov convex method; moreover, boundary node and boundary saddle bifurcations are discovered.

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Convergence guarantees for response prediction for latent structure network time series

arXiv

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arXiv 2025年

作者： Acharyya, Aranyak Passino, Francesco Sanna Trosset, Michael W. Priebe, Carey E. The Mathematical Institute for Data Science Johns Hopkins University BaltimoreMD21218 United States The Department of Mathematics Imperial College London London United Kingdom The Department of Statistics Indiana University Bloomington BloomingtonIN47405 United States The Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD21218 United States

In this article, we propose a technique to predict the response associated with an unlabeled time series of networks in a semisupervised setting. Our model involves a collection of time series of random networks of growing size, where some of the time series are associated with responses. Assuming that the collection of time series admits an unknown lower dimensional structure, our method exploits the underlying structure to consistently predict responses at the unlabeled time series of networks. Each time series represents a multilayer network on a common set of nodes, and raw stress embedding, a popular dimensionality reduction tool, is used for capturing the unknown latent low dimensional structure. Apart from establishing theoretical convergence guarantees and supporting them with numerical results, we demonstrate the use of our method in the analysis of real-world biological learning circuits of larval Drosophila. © 2025, CC BY.

关键词： Network embeddings

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Understanding and overcoming students' difficulties in interpreting graphs: Insights from a racing car problem 23

Understanding and overcoming students' difficulties in inter...

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23rd DIDFYZ Conference on New Approaches in physics Education for Generation Z

作者： Susac, A. Sajka, M. Rosiek, R. Šipuš, Ž Milin Department of Applied Physics Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb10 000 Croatia Department of Mathematics Faculty of Exact and Natural Sciences University of the National Education Commission in Krakow ul. Podchorazych 2 Krakow30-084 Poland Institute of Technical Sciences Faculty of Exact and Natural Sciences University of the National Education Commission in Krakow ul. Podchorazych 2 Krakow30-084 Poland Department of Mathematics Faculty of Science University of Zagreb Horvatovac 102a Zagreb10 000 Croatia

The study investigates university students' difficulties in interpreting graphs through the analysis of their responses to the modified PISA racing car problem. The impact of adding a distractor that is appealing to students and supplementary questions to enhance students' graph understanding is explored. Eye-tracking technology was used to capture participants' visual attention during the task. The findings highlight the challenges of treating graphs as pictures and the benefits of incorporating additional questions to improve students' comprehension. The study emphasizes the need for instructional strategies that address intuitive reasoning and promote reflective thinking in graph interpretation. © Published under licence by IOP Publishing Ltd.

关键词： Students

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XAI Based Cattle Identification with YOLO and SIFT Technique

XAI Based Cattle Identification with YOLO and SIFT Technique

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Future Machine Learning and data science (FMLDS), IEEE International Conference on

作者： Aashu Katharria Millie Pant Kusum Deep Indu Devi Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Roorkee Uttarakhand India Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Roorkee Uttarakhand India Department of Mathematics Indian Institute of Technology Roorkee Roorkee Uttarakhand India Livestock Production Management Division ICAR-National Dairy Research Institute Karnal Haryana India

ISBN: (数字)9798350391213

ISBN: (纸本)9798350391220

In precision livestock farming, accurate cattle identification is essential for enhancing animal welfare, health monitoring, and productivity, while also supporting traceability and minimizing false insurance claims. This paper presents a novel approach for cattle identification using muzzle prints, with a focus on both efficiency and explainability. Taxicab metric, employed for efficient annotation of muzzle patterns significantly reduces the labeling time for training of YOLOv8 model. YOLOv8 is utilized for detecting muzzle prints in images, followed by SIFT (Scale-Invariant Feature Transform) for feature extraction and matching. The incorporation of Explainable AI (XAI) methods, particularly Grad-CAM, further enhances the transparency and interpretability of the SIFT model.

关键词： YOLO Training Adaptation models Accuracy Annotations Transforms Cows Feature extraction data models Labeling

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Measurement of molybdenum ion density for L-mode and H-mode plasma discharges in the EAST tokamak

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Chinese physics B 2020年第6期29卷 333-338页

作者： Yongcai Shen Hongming Zhang Bo Lyu Yingying Li Jia Fu Fudi Wang Qing Zang Baonian Wan Pan Pan Minyou Ye 不详 Institute of Plasma Physics Chinese Academy of SciencesHefei 230031China School of Mathematics and Physics Anqing Normal UniversityAnqing 246011China Department of Engineering and Applied Physics University of Science and Technology of ChinaHefei 230026China the EAST team

We report the measurement of total molybdenum ion density for L-mode and H-mode plasmas on EAST using spectral lines observation and calculation based on an impurity transport code.A flat-filed extreme ultraviolet spectrometer with some spatial resolution is used to obtain the radial profiles of molybdenum spectral line *** absolute calibration for the extreme ultraviolet spectrometer is finished by comparing the calculated bremsstrahlung intensity with the readings of CCD *** ion transport study is performed using the radial ion density profiles and one-dimensional impurity transport code *** total molybdenum density profiles are determined from the transport *** molybdenum density during L-mode and H-mode phases are obtained,which are about 3 and 4 orders of magnitude smaller than the electron density,*** inward pinch is found during the H-mode phase that leads to the peaked profile of molybdenum density.

关键词： EAST EUV spectroscopy molybdenum density L-mode and H-mode

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Regulation of Shock Response in Cage-Like High-Energy-Density Materials: Competition between Strain Energy and Structural Symmetry

SSRN

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SSRN 2025年

作者： Song, Qingguan Meng, Li Jiang, Shengli Zhang, Lei Mo, Zeyao Institute of Applied Physics and Computational Mathematics Beijing100088 China CAEP Software Center for High Performance Numerical Simulation Beijing100088 China Institute of Chemical Materials China Academy of Engineering Physics Mianyang621999 China School of Materials Science & Engineering Beijing Institute of Technology Beijing100081 China State Key Laboratory of Explosion Science and Safety Protection Beijing Institute of Technology Beijing100081 China

The cage-like structures provide a unique combination of strain energy for enhanced energy storage and hyperstatic constraints to stabilize the system, positioning them as promising pioneers in advancing high-energy-density materials (HEDMs). However, the shock response of cage-like HEDMs is highly complex and exhibits significant variability, posing a grand challenge in the underlying mechanisms. This study reveals that the shock response of cage-like HEDMs is regulated by the interplay between strain energy and structural symmetry, and introduces a physical model to quantitatively describe how they determine shock stability. The investigated systems include a comprehensive series of cage-like polynitrocubane HEDMs, with each carbon atom in the backbone progressively functionalized with nitro groups. Ab initio molecular dynamics (AIMD) simulations were employed to simulate dynamic and kinetic responses at shock velocities ranging from 8 km/s to 11 km/s, with validation provided by consistent Hugoniot curves that align with reported experimental data. Nitro substituents at the carbon-based cage structure, recognized as explosion functional groups, were found to substantially increase the system's strain energy. However, an increasing number of nitro substituents also intensified electrostatic repulsion among oxygen lone pairs, which weakens structural integrity and renders the material more susceptible to disassembly under shock conditions. Conversely, structural symmetry—including both the cage-like molecular conformation and spatial packing within the crystal lattice—was found to mitigate these destabilizing effects, effectively balancing the trade-off between energy storage and structural stability. Based on these findings, we propose a physical model that captures the essential factors driving shock initiation in cage-like HEDMs, offering new insights to inform the design and application of novel advanced HEDMs. © 2025, The Authors. All rights reserved.

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Machine Learning Based Prediction of Polaron-Vacancy Patterns on the TiO2(110) Surface

arXiv

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arXiv 2024年

作者： Birschitzky, Viktor C. Sokolović, Igor Prezzi, Michael Palotás, Krisztián Setvín, Martin Diebold, Ulrike Reticcioli, Michele Franchini, Cesare Faculty of Physics Center for Computational Materials Science University of Vienna Vienna Austria Institute of Applied Physics TU Wien Vienna Austria Institute for Solid State Physics and Optics HUN-REN Wigner Research Center for Physics Budapest Hungary Department of Surface and Plasma Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Dipartimento di Fisica e Astronomia Università di Bologna Bologna Italy

The multifaceted physics of oxides is shaped by their composition and the presence of defects, which are often accompanied by the formation of polarons. The simultaneous presence of polarons and defects, and their complex interactions, pose challenges for first-principles simulations and experimental techniques. In this study, we leverage machine learning and a first-principles database to analyze the distribution of surface oxygen vacancies (VO) and induced small polarons on rutile TiO2(110), effectively disentangling the interactions between polarons and defects. By combining neural-network supervised learning and simulated annealing, we elucidate the inhomogeneous VO distribution observed in scanning probe microscopy (SPM). Our innovative approach allows us to understand and predict defective surface patterns at previously inaccessible length scales, identifying the specific role of individual types of defects. Specifically, surface-polaron-stabilizing VO-configurations are identified, which could have consequences for surface reactivity. © 2024, CC BY.

关键词： Polarons

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A Probabilistic Framework for Pruning Transformers Via a Finite Admixture of Keys 48

A Probabilistic Framework for Pruning Transformers Via a Fin...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Nguyen, Tan M. Nguyen, Tam Bui, Long Do, Hai Nguyen, Duy Khuong Le, Dung D. Tran-The, Hung Ho, Nhat Osher, Stan J. Baraniuk, Richard G. University of California Department of Mathematics Los Angeles United States Rice University Department of Electrical and Computer Engineering Houston United States Fpt Software Ai Center Ha Noi Viet Nam Vin University College of Engineering and Computer Science Viet Nam Deakin University Applied Artificial Intelligence Institute Geelong Australia The University of Texas Department of Statistics and Data Sciences Austin United States

ISBN: (纸本)9781728163277

Pairwise dot product-based self-attention is key to the success of transformers which achieve state-of-the-art performance across a variety of applications in language and vision, but are costly to compute. It has been shown that most attention scores and keys in transformers are redundant and can be removed without loss of accuracy. In this paper, we develop a novel probabilistic framework for pruning attention scores and keys in transformers. We first formulate an admixture model of attention keys whose input data to be clustered are attention queries. We show that attention scores in self-attention correspond to the posterior distribution of this model when attention keys admit a uniform prior distribution. We then relax this uniform prior constraint and let the model learn these priors from data, resulting in a new Finite Admixture of Keys (FiAK). The learned priors are used for pruning away redundant attention scores and keys in the baseline transformers, improving the diversity of attention patterns that the models capture. We corroborate the efficiency of transformers pruned with FiAK on the ImageNet object classification and WikiText-103 language modeling tasks. Our experiments demonstrate that transformers pruned with FiAK yield similar or better accuracy than the baseline dense transformers while being much more efficient in terms of memory and computational cost. © 2023 IEEE.

关键词： admixture models pruning Transformers

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