Article
Engineering, Electrical & Electronic
Guoxing Huang, Wenqing Qian, Jingwen Wang, Weidang Lu, Hong Peng
Summary: In this article, an image reconstruction algorithm based on the sequential Monte Carlo principle for electromagnetic tomography (EMT) is proposed. It increases the diversity of samples for image reconstruction and maintains a high reconstruction effect for different flow patterns.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Nuclear Science & Technology
Si-Yuan Luo, Yu-He Huang, Xuan-Tao Ji, Lie He, Wan-Cheng Xiao, Feng-Jiao Luo, Song Feng, Min Xiao, Xiao-Dong Wang
Summary: Muon tomography is a novel method for non-destructive material imaging using muon rays. A hybrid model tomography combining scattering tomography and coinciding muon trajectory density tomography is proposed and verified. Simulation results show that this method can image materials with different atomic numbers simultaneously and provide higher image quality.
NUCLEAR SCIENCE AND TECHNIQUES
(2022)
Article
Nanoscience & Nanotechnology
Chang Liu, Zhen Zhang, Jun Ding, En Ma
Summary: Reverse Monte Carlo (RMC) simulations are widely used for generating three-dimensional models of amorphous materials. This study evaluates the reliability of RMC modeling for metallic glasses by comparing it with molecular dynamics simulations. The results show that RMC-generated structures lack accuracy in reproducing the local atomic packing, and additional constraints and validation check points are advised for obtaining a physically stable and meaningful atomic configuration.
SCRIPTA MATERIALIA
(2023)
Article
Geochemistry & Geophysics
Xi Tang, Jiawen Li, Lixiao Wang, Feng Han, Hai Liu, Qing Huo Liu
Summary: A hybrid method for reconstructing subsurface 3-D objects with electromagnetic fields is proposed, using FRTM to determine approximate locations and sizes, VBIM to reconstruct shapes and dielectric parameters based on those results, and MCM for further refinement. Numerical simulations demonstrate the effectiveness of this method for subsurface imaging and detection.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2021)
Article
Computer Science, Software Engineering
In-Young Cho, Yuchi Huo, Sung-Eui Yoon
Summary: The paper introduces a contrastive manifold learning framework to effectively utilize path-space features, resulting in considerable improvements in reconstruction networks.
ACM TRANSACTIONS ON GRAPHICS
(2021)
Article
Nuclear Science & Technology
Xuan-Tao Ji, Si-Yuan Luo, Yu-He Huang, Kun Zhu, Jin Zhu, Xiao-Yu Peng, Min Xiao, Xiao-Dong Wang
Summary: This study presents a new μon imaging system for low and medium Z objects. The system acquires data using the coincidence detection technique of cosmic-ray muon and its secondary particles, and develops a multi-dimensional imaging algorithm for image reconstruction.
NUCLEAR SCIENCE AND TECHNIQUES
(2022)
Article
Materials Science, Multidisciplinary
N. Qureshi, H. E. Fischer, S. X. M. Riberolles, T. C. Hansen, M. Ciomaga Hatnean, O. A. Petrenko
Summary: In this study, we investigate the short-range magnetic spin correlations in two compounds of rare-earth strontium oxides using total-scattering powder neutron diffraction, reverse Monte Carlo simulations, and magnetic pair-distribution function analysis. The compounds exhibit a distorted honeycomb lattice, leading to significant geometrical frustration due to antiferromagnetic exchange between magnetic ions. The results demonstrate the ordering of the short-range spin correlations above the respective Néel temperatures, indicating the dominance of nearest and next-nearest interactions.
Article
Computer Science, Interdisciplinary Applications
Valentin Niess
Summary: We present an algorithm for simulating reverse Monte Carlo decays using an existing forward Monte Carlo decay engine, implemented in the Alouette library. We provide a detailed description of Alouette and validation results.
COMPUTER PHYSICS COMMUNICATIONS
(2023)
Article
Chemistry, Multidisciplinary
Nileena Nandakumaran, Lester Barnsley, Artem Feoktystov, Sergei A. Ivanov, Dale L. Huber, Lisa S. Fruhner, Vanessa Leffler, Sascha Ehlert, Emmanuel Kentzinger, Asma Qdemat, Tanvi Bhatnagar-Schoeffmann, Ulrich Ruecker, Michael T. Wharmby, Antonio Cervellino, Rafal E. Dunin-Borkowski, Thomas Brueckel, Mikhail Feygenson
Summary: This study investigates the self-assembly of iron oxide nanoparticles into chains in a dispersion, highlighting the reversible formation of chains under specific magnetic fields. The visualization of real-space assemblies of IONPs in dispersions serves as a novel tool for biomedical researchers, allowing rapid exploration of IONPs behavior and extraction of equilibrium structure parameters.
ADVANCED MATERIALS
(2021)
Article
Materials Science, Ceramics
P. Jovari, A. Chrissanthopoulos, K. S. Andrikopoulos, I. Pethes, I. Kaban, S. Kohara, B. Beuneu, S. N. Yannopoulos
Summary: We conducted a detailed study on the short- and medium-range order of glassy KSb5S8, a potential phase change material, using experimental and simulation techniques. The study involved diffraction techniques, EXAFS, and reverse Monte Carlo simulation to obtain accurate structural data and generate structural models. Density functional theory was also employed to investigate the structure and vibrational modes of selected clusters in the glassy structure. The findings revealed the coordination of Sb-S, the distribution of bridging S atoms, and the identification of a specific Raman mode associated with hypervalent bonding in the glass structure.
JOURNAL OF NON-CRYSTALLINE SOLIDS
(2023)
Article
Optics
Pavel Subochev, Florentin Spadin, Valeriya Perekatova, Aleksandr Khilov, Andrey Kovalchuk, Ksenia Pavlova, Alexey Kurnikov, Martin Frenz, Michael Jaeger
Summary: We propose a GPU-accelerated implementation of frequency-domain synthetic aperture focusing technique (SAFT) using truncated regularized inverse k-space interpolation. Our implementation achieves sub-1s reconstruction time for data sizes of up to 100 M voxels, providing more than a tenfold decrease in reconstruction time compared to CPU-based SAFT. We provide an empirical model that can predict the execution time of quasi-3D reconstruction for any data size given the specifications of the computing system.
Article
Mathematics, Applied
Gordon E. Sarty
Summary: TRASE MRI encodes image information using spatially varying phases, where data falls into traditional k-space when coils are perfect, and into non-Cartesian coordinates when coils are imperfect. This new mathematical set-up may have further developments as TRASE technology evolves.
LINEAR ALGEBRA AND ITS APPLICATIONS
(2021)
Article
Materials Science, Ceramics
Karel Saksl, Ildiko Pethes, Pal Jovari, Zuzana Molcanova, Juraj Durisin, Beata Ballokova, Laszlo Temleitner, Stefan Michalik, Michaela Sulikova, Katarina Sulova, Milos Fejercak, Dagmara Varcholova, Rastislav Motyl
Summary: Amorphous alloys composed of magnesium, zinc, and calcium are being studied for potential use as biodegradable orthopaedic implants. Among these alloys, Mg66Zn30Ca4 shows promise with its high compressive strength and good glass formability due to the presence of densely packed Zn-centered clusters in its atomic structure.
JOURNAL OF NON-CRYSTALLINE SOLIDS
(2021)
Article
Materials Science, Ceramics
I. M. Kirian, A. D. Rud, O. S. Roik, V. P. Kazimirov, O. M. Yakovenko, A. M. Lakhnik
Summary: The structure of liquid Al87Mg13 alloy was investigated using X-ray diffraction, reverse Monte Carlo simulations, and Voronoi diagram method. The study calculated the quantitative characteristics of short-range order and found the presence of chemical short-range order and icosahedral structure.
JOURNAL OF NON-CRYSTALLINE SOLIDS
(2022)
Article
Engineering, Manufacturing
Feiyu Xiong, Yanping Lian, Ming-Jian Li, Jiaqi Ouyang, Yufan Liu
Summary: This paper proposes an extended cellular automaton finite volume method (xCAFVM) for predicting the melt pool flow and grain structure evolution in wire-based metal additive manufacturing. The method uses a cellular automaton model coupled with an improved Monte Carlo model to predict grain coarsening in the heat-affected zone. The proposed method can provide insights into the relationship between the process and the microstructure and guide parameter optimization.
ADDITIVE MANUFACTURING
(2023)
Article
Physics, Condensed Matter
Rie Y. Umetsu, Masato Tsujikawa, Kotaro Saito, Kanta Ono, Toru Ishigaki, Ryosuke Kainuma, Masafumi Shirai
JOURNAL OF PHYSICS-CONDENSED MATTER
(2019)
Article
Physics, Applied
Hiroshi Tsukahara, Kaoru Iwano, Tadashi Ishikawa, Chiharu Mitsumata, Kanta Ono
PHYSICAL REVIEW APPLIED
(2019)
Article
Physics, Multidisciplinary
Kenta Takahashi, Ryo Kato, Mario Okawa, Tetsuji Okuda, Akira Yasui, Eiji Ikenaga, Kanta Ono, Noriaki Hamada, Tomohiko Saitoh
JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN
(2019)
Article
Materials Science, Multidisciplinary
Hiroshi Tsukahara, Kaoru Iwano, Tadashi Ishikawa, Chiharu Mitsumata, Kanta Ono
NPG ASIA MATERIALS
(2020)
Article
Physics, Applied
Munehisa Matsumoto, Takafumi Hawai, Kanta Ono
PHYSICAL REVIEW APPLIED
(2020)
Article
Chemistry, Physical
Yoshihiko Ozaki, Yuta Suzuki, Takafumi Hawai, Kotaro Saito, Masaki Onishi, Kanta Ono
NPJ COMPUTATIONAL MATERIALS
(2020)
Article
Multidisciplinary Sciences
Yuta Suzuki, Hideitsu Hino, Takafumi Hawai, Kotaro Saito, Masato Kotsugi, Kanta Ono
SCIENTIFIC REPORTS
(2020)
Article
Chemistry, Physical
Tetsuro Ueno, Hideaki Ishibashi, Hideitsu Hino, Kanta Ono
Summary: An automated stopping method for spectral measurements using active learning is proposed, which achieves optimal stopping based on the upper bound of the posterior average of generalization error in Gaussian process regression. The method provides an approximated X-ray absorption spectrum with sufficient accuracy and reduced data size. It not only validates the concept of optimal stopping in active learning, but also enhances the efficiency of spectral measurements for high-throughput experiments in the era of materials informatics.
NPJ COMPUTATIONAL MATERIALS
(2021)
Article
Materials Science, Multidisciplinary
Hiroshi Tsukahara, Hiroshi Imamura, Chiharu Mitsumata, Kiyonori Suzuki, Kanta Ono
Summary: Soft magnetic materials are crucial for the magnetic cores in motors and generators. Understanding the mechanism of energy loss under oscillating magnetic fields is important for improving energy efficiency. Micromagnetic simulations revealed that the magnetic energy of moving domain walls in nanocrystalline soft magnetic materials dissipates through magnetostriction, thus providing guidance for the design of highly energy-efficient materials.
NPG ASIA MATERIALS
(2022)
Article
Computer Science, Artificial Intelligence
Yuta Suzuki, Tatsunori Taniai, Kotaro Saito, Yoshitaka Ushiku, Kanta Ono
Summary: Understanding the structure-functionality relationships of materials is key to material development. This study shows that self-supervised deep learning can successfully learn material embeddings from over 120,000 crystal structures, revealing similarities and functionality-aware similarities between materials. These findings enable the mapping of the materials space and facilitate more strategic approaches to material development.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Eri Teruya, Tadashi Takeuchi, Hidekazu Morita, Takayuki Hayashi, Kanta Ono
Summary: This paper introduces an autonomous research topic selection (ARTS) system that analyzes research information in articles to construct research concept networks and selects potential research topics that are likely to reveal new scientific facts yet have not been extensively studied.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2022)
Article
Nanoscience & Nanotechnology
Sho Goto, Hiroaki Kura, Hideto Yanagihara, Eiji Kita, Masahito Tsujikawa, Ryusei Sasaki, Masafumi Shirai, Yasuhiro Kobayashi, Takashi Honda, Kanta Ono
ACS APPLIED NANO MATERIALS
(2019)
Article
Materials Science, Multidisciplinary
S. Dash, H. Enomoto, T. Kajita, K. Ono, K. Horiba, M. Kobayashi, H. Kumigashira, V Kandyba, A. Giampietri, A. Barinov, F. Stramaglia, N. L. Saini, T. Katsufuji, T. Mizokawa
Article
Materials Science, Multidisciplinary
M. Suzuki, B. Gao, K. Koshiishi, S. Nakata, K. Hagiwara, C. Lin, Y. X. Wan, H. Kumigashira, K. Ono, Sungmo Kang, Seungjin Kang, J. Yu, M. Kobayashi, S-W Cheong, A. Fujimoril
Article
Multidisciplinary Sciences
Kotaro Saito, Masao Yano, Hideitsu Hino, Tetsuya Shoji, Akinori Asahara, Hidekazu Morita, Chiharu Mitsumata, Joachim Kohlbrecher, Kanta Ono
SCIENTIFIC REPORTS
(2019)
