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1دورية أكاديمية
المؤلفون: Kuopanportti, Pekko, Ropo, Matti, Holmberg, Daniel, Levamaki, Henrik, Kokko, Kalevi, Granroth, Sari, Kuronen, Antti
المساهمون: Department of Physics
مصطلحات موضوعية: Iron-chromium alloy, Molecular dynamics, Tersoff potential, EMBEDDED-ATOM METHOD, ORDER, ALLOYS, MINIMIZATION, ENERGIES, STATE, 114 Physical sciences
وصف الملف: application/pdf
العلاقة: This work was supported by the Academy of Finland (Grants No. 308632 and No. 308633) . The computational resources granted by the CSC - IT Center for Science, Finland, and by the Finnish Grid and Cloud Infrastructure project (FGCI; urn:nbn:fi:research-infras-2016072533) are gratefully acknowledged, as are the facilities provided by the Turku University Centre for Materials and Surfaces (MatSurf) .; Kuopanportti , P , Ropo , M , Holmberg , D , Levamaki , H , Kokko , K , Granroth , S & Kuronen , A 2022 , ' Interatomic Fe-Cr potential for modeling kinetics on Fe surfaces ' , Computational Materials Science , vol. 203 , 110840 . https://doi.org/10.1016/j.commatsci.2021.110840Test; ORCID: /0000-0003-0795-8003/work/106337446; e54ce3fb-7536-4226-abaf-cd60b4b0065b; http://hdl.handle.net/10138/338530Test; 000734348800002
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2دورية أكاديمية
المؤلفون: Yoshiaki KOGURE, Tomoko FUNAYAMA, Yasutaka UCHIDA
المصدر: Sensors & Transducers, Vol 237, Iss 9-10, Pp 137-143 (2019)
مصطلحات موضوعية: germanium thin film, molecular dynamics, tersoff potential, crystallization, stress-strain relation, flexible sensor, communication device, Technology (General), T1-995
وصف الملف: electronic resource
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3دورية أكاديمية
المؤلفون: O. V. Uvarova, S. I. Uvarov, О. В. Уварова, С. И. Уваров
المساهمون: This work was supported by the Russian Foundation for Basic Research, project No. 19-29-03051 MK. The calculations were performed using the computing cluster of the Federal Research Center of the Institute of Management of the Russian Academy of Sciences., Работа выполнена при поддержке РФФИ проект № 19-29-03051 мк. При проведении расчетов использовался вычислительный кластер ФИЦ ИУ РАН.
المصدر: Izvestiya Vysshikh Uchebnykh Zavedenii. Materialy Elektronnoi Tekhniki = Materials of Electronics Engineering; Том 23, № 4 (2020); 304-310 ; Известия высших учебных заведений. Материалы электронной техники; Том 23, № 4 (2020); 304-310 ; 2413-6387 ; 1609-3577 ; 10.17073/1609-3577-2020-4
مصطلحات موضوعية: машинное обучение, potential energy of structure, Tersoff potential, machine learning potential, Gaussian Approximation Potential, Gaussian Process Regression, machine learning, потенциальная энергия структуры, потенциал Терсоффа, машиннообучаемый потенциал, Gaussian Approximation Potentials
وصف الملف: application/pdf
العلاقة: https://met.misis.ru/jour/article/view/431/342Test; Powell D. Elasticity, lattice dynamics and parameterization techniques for the Tersoff potential applied to elemental and type III—V semiconductors: dis. University of Sheffield, 2006. 259 p. URL: https://etheses.whiterose.ac.uk/15100/1/434519.pdfTest; Abgaryan K. K., Volodina O. V., Uvarov S. I. Mathematical modeling of point defect cluster formation in silicon based on molecular dynamic approach // Modern Electronic Materials. 2015. V. 1, N 3. P. 82—87. DOI:10.1016/j.moem.2016.03.001; Bartók-Pįrtay A. The Gaussian Approximation Potential: an interatomic potential derived from first principles quantum mechanics. Springer Science & Business Media, 2010. 107 p. DOI:10.1007/978-3-642-14067-9; Круглов И. А. Поиск новых соединений, изучение их стабильности и свойств с использованием современных методов компьютерного дизайна материалов: Дисс. канд. физ.-мат. наук. М.: Ин-т физики высоких давлений им. Л.Ф. Верещагина РАН, 2018. 112 c.; Gramacy R. B. Surrogates: Gaussian process modeling, design, and optimization for the applied sciences. Chapman and Hall/CRC, 2020. 559 p.; Vorontsov K. Mathematical Learning Methods on Precedents. Course of Lectures, 2006.; Rupp M., Tkatchenko A., Müller K.-R., von Lilienfeld O. A. Fast and accurate modeling of molecular atomization energies with machine learning // Phys. Rev. Lett. 2012. V. 108, N 5. P. 058301. DOI:10.1103/PhysRevLett.108.058301; Faber F., Lindmaa A., von Lilienfeld O. A., Armiento R. Crystal structure representations for machine learning models of formation energies // Int. J. Quantum Chem. 2015. V. 115, N 16. P. 1094—1101. DOI:10.1002/qua.24917; Bartók A. P., Csányi G. Gaussian approximation potentials: A brief tutorial introduction // Int. J. Quantum Chem. 2015. V. 115, N 16. P. 1051—1057. DOI:10.1002/qua.24927; Abgaryan K. K., Mutigullin I. V., Uvarov S. I., Uvarova O. V. Multiscale Modeling of Clusters of Point Defects in Semiconductor Structures // CEUR Workshop Proceedings, 2019. P. 43—51. http://ceur-ws.org/Vol-2426/paper7.pdfTest; Deringer V. L., Csányi G. Machine learning based interatomic potential for amorphous carbon // Phys. Rev. B. 2017. V. 95, N 9. P. 094203. DOI:10.1103/PhysRevB.95.094203; Novikov I. S., Shapeev A. V. Improving accuracy of interatomic potentials: more physics or more data? A case study of silica // Materials Today Commun. 2019. V. 18. P. 74—80. DOI:10.1016/j.mtcomm.2018.11.008; Wu S. Q., Ji M., Wang C. Z., Nguyen M. C., Zhao X., Umemoto K., Wentzcovitch R. M., Ho K. M. An adaptive genetic algorithm for crystal structure prediction // J. Phys.: Condens. Matter. 2014. V. 26, N 3. P. 035402. DOI:10.1088/0953-8984/26/3/035402; Coifman R. R., Kevrekidis I. G., Lafon S., Maggioni M., Nadler B. Diffusion maps, reduction coordinates, and low dimensional representation of stochastic systems // Multiscale Model. Simul. 2008. V. 7, N 2. P. 842—864. DOI:10.1137/070696325; Behler J., Parrinello M. Generalized neural-network representation of high-dimensional potential-energy surfaces // Phys. Rev. Lett. 2007. V. 98, N 14. P. 146401. DOI:10.1103/PhysRevLett.98.146401; Hastie T., Tibshirani R., Friedman J. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, 2009. 767 p. DOI:10.1007/b94608; https://met.misis.ru/jour/article/view/431Test
الإتاحة: https://doi.org/10.17073/1609-3577-2020-4-304-310Test
https://doi.org/10.17073/1609-3577-2020-4Test
https://doi.org/10.1016/j.moem.2016.03.001Test
https://doi.org/10.1103/PhysRevLett.108.058301Test
https://doi.org/10.1002/qua.24917Test
https://doi.org/10.1002/qua.24927Test
https://doi.org/10.1103/PhysRevB.95.094203Test
https://doi.org/10.1088/0953-8984/26/3/035402Test
https://doi.org/10.1137/070696325Test
https://doi.org/10.1103/PhysRevLett.98.146401Test -
4دورية أكاديمية
المؤلفون: Fan, Zheyong, Chen, Wei, Vierimaa, Ville, Harju, Ari
المساهمون: Helsinki Institute of Physics
مصطلحات موضوعية: Molecular dynamics simulation, Many-body potential, Tersoff potential, Stillinger-Weber potential, Graphics processing units, Virial stress, Heat current, THERMAL-CONDUCTIVITY, PERFORMANCE, GPU, ORDER, IMPLEMENTATION, SYSTEMS, 113 Computer and information sciences, 114 Physical sciences
وصف الملف: application/pdf
العلاقة: This work was supported by the Academy of Finland through its Centres of Excellence Programme (2015-2017) under project number 284621 and National Natural Science Foundation of China under Grant Nos. 11404033 and 11504384. We acknowledge the computational resources provided by Aalto Science-IT project, Finland's IT Center for Science (CSC), and China Scientific Computing Grid (ScGrid). We thank the great help from the GPU experts from CSC and NVIDIA during the GPU hackathon organized by Sebastian von Alfthan.; Fan , Z , Chen , W , Vierimaa , V & Harju , A 2017 , ' Efficient molecular dynamics simulations with many-body potentials on graphics processing units ' , Computer Physics Communications , vol. 218 , pp. 10-16 . https://doi.org/10.1016/j.cpc.2017.05.003Test; 85019566902; bb4f6b61-8f0e-4ce9-a120-d0575c952bcd; http://hdl.handle.net/10138/307537Test; 000404204800002
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5دورية أكاديمية
المؤلفون: Zheyong Fan, Yanzhou Wang, Xiaokun Gu, Ping Qian, Yanjing Su, Tapio Ala-Nissila
مصطلحات موضوعية: Condensed Matter Physics, Tersoff potential, empirical potential fitting, genetic algorithm, thermal conductivity, phonon dispersion, molecular dynamics (MD) simulation, graphics processing units
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مصطلحات موضوعية: Theory and design of materials, Nanomaterials, blue phosphorene, 2d material interfaces, density functional theory, Graph Neural Network (GNN), Tersoff potential
الإتاحة: https://doi.org/10.25417/uic.21288066.v1Test
https://figshare.com/articles/dataset/Blue_Phosphorene_Dataset_for_Graph_Neural_Network_Training/21288066Test -
7دورية أكاديمية
المؤلفون: Leyu Wang, James D. Lee, Cing-Dao Kan
المصدر: International Journal of Smart and Nano Materials, Vol 7, Iss 3, Pp 144-178 (2016)
مصطلحات موضوعية: Thermodynamic conjugacy, logarithmic strain, atomistic stress, deformation gradient, strain energy, interatomic potential, Tersoff potential, atomistic first-order Piola–Kirchhoff stress, atomistic second-order Piola–Kirchhoff stress, atomistic Kirchhoff stress, virial stress, Materials of engineering and construction. Mechanics of materials, TA401-492
وصف الملف: electronic resource
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8دورية أكاديمية
المؤلفون: A. S. Minkin, A. A. Knizhnik, B. V. Potapkin
المصدر: Компьютерные исследования и моделирование, Vol 7, Iss 3, Pp 549-558 (2015)
مصطلحات موضوعية: GPGPU, OpenCL, many-body potentials, Tersoff potential, embedded-atom potential, atomic operations, Applied mathematics. Quantitative methods, T57-57.97, Mathematics, QA1-939
وصف الملف: electronic resource
العلاقة: http://crm.ics.org.ru/uploads/crmissues/crm_2015_3/15724.pdfTest; https://doaj.org/toc/2076-7633Test; https://doaj.org/toc/2077-6853Test
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9دورية أكاديمية
المؤلفون: THE HUNG TRAN, SZYMON WINCZEWSKI
المصدر: TASK Quarterly, Vol 21, Iss 3 (2017)
مصطلحات موضوعية: central-force decomposition, Tersoff potential, molecular dynamics, empirical potentials, Information technology, T58.5-58.64
وصف الملف: electronic resource
العلاقة: https://journal.mostwiedzy.pl/TASKQuarterly/article/view/1798Test; https://doaj.org/toc/1428-6394Test
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10دورية أكاديمية