يعرض 1 - 10 نتائج من 83 نتيجة بحث عن '"Tersoff potential"', وقت الاستعلام: 1.32s تنقيح النتائج
  1. 1
    دورية أكاديمية

    المساهمون: Department of Physics

    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

  2. 2
    دورية أكاديمية
  3. 3
    دورية أكاديمية

    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

    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

  4. 4
    دورية أكاديمية

    المساهمون: Helsinki Institute of Physics

    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

  5. 5
  6. 6
  7. 7
    دورية أكاديمية
  8. 8
    دورية أكاديمية
  9. 9
    دورية أكاديمية
  10. 10