مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion
مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

video

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

sound

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Version

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

View:

292
مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Download:

0
مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Cites:

Information Journal Paper

Title

Coulombic atomic descriptor for machine learning applications in condensed matter physics

Pages

  135-144

Abstract

 A main class of machine learning approaches aims at predicting a label or value of some quantity from a set of input data (e. g., recognizing a face from the pixels of a digital image). As an example of the application of such techniques in computational condensed matter physics, we demonstrate, in this study, an accurate prediction of the atomic contributions into a given physical quantity from the arrangement of neighboring atoms. We introduce a descriptor that quantifies the environment of each atom and is filled by the eigenvalues of an approximate Coulomb matrix. The descriptor is invariant under rotation or translation of the molecule and the permutation of the atomic indices. It captures fine structural deformations including the change of the four-body, dihedral angles. Employing this atomic descriptor, we exemplify a promising case where the charges on different atomic species in the molecule are predicted by machine learning to within one tenth of the elementary charge.

Cites

  • No record.

    • References

  • No record.

    • Cite

    APA: Copy

    Zarandi, Akram, & SADEGHI, ALI. (2019). Coulombic atomic descriptor for machine learning applications in condensed matter physics. JOURNAL OF RESEARCH ON MANY BODY SYSTEMS, 9(3 (22) ), 135-144. SID. https://sid.ir/paper/383547/en

    Vancouver: Copy

    Zarandi Akram, SADEGHI ALI. Coulombic atomic descriptor for machine learning applications in condensed matter physics. JOURNAL OF RESEARCH ON MANY BODY SYSTEMS[Internet]. 2019;9(3 (22) ):135-144. Available from: https://sid.ir/paper/383547/en

    IEEE: Copy

    Akram Zarandi, and ALI SADEGHI, “Coulombic atomic descriptor for machine learning applications in condensed matter physics,” JOURNAL OF RESEARCH ON MANY BODY SYSTEMS, vol. 9, no. 3 (22) , pp. 135–144, 2019, [Online]. Available: https://sid.ir/paper/383547/en

    Related Journal Papers

  • No record.

    • Related Seminar Papers

  • No record.

    • Related Plans

  • No record.

    • Recommended Workshops






    Move to top
    telegram sharing button
    whatsapp sharing button
    linkedin sharing button
    twitter sharing button
    email sharing button
    email sharing button
    email sharing button
    sharethis sharing button