This work has been published in "Modern Approaches on Materials Science", Vol 5, Issue 2, 678-682 (2023)
Our research focuses on the behavior of superconductors experiencing phase fluctuations. These materials exhibit a finite electrical resistance below their pairing temperature, Tc, unless they undergo a Berezinskii-Kosterlitz-Thouless (BKT) transition. During this transition, vortices and anti-vortices form pairs at a specific temperature, TBKT.
Understanding the distribution of vortices is crucial for comprehending vortex melting in superconductors. To accurately identify both Tc and TBKT, which often have overlapping heat capacity anomalies, we aim to utilize the Google Net model. Our objective is to determine if this model can successfully separate the overlapped anomalies, preserving the primary information about the phase transitions. Additionally, we want to assess its accuracy in predicting the Tc and TBKT values in a new system.
Through our work, we hope to demonstrate the potential of the Google Net model as an artificial intelligence tool in the field of superconductivity.
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