Machine-learning Guided Search for Phonon-mediated Superconductivity in Boron and Carbon Compounds

TL;DR

This study combines ab-initio calculations with machine learning to identify and analyze potentially superconducting boron and carbon compounds, including those with unstable phonon modes, predicting several promising candidates with specific critical temperatures.

Contribution

It introduces a workflow that integrates ML-guided search with phonon stability analysis, addressing the overlooked role of imaginary phonon modes in superconductivity predictions.

Findings

Predicted several new superconducting compounds with specific T_c values.

Compared ML models' performance on dynamically unstable compounds.

Addressed T_c convergence issues in phonon calculations.

Abstract

We present a workflow that iteratively combines \textit{ab-initio} calculations with a machine-learning (ML) guided search for superconducting compounds with both dynamical stability and instability from imaginary phonon modes, the latter of which have been largely overlooked in previous studies. Electron-phonon coupling (EPC) properties and critical temperature (T$_c$) of 417 boron, carbon, and borocarbide compounds have been calculated with density functional perturbation theory (DFPT) and isotropic Eliashberg approximation. Our study addresses T$_c$ convergence of Brillouin zone sampling with an ansatz test, stabilizing imaginary phonon modes for significant EPC contributions and comparing performance of two ML models especially when including compounds of dynamical instability. We predict a few promising superconducting compounds with formation energy just above the ground state convex hull, such as Ca$_5$B$_3$N$_6$ (35 K), TaNbC$_2$ (28.4 K), Nb$_3$B$_3$C (16.4 K), Y$_2$B$_3$C$_2$ (4.0 K), Pd$_3$CaB (7.0 K), MoRuB$_2$ (15.6 K), RuVB$_2$ (15.0 K), RuSc$_3$C$_4$ (6.6 K) among others.