Active-Learning Inspired $\textit{Ab Initio}$ Theory-Experiment Loop Approach for Management of Material Defects: Application to Superconducting Qubits

TL;DR

This paper presents an active-learning inspired framework combining first-principles calculations, machine learning, and limited experiments to predict and design metal capping layers that prevent oxide formation in superconducting qubits.

Contribution

It introduces a novel closed-loop approach integrating DFT, logistic regression, and experimental data for rational material design to inhibit oxide formation.

Findings

Zr, Hf, and Ta identified as effective diffusion barriers.

Oxide formation energy per oxygen atom is a strong standalone predictor.

Combining oxide energy with lattice mismatch improves candidate selection.

Abstract

Surface oxides are associated with two-level systems (TLSs) that degrade the performance of niobium-based superconducting quantum computing devices. To address this, we introduce a predictive framework for selecting metal capping layers that inhibit niobium oxide formation. Using DFT-calculated oxygen interstitial and vacancy energies as thermodynamic descriptors, we train a logistic regression model on a limited set of experimental outcomes to successfully predict the likelihood of oxide formation beneath different capping materials. This approach identifies Zr, Hf, and Ta as effective diffusion barriers. Our analysis further reveals that the oxide formation energy per oxygen atom serves as an excellent standalone descriptor for predicting barrier performance. By combining this new descriptor with lattice mismatch as a secondary criterion to promote structurally coherent interfaces, we identify Zr, Ta, and Sc as especially promising candidates. This closed-loop strategy integrates first-principles theory, machine learning, and limited experimental data to enable rational design of next-generation materials.