Kavli Affiliate: David Muller
| First 5 Authors: Sarvesh Chaudhari, Sarvesh Chaudhari, , ,
| Summary:
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.
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