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Model identifies capping materials that reduce niobium oxide formation

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Research area:chemistry-materials

What the study found

The study found that a predictive framework can identify metal capping layers that inhibit niobium oxide formation on niobium-based superconducting quantum computing devices. Using this approach, the authors identified Zr, Hf, and Ta as effective diffusion barriers.

Why the authors say this matters

The authors conclude that this closed-loop strategy, which combines first-principles theory, machine learning, and limited experimental data, enables rational design of next-generation materials. The study suggests it may help manage material defects associated with two-level systems, which are described in the abstract as degrading device performance.

What the researchers tested

The researchers used density functional theory, or DFT, to calculate oxygen interstitial and vacancy energies as thermodynamic descriptors. They then trained a logistic regression model on a limited set of experimental outcomes to predict the likelihood of oxide formation beneath different capping materials.

What worked and what didn't

The framework successfully predicted whether oxide would form beneath different capping materials. The abstract says oxide formation energy per oxygen atom was an excellent standalone descriptor for barrier performance, and that adding lattice mismatch as a secondary criterion led to Zr, Ta, and Sc as especially promising candidates. The abstract does not describe materials that clearly failed beyond the comparison set used for prediction.

What to keep in mind

The summary available here is limited to the abstract, so detailed experimental conditions, dataset size, and validation specifics are not described. The abstract also does not report performance metrics for the model or explain how strongly each candidate outperformed others.

Key points

  • A predictive framework was used to select metal capping layers that inhibit niobium oxide formation.
  • Density functional theory, or DFT, supplied oxygen interstitial and vacancy energies for the model.
  • A logistic regression model trained on limited experimental outcomes predicted oxide formation likelihood.
  • Zr, Hf, and Ta were identified as effective diffusion barriers.
  • Oxide formation energy per oxygen atom was reported as an excellent standalone descriptor.
  • Adding lattice mismatch as a criterion highlighted Zr, Ta, and Sc as especially promising candidates.

Disclosure

Research title:
Model identifies capping materials that reduce niobium oxide formation
Authors:
S. M. Chaudhari, Cristóbal Méndez, Rushil Choudhary, Tathagata Banerjee, Maciej Olszewski, Jadrien T. Paustian, Jae‐Hong Choi, Zhaslan Baraissov, Rafael Hernández, David A. Muller, B. L. T. Plourde, Gregory D. Fuchs, Valla Fatemi, T. A. Arias
Institutions:
Cornell University, Cornell University, Cornell University, Cornell University, Cornell University, Cornell University, Cornell University, Cornell University, Cornell University, Cornell University, Cornell University, Massachusetts Institute of Technology, Syracuse University, Syracuse University
Publication date:
2026-04-27
DOI:
10.1103/4pbx-wvk7
OpenAlex record:
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AI provenance: This post was generated by gpt-5.4-mini (OpenAI). The original authors did not write or review this post.

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