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October 20, 2025Open Access

Beyond Diamond: Interpretable Machine Learning Discovery of Coherent Quantum Defect Hosts in Semiconductors

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Authors

MMMohammed MahshookRBRudra Banerjee

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Overview

This study demonstrates machine learning predicts quantum defect-host materials in semiconductors, highlighting key properties and candidate materials.

Key Points

  • The model effectively identifies quantum-compatible defect-host materials, enhancing material discovery for quantum applications.
  • Trained on a curated dataset, the machine learning model achieves a high Matthews correlation coefficient (MCC > 0.95) for predictions.
  • First-principles calculations corroborate predicted properties, including coherence-related characteristics and formation energetics.
  • The approach reveals both known and novel candidates, paving the way for future advancements in spin qubit technology.

Cite This Study

Mahshook et al. (2025) studied this question.

synapsesocial.com/papers/68f6379bb481a140a36cf519https://doi.org/10.48550/arxiv.2506.03844
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