Quantum Machine Learning in Materials Prediction: A Case Study on ABO<sub>3</sub> Perovskite Structures
Why this work is in the frame
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Bibliographic record
Abstract
Quantum machine learning (QML), ML on quantum computers, offers a promising approach for discovering and screening novel materials. This study introduces a hybrid classical-quantum ML method using a variational quantum classifier to identify simple perovskite structures within a data set of ABO 3 compounds. The model is trained using a data set of 397 known ABO 3 compounds, with 254 perovskites and 143 non-perovskite structures labeled as +1 and −1, respectively. By considering feature correlation and eliminating less important features, the QML system achieves an optimal accuracy of 88% for training data and 87% for unseen test data. These results demonstrate the potential of QML in materials science classification tasks, even with limited training data, leveraging the intrinsic properties of quantum computation to enhance the investigation of materials. In addition, perspectives on QML applications in materials science are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it