A Fourier-transformed feature engineering design for predicting ternary perovskite properties by coupling a two-dimensional convolutional neural network with a support vector machine (Conv2D-SVM)
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Bibliographic record
Abstract
Abstract In computational material sciences, Machine Learning (ML) techniques are now competitive alternatives that can be used in determining target properties conventionally resolved by ab initio quantum mechanical simulations or experimental synthesization. The successes realized with ML-based techniques often rely on the quality of the design architecture, in addition to the descriptors used in representing a chemical compound with good target mapping property. With the perovskite crystal structure at the forefront of modern energy materials discovery, accurately estimating related target properties is even of high importance due to the role such properties may have in defining the functionalization. As a result, the present study proposes a new feature engineering approach that takes advantage of both the direct ionic features and the periodic Fourier transformed reciprocal features of a three-dimensional perovskite polyhedral. The study is conducted on about 27,000 ABX 3 perovskite structures with the stability energy, the formation energy, and the energy bandgap as targets. For accurate modeling, a feature-extracting two-dimensional convolutional neural network (Conv2D) is coupled with a prediction-enhancing Support Vector Machine (SVM) to form a hybridized Conv2D-SVM architecture. A comparison with previous benchmark evaluations reveals appreciable improvements in modeling accuracy for all target properties, particularly for the energy bandgap, for which the feature extraction approach yields 0.105 eV MAE, 0.301 eV RMSE, and 93.48% R 2 . Besides, the proposed design is further demonstrated to out-perform other similar periodic feature engineering approaches in the Coulomb matrix, Ewald-sum matrix, and Sine matrix, all in their absolute eigenvalue forms. All preprocessed data, source codes, and relevant sample calculations are openly available at: github.com/chenebuah/high_dim_descriptor.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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