Deep-learning approach to the structure of amorphous silicon
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present a deep-learning approach for modeling the atomic structure of amorphous silicon ($a$-Si). While accurate models of disordered systems require an ab initio description of the energy landscape which severely limits the attainable system size, large-scale models rely on empirical potentials, at the price of reduced reliability and a computational load that is still restricting for many purposes. In this paper, we explore an approach based on deep learning, particularly generative modeling that could reconcile both requirements of accuracy and efficiency by learning structural features from data. When trained on a set of observations, such models can generate new structures very efficiently with the desired level of accuracy, as determined by the data set. We first validate our approach by training a convolutional neural network to approximate the potential-energy surface of $a$-Si, as given by the Stillinger-Weber potential, which results in a root-mean-square error of 5.05 meV per atom---about $0.16%$ of the atomic energy. We then train a deep generative model, the Wasserstein autoencoder, for the generation of $a$-Si configurations. Our approach leads to models which exhibit some of the essential features of $a$-Si while possessing too much structural disorder, thus suggesting that the method is viable; we indicate avenues for improving it towards the generation of state-of-the-art structures.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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