Characterizing the potential energy surface of two dimensional and bulk materials using high dimensional neural network potentials
Bibliographic record
Abstract
Computing material properties at the ab-initio level of detail is computationally prohibitive for large systems or long timescales. As a result, such methods cannot be used to efficiently sample configuration space. Force field methods can efficiently sample configuration space, but rely on large parameter sets that are tuned to specific contexts.\nIn this work we will explore the ??net approach and its application to six systems:\n2D silica, bulk silica, graphene, diamond, hexagonal boron nitride, and cubic boron nitride. Here, a general mapping from atomic coordinates to the potential energy surface is obtained using a feed-forward artificial neural network. An approximate Density Functional Theory method, Density Functional Tight Binding (DFTB+), is used to compute quantities required for the reference dataset. It is found that a network made up of linear activation functions in ??net is (almost) equivalent to a one-layer radial basis function network, and is sufficient to learn a reference dataset consisting of structures sampled from a canonical ensemble at various temperatures. We look at how sampling outside of these frequently visited energy states, through data augmentation, significantly increases the complexity of the problem.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".