Development and application of a high-dimensional neural network potential for boron carbide
Why this work is in the frame
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Bibliographic record
Abstract
Abstract A high-dimensional neural network potential (HDNNP) is developed to accurately model the potential energy surface of boron carbide. The HDNNP is trained on an extensive dataset of structures generated using a Monte Carlo algorithm, covering a wide range of stoichiometries and structural configurations. Density functional theory (DFT) calculations provided energy and atomic force data, which were encoded using atom-centered symmetry functions to capture local atomic environments. The resulting HDNNP exhibits exceptional predictive performance, accurately estimating energies and forces across diverse structural configurations of boron carbide. The model effectively captures essential structural, energetic, and mechanical properties, including elastic behavior and the influence of stoichiometry on stability. The HDNNP development followed an iterative refinement strategy, in which the initial training set was systematically expanded to 2931 structures by incorporating finite-temperature AIMD snapshots at 300 K, 600 K, and 1000 K, as well as extrapolative configurations from MD simulations and defect-containing cells. This approach resulted in an HDNNP model that is numerically stable and physically consistent up to 500 K, with only minor drift observed at 600 K. The HDNNP achieves exceptional accuracy in predicting energies, forces, and mechanical properties across temperatures, reproducing Young’s modulus values of 416 GPa at 10 K and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mn>420</mml:mn> <mml:mo>±</mml:mo> <mml:mn>20</mml:mn> </mml:mrow> </mml:math> GPa at 300 K, in close agreement with DFT and experimental ranges. The predicted ultimate tensile strength of 43 GPa at a failure strain of 0.14 at 300 K is consistent with atomistic simulations (40.23 GPa, strain 0.12). Comparative MD simulations show that the HDNNP outperforms Tersoff and ReaxFF in both accuracy and computational efficiency, enabling timesteps up to an order of magnitude larger, achieving 43% faster performance than Tersoff and 79% faster than ReaxFF. These results highlight the HDNNP as a robust and efficient tool for simulating boron carbide across a range of temperatures and deformation conditions, offering quantum-level accuracy for large-scale atomistic modeling in extreme environments.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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