U-PolyConformer: Spatiotemporal machine learning for microstructure engineering
Bibliographic record
Abstract
• A novel spatiotemporal CNN-Transformer machine learning framework is proposed. • The first framework that captures full-field stress and strain evolution under arbitrary loading conditions. • Exhibits strong generalization; accurately capturing loading conditions, microstructures, and hardening outside the training distributions. • A novel microstructure optimization framework is employed to enhance formability by delaying localization. Accelerating the prediction of mechanical behaviour in heterogenous materials is critical for large-scale microstructure optimization and realizing functionally optimized materials. While existing machine learning approaches have demonstrated an ability to accelerate predictions for the full-field mechanical response of a wide range of heterogenous microstructures, they have been largely limited to monotonic loading conditions. This paper introduces U-PolyConformer, a spatiotemporal machine learning framework that combines U-Net convolutional neural networks with transformer layers, capable of capturing the full-field stress and strain evolution under monotonic and random walk loading conditions. Trained on a large dataset of crystal plasticity finite element method (CPFEM) simulations with FCC polycrystals, the model accurately captures complex phenomena, including strain localization and stress unloading. The U-PolyConformer achieves a 7,900x speed-up over the ground-truth CPFEM simulations while producing high-fidelity results in both interpolative and extrapolative regimes. Comprehensive evaluations demonstrate the U-PolyConformer’s capacity to generalize outside the training distribution to novel microstructures, loading conditions, and strain hardening behaviours. To highlight the model’s potential as a surrogate for accelerating computational materials engineering workflows, a microstructure optimization framework based on static recrystallization is introduced and used to delay the onset of localization. This framework is successfully used to identify the grains which initiate the onset of localization, illustrating how the proposed model and optimization framework may be used for identifying and exploring property-performance relationships.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".