Neural surrogates for crystal growth dynamics with variable supersaturation: explicit vs. implicit conditioning
Simulations of crystal growth are performed by using Convolutional Recurrent Neural Network surrogate models, trained on a dataset of time sequences computed by numerical integration of Allen-Cahn dynamics including faceting via kinetic anisotropy. Two network architectures are developed to take into account the effects of a variable supersaturation value. The first infers it implicitly by processing an input mini-sequence of a few evolution frames and then returns a consistent continuation of the evolution. The second takes the supersaturation parameter as an explicit input along with a single initial frame and predicts the entire sequence. The two models are systematically tested to est...
| Authors | Matteo Rigoni, Daniele Lanzoni, Francesco Montalenti et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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