Efficient Probabilistic Modeling of Crystallization at Mesoscopic Scale

TL;DR

This paper introduces CGNE, a probabilistic neural emulator that efficiently models mesoscopic crystal growth, significantly reducing inference time while accurately capturing complex, stochastic crystallization trajectories.

Contribution

The paper presents CGNE, a novel probabilistic model that overcomes training challenges to faithfully emulate mesoscopic crystallization processes with high efficiency.

Findings

CGNE achieves an 11-fold speedup in inference time.

CGNE accurately reproduces morphological properties of crystals.

Performance surpasses recent state-of-the-art probabilistic models.

Abstract

Crystallization processes at the mesoscopic scale, where faceted, dendritic growth, and multigrain formation can be observed, are of particular interest within materials science and metallurgy. These processes are highly nonlinear, stochastic, and sensitive to small perturbations of system parameters and initial conditions. Methods for the simulation of these processes have been developed using discrete numerical models, but these are computationally expensive. This work aims to scale crystal growth simulation with a machine learning emulator. Specifically, autoregressive latent variable models are well suited for modeling the joint distribution over system parameters and the crystallization trajectories. However, successfully training such models is challenging due to the stochasticity and sensitivity of the system. Existing approaches consequently fail to produce diverse and faithful crystallization trajectories. In this paper, we introduce the Crystal Growth Neural Emulator (CGNE), a probabilistic model for efficient crystal growth emulation at the mesoscopic scale that overcomes these challenges. We validate CGNE results using the morphological properties of the crystals produced by numerical simulation. CGNE delivers a factor of 11 improvement in inference time and performance gains compared with recent state-of-the-art probabilistic models for dynamical systems.