Efficient Probabilistic Modeling of Crystallization at Mesoscopic Scale

• URL: http://arxiv.org/abs/2405.16608v1
• Date: Sun, 26 May 2024 15:37:19 GMT
• Title: Efficient Probabilistic Modeling of Crystallization at Mesoscopic Scale
• Authors: Pol Timmer, Koen Minartz, Vlado Menkovski,
• Abstract summary: Crystallization processes at the mesoscopic scale are of particular interest in materials science and metallurgy. We introduce the Crystal Growth Neural Emulator (CGNE), a probabilistic model for efficient crystal growth at the mesoscopic scale. 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.
• Score: 4.271235935891555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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