用于捕获随机薄膜沉积的多尺度物理信息神经网络框架,The Journal of Physical Chemistry C

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用于捕获随机薄膜沉积的多尺度物理信息神经网络框架
The Journal of Physical Chemistry C ( IF 3.3 ) Pub Date : 2024-02-27 , DOI: 10.1021/acs.jpcc.3c07168
Donovan Chaffart ₁ , Yue Yuan ₂ , Luis A. Ricardez-Sandoval ₃

Affiliation

这项工作概述了多尺度物理信息神经网络（PINN）方法的开发，以捕获驻点流气相沉积室内的完整多尺度薄膜生长过程。这些 PINN 使用多尺度模型进行训练，该模型由捕捉气态前体物质运动的宏观质量传递偏微分方程 (PDE) 和捕捉薄膜表面生长的微观随机 PDE (SPDE) 组成。宏观尺度和微观尺度的微分表达式都直接嵌入到损失函数中，以便两个尺度之间的所有相互通信都可以在 PINN 中直接解释。此外，尽管存在随机性，但 SPDE 仍经过级数展开和对角正交分解，将其嵌入到 PINN 中。通过与用于捕获薄膜沉积过程的基于标准 kMC 的多尺度模型进行比较，对经过充分训练的多尺度 PINN 进行了验证。总体而言，这项工作证明了 PINN 在存在分子水平随机变异的情况下捕获相互通信的多尺度系统的整个随机行为的潜力，这在之前的文献中尚未明确解决。此外，它们还说明了多尺度 PINN 的显着计算效率，比基于 kMC 和基于 SPDE 的多尺度模型快几个数量级。

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Multiscale Physics-Informed Neural Network Framework to Capture Stochastic Thin-Film Deposition

This work outlines the development of a multiscale Physics-Informed Neural Network (PINN) approach to capture the full multiscale thin-film growth process within a stagnation point flow vapor deposition chamber. These PINNs were trained using a multiscale model consisting of a macroscale mass transport partial differential equation (PDE) that captures the gaseous precursor species movement coupled with a microscale stochastic PDE (SPDE) to capture the film surface growth. Both the macroscale and microscale differential expressions were embedded directly within the loss function so that all intercommunication between the two scales could be directly accounted for within the PINN. Furthermore, the SPDE was subjected to series expansions and diagonally orthogonal decomposition to embed it within the PINNs despite the stochasticity. The fully trained multiscale PINNs were validated via comparison to the standard kMC-based multiscale model used to capture thin-film deposition processes. Overall, this work demonstrates the potential of PINNs to capture the entire stochastic behavior of an intercommunicating multiscale system in the presence of molecular-level stochastic variability, which has not been explicitly addressed previously in the literature. Furthermore, they illustrate the significant computational efficiency of the multiscale PINNs, which were orders of magnitude faster than the kMC-based and SPDE-based multiscale models.

更新日期：2024-02-27

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