npj: 机械学习带着相场走来了—又快又准的模拟方式

海归学者发起的公益学术平台 分享信息,整合资源 交流学术,偶尔风月 相场方法是一种流行的介观尺度计算方法,用于研究微结构及其物理性质的时空演化。它已被广泛用于描述各种重要的介观尺度演化现象,包括晶粒生长和粗化、凝固、薄膜沉积、位错动力学、生物

海归学者提议的公益学术平台

分享信息,整合资源

交流学术,偶然风月

相场方式是一种盛行的介观尺度盘算方式,用于研究微结构及其物理性质的时空演化。它已被普遍用于形貌种种主要的介观尺度演化征象,包罗晶粒生长和粗化、凝固、薄膜沉积、位错动力学、生物膜中的囊泡形成和裂纹流传。现有的高保真相场模子现实盘算成本很高,由于它们需要解决一组形貌这些历程的延续场变量的耦合偏微分方程系统。现在,最大限度地降低盘算成本的探索主要集中在行使高性能盘算架构和先进的数值方案,或将机械学习算法与微观结构模拟相结合。然而,对于这些乐成的解决方案来说,若何平衡精度与盘算效率也照样个令人头痛的问题。要么盘算效率高就不能保证获得正确解;要么可以求解庞大的、耦合的相场方程,却盘算成本高昂;要么能够展望训练局限之内的微观结构演化,却展望不了训练之外的演化。

来自美国桑迪亚国家实验室集成纳米技术中央的Rémi Dingreville教授向导的团队,开发了一个机械学习框架来高效、快速地展望庞大的微结构演化问题。通过接纳长短期影象(LSTM)神经网络学习历久模式和解决历史依赖性问题,作者将微结构演化问题重新表述为多变量时间序列问题。在这种情况下,神经网络能学习若何通过微结构随时间演化的低维形貌来展望微结构的演化。他们发现这种机械学习的替换模子,可以在几分之一秒的时间内展望两相混合物在亚稳态剖析时的非线性微观结构演化,与高保真相场模拟相比,准确性仅损失5%。作者解释,该替换模子轨迹作为经典高保真相场模子的输入数据时,可以加速相场模拟。作者的解决方案开拓了一条很有前途的门路,在尺度征象至关主要的问题中(如质料设计等演化问题),可行使他们加速的相场模拟来发现、求解和展望加工-微结构-性能关系。


该文近期发表于npj Computational Materials 7: 3 (2021),英文题目与摘要如下,点击左下角“阅读原文”可以自由获取论文PDF。



Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods 

David Montes de Oca Zapiain, James A. Stewart & Rémi Dingreville 

The phase-field method is a powerful and versatile computational approach for modeling the evolution of microstructures and associated properties for a wide variety of physical, chemical, and biological systems. However, existing high-fidelity phase-field models are inherently computationally expensive, requiring high-performance computing resources and sophisticated numerical integration schemes to achieve a useful degree of accuracy. In this paper, we present a computationally inexpensive, accurate, data-driven surrogate model that directly learns the microstructural evolution of targeted systems by combining phase-field and history-dependent machine-learning techniques. We integrate a statistically representative, low-dimensional description of the microstructure, obtained directly from phase-field simulations, with either a time-series multivariate adaptive regression splines autoregressive algorithm or a long short-term memory neural network. The neural-network-trained surrogate model shows the best performance and accurately predicts the nonlinear microstructure evolution of a two-phase mixture during spinodal decomposition in seconds, without the need for “on-the-fly” solutions of the phase-field equations of motion. We also show that the predictions from our machine-learned surrogate model can be fed directly as an input into a classical high-fidelity phase-field model in order to accelerate the high-fidelity phase-field simulations by leaping in time. Such machine-learned phase-field framework opens a promising path forward to use accelerated phase-field simulations for discovering, understanding, and predicting processing–microstructure–performance relationships.

扩展阅读

 

ACS Energy Letters:开源相场助力能源质料新发现

npj:开源、通用—新的相场模拟框架

npj: 新质料发现—机械学习加速遗传算法

npj: 分类算法—外面和2D质料的原子结构谱系的自动分类

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