深度学习预测材料的冲击温度场

材料对冲击载荷的响应对行星科学、航空航天工程和高能材料非常重要。热激发过程如化学反应和相变在能量集中处会发生显著的加速。这是由冲击波与材料微观结构相互作用产生的结果，并受复杂的耦合过程控制，而其中的过程控制机制尚未被完全了解。

这些过程大多发生在温度、压力和应变速率的极端条件下，而且其中各种能量局部集中和微观结构特征存在于不同长度和时间尺度。因此，现有的模型都无法在没有强近似假设的情况下预测冲击诱导的热点形成。

Fig. 2 Ability of MISTnet to predict temperature fields for an unseen microstructure.

分子动力学（MD）已被广泛用于研究激波诱导的热点形成，包括孔隙率的坍塌、剪切、摩擦和局部塑性变形，但是MD方法需要巨大的计算成本。同时，深度学习已经被用于模拟材料在冲击载荷下的中尺度热机械响应，其精度与基于物理的模拟相当，但只需要的计算成本相对而言非常小。

来自普渡大学材料工程学院的Alejandro Strachan教授等人，基于UNet网络结构设计了冲击诱导温度网络（MISTnet），实现了材料中冲击温度场的预测。

Fig. 4 Comparison of hotspots obtained from MD and MISTnet.

作者通过MD方法对数个百万原子规模的系统进行冲击模拟采集数据，通过将系统内原子坐标和原子速度以及网格化的局部密度场作为输入，将网格化的局部温度场作为输出，训练的神经网络能够将初始的、冲击前的微观结构映射到冲击后的温度场，其计算成本比MD模拟的计算成本小108倍，且准确性超过了现有的模型。在泛化测试中，模型仍然准确地预测了热点的形状，虽然温度被高估了，但却准确地捕捉到了孔隙大小和方向的趋势。

该工作可以减少现有方法的经验主义，并为将微观结构与冲击载荷下材料的响应联系起来提供了有效的手段。该文近期发布于npj Computational Materials 9: 178 (2023)。

Editorial Summary

Material response to shock loading is important to planetary science, aerospace engineering, and energetic materials. Thermal excitation processes such as chemical reactions and phase transitions are significantly accelerated at energy localization. This results from the interaction of shock waves with the material’s microstructure and is controlled by complex coupling processes. The process control mechanisms are not fully understood. Most of these processes occur under extreme conditions of temperature, pressure, and strain rate, and various local energy concentrations and microstructural features exist at different length and time scales. Therefore none of the existing models are able to predict shock-induced hotspot formation without strong approximation assumptions.

Molecular dynamics (MD) has now been widely used to study shock-induced hotspot formation, including collapse of porosity, shear, friction, and local plastic deformation, but MD methods require huge computational costs. Deep learning has been used to simulate the mesoscale thermomechanical response of materials under shock loading with an accuracy comparable to physics-based simulations, but at a relatively small computational cost.

Prof. Alejandro Strachan et al. from the School of Materials Engineering, Purdue University, designed the Microstructure-Informed Shock-induced Temperature net (MISTnet) based on the UNet network structure, in order to predict the temperature field caused by shock loading. The authors used the MD method to perform shock loading simulations on several million-atom-scale systems to collect data. They used the atomic coordinates and atomic velocities in the system as well as the gridded local density field as input, and used the gridded local temperature field as the output. The trained neural network is able to map the initial, pre-shock microstructure to the post-shock temperature field with a computational cost that is 108 times smaller than that of MD simulations and an accuracy that exceeds existing models. In generalization tests, the model still accurately predicted the shape of hot spots, and although temperatures were overestimated, it accurately captured trends in pore size and orientation. This study can reduce the empiricism of existing methods and provides an effective means to relate microstructure to material response under shock loading. This article was recently published in npj Computational Materials 9: 178 (2023).

原文Abstract及其翻译

Mapping microstructure to shock-induced temperature fields using deep learning (利用深度学习技术将微结构映射到冲击诱导的温度场)

Chunyu Li, Juan Carlos Verduzco, Brian H. Lee, Robert J. Appleton & Alejandro Strachan

Abstract

The response of materials to shock loading is important to planetary science, aerospace engineering, and energetic materials. Thermally activated processes, including chemical reactions and phase transitions, are significantly accelerated by energy localization into hotspots. These result from the interaction of the shockwave with the materials’ microstructure and are governed by complex, coupled processes, including the collapse of porosity, interfacial friction, and localized plastic deformation. These mechanisms are not fully understood and the lack of models limits our ability to predict shock to detonation transition from chemistry and microstructure alone. We demonstrate that deep learning can be used to predict the resulting shock-induced temperature fields in composite materials obtained from large-scale molecular dynamics simulations with the initial microstructure as the only input. The accuracy of the Microstructure-Informed Shock-induced Temperature net (MISTnet) model is higher than the current state of the art and its evaluation requires a fraction of the computation cost.

摘要

材料对冲击载荷的响应对行星科学、航空航天工程和高能材料非常重要。热激活过程，包括化学反应和相变，会由于能量定位到热点而显著加速。这些是由冲击波与材料微观结构相互作用产生的结果，并受复杂的耦合过程控制，包括孔隙的坍塌、界面摩擦和局部塑性变形等。这些机制还没有被完全理解，且模型的缺乏限制了我们单独从化学和微观结构预测冲击带来的转变的能力。我们证明了深度学习可以用于预测复合材料的冲击诱导温度场，这些材料可以通过大规模分子动力学模拟得到，仅需要初始微观结构作为输入。微结构信息冲击诱导温度网（MISTnet）模型的精度高于目前的技术水平，其预测需要传统方法计算成本的一小部分。