Inverse material design is a cornerstone challenge in materials science, with significant applications across many industries. Traditional approaches that invert the structure–property (SP) linkage to identify microstructures with targeted properties often overlook the feasibility of production processes, leading to microstructures that may not be manufacturable. Achieving both desired properties and a realizable manufacturing procedure necessitates inverting the entire Process–Structure–Property (PSP) chain. However, this task is fraught with challenges, including stochasticity along the whole modeling chain, the high dimensionality of microstructures and process parameters, and the inherent ill-posedness of the inverse problem. This paper proposes a novel framework, named PSP-GEN, for the goal-oriented material design that effectively addresses these challenges by modeling the entire PSP chain with a deep generative model. It employs two sets of continuous, microstructure- and property-aware, latent variables, the first of which provides a lower-dimensional representation that captures the stochastic aspects of microstructure generation, while the second is a direct link to processing parameters. This structured, low-dimensional embedding not only simplifies the handling of high-dimensional microstructure data but also facilitates the application of gradient-based optimization techniques. The effectiveness and efficiency of this method are demonstrated in the inverse design of two-phase materials, where the objective is to design microstructures with target effective permeability. We compare state-of-the-art alternatives in challenging settings involving limited training data, target property regions for which no training data is available, and design tasks where the process parameters and microstructures have high-dimensional representations.

中文翻译：

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PSP-GEN：通过深入的生成式概率建模，对材料设计中的过程-结构-属性链进行随机反转


逆向材料设计是材料科学的基石挑战，在许多行业都有重要的应用。颠倒结构-性能 （SP） 联系以识别具有目标特性的微观结构的传统方法往往忽视了生产过程的可行性，导致微观结构可能无法制造。要同时实现所需的特性和可实现的制造程序，需要颠倒整个工艺-结构-特性 （PSP） 链。然而，这项任务充满了挑战，包括整个建模链的随机性、微观结构和工艺参数的高维性，以及逆问题固有的病态性。本文提出了一个名为 PSP-GEN 的新框架，用于面向目标的材料设计，通过使用深度生成模型对整个 PSP 链进行建模来有效应对这些挑战。它采用两组连续的、微观结构和属性感知的潜在变量，其中第一组提供低维表示，用于捕获微观结构生成的随机方面，而第二组则直接链接到处理参数。这种结构化的低维嵌入不仅简化了高维微观结构数据的处理，还促进了基于梯度的优化技术的应用。该方法的有效性和效率在两相材料的逆向设计中得到了证明，其目标是设计具有目标有效磁导率的微观结构。 我们比较了涉及有限训练数据的挑战性环境中最先进的替代方案，没有训练数据的目标属性区域，以及过程参数和微观结构具有高维表示的设计任务。