Generative modeling has opened new avenues for solving previously intractable materials design problems. However, these new opportunities are accompanied by a drastic increase in the required amount of training data. This is in stark juxtaposition to the high expense and difficulty in curating such large materials datasets. In this work, we propose a novel framework for integrating generative models within an active learning loop. This enables the training of generative models with datasets significantly smaller than what has previously been demonstrated, providing a direct route for their application in data constrained environments. The functionality of this framework is then demonstrated by addressing the challenge of designing polycrystalline textures associated with target anisotropic mechanical properties. The developed protocol exhibited a cost reduction between 14 to 18 times over a randomly sampled experimental design.

中文翻译：

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使用条件归一化流设计多晶纹理的主动学习


创成式建模为解决以前棘手的材料设计问题开辟了新的途径。然而，这些新机会伴随着所需训练数据量的急剧增加。这与管理如此大型材料数据集的高成本和难度形成鲜明对比。在这项工作中，我们提出了一种新的框架，用于将生成模型集成到主动学习循环中。这使得使用比以前演示的数据集小得多的数据集来训练生成模型，从而为它们在数据受限环境中的应用提供了直接的途径。然后，通过解决设计与目标各向异性机械性能相关的多晶纹理的挑战来证明该框架的功能。与随机采样的实验设计相比，开发的方案的成本降低了 14 到 18 倍。