用好奇心驱动采样，高效发现材料微观结构与性能的关系。
Curiosity Driven Exploration to Optimize Structure-Property Learning in Microscopy
- 基于深度学习预测误差，主动选择未知结构-性能区域采样。
- 相比随机采样，提升性能预测精度，节省计算资源。
- 适合材料科学中快速探索结构与性能关联的研究者。
在材料科学中，快速确定微观结构与性能之间的关联是理解基本机制和辅助材料设计的关键挑战。显微镜成像可直接获取局部结构信息，而光谱测量则提供功能性能数据。尽管已有深度核主动学习方法用于快速建立结构到性能的映射，但在多维且相关性强的输出空间中计算成本较高。本文提出一种轻量级好奇心驱动算法，通过深度学习代理模型预测误差，主动采样尚未探索的结构-性能关系区域。实验表明，该算法在从结构预测性能方面优于随机采样，为材料科学中的结构-性能关系高效映射提供了便捷工具。
原文摘要 · Abstract (English)
Rapidly determining structure-property correlations in materials is an important challenge in better understanding fundamental mechanisms and greatly assists in materials design. In microscopy, imaging data provides a direct measurement of the local structure, while spectroscopic measurements provide relevant functional property information. Deep kernel active learning approaches have been utilized to rapidly map local structure to functional properties in microscopy experiments, but are computationally expensive for multi-dimensional and correlated output spaces. Here, we present an alternative lightweight curiosity algorithm which actively samples regions with unexplored structure-property relations, utilizing a deep-learning based surrogate model for error prediction. We show that the algorithm outperforms random sampling for predicting properties from structures, and provides a convenient tool for efficient mapping of structure-property relationships in materials science.
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