Curiosity Driven Exploration to Optimize Structure-Property Learning in Microscopy
Journal:
arXiv
Published Date:
Apr 28, 2025
Abstract
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.
