# Curiosity Driven Exploration to Optimize Structure-Property Learning in Microscopy

arXiv: https://arxiv.org/abs/2504.20011 · HTML: https://mlsanity.com/paper/2504.20011/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 28 Apr 2025.

## Builds on (most cited first, 7 of 7)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Exploration by Random Network Distillation](https://mlsanity.com/paper/1810.12894/) (2018)
- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Episodic Multi-agent Reinforcement Learning with Curiosity-Driven Exploration](https://mlsanity.com/paper/2111.11032/) (2021)
- [Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning](https://mlsanity.com/paper/2012.11293/) (2020)
- [Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning](https://mlsanity.com/paper/2404.12899/) (2024)

## Built on by (most cited first, 2 of 2)

- [Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments](https://mlsanity.com/paper/2508.20254/) (2025)
- [Quality-Controlled Active Learning via Gaussian Processes for Robust Structure-Property Learning in Autonomous Microscopy](https://mlsanity.com/paper/2603.29135/) (2026)

## Abstract (arXiv)

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.