# PATHFINDER: Multi-objective discovery in structural and spectral spaces

arXiv: https://arxiv.org/abs/2604.04194 · HTML: https://mlsanity.com/paper/2604.04194/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 47 references that are in the map.
- First posted on arXiv 5 Apr 2026.

## Builds on (most cited first, 12 of 12)

- [Learning two-phase microstructure evolution using neural operators and autoencoder architectures](https://mlsanity.com/paper/2204.07230/) (2022)
- [Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning](https://mlsanity.com/paper/2205.15458/) (2022)
- [SANE: Strategic Autonomous Non-Smooth Exploration for Multiple Optima Discovery in Multi-modal and Non-differentiable Black-box Functions](https://mlsanity.com/paper/2409.12295/) (2024)
- [Unraveling the Impact of Initial Choices and In-Loop Interventions on Learning Dynamics in Autonomous Scanning Probe Microscopy](https://mlsanity.com/paper/2402.00071/) (2024)
- [Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features](https://mlsanity.com/paper/2501.18064/) (2025)
- [Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments](https://mlsanity.com/paper/2508.20254/) (2025)
- [BEACON: A Bayesian Optimization Inspired Strategy for Efficient Novelty Search](https://mlsanity.com/paper/2406.03616/) (2024)
- [Rapid optimization in high dimensional space by deep kernel learning augmented genetic algorithms](https://mlsanity.com/paper/2410.03173/) (2024)
- [Causal Discovery from Data Assisted by Large Language Models](https://mlsanity.com/paper/2503.13833/) (2025)
- [Domain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy](https://mlsanity.com/paper/2506.08073/) (2025)
- [Separating physically distinct mechanisms in complex infrared plasmonic nanostructures via machine learning enhanced electron energy loss spectroscopy](https://mlsanity.com/paper/2009.08501/) (2020)
- [Reward driven discovery of the optimal microstructure representations with invariant variational autoencoders](https://mlsanity.com/paper/2510.00243/) (2025)

## Abstract (arXiv)

Automated decision-making is becoming key for automated characterization including electron and scanning probe microscopies and nano indentation. Most machine learning driven workflows optimize a single predefined objective and tend to converge prematurely on familiar responses, overlooking rare but scientifically important states. More broadly, the challenge is not only where to measure next, but how to coordinate exploration across structural, spectral, and measurement spaces under finite experimental budgets while balancing target-driven optimization with novelty discovery. Here we introduce PATHFINDER, a framework for autonomous microscopy that combines novelty driven exploration with optimization, helping the system discover more diverse and useful representations across structural, spectral, and measurement spaces. By combining latent space representations of local structure, surrogate modeling of functional response, and Pareto-based acquisition, the framework selects measurements that balance novelty discovery in feature and object space and are informative and experimentally actionable. Benchmarked on pre acquired STEM EELS data and realized experimentally in scanning probe microscopy of ferroelectric materials, this approach expands the accessible structure property landscape and avoids collapse onto a single apparent optimum. These results point to a new mode of autonomous microscopy that is not only optimization-driven, but also discovery-oriented, broad in its search, and responsive to human guidance.