PATHFINDER: Multi-objective discovery in structural and spectral spaces

TL;DR

PATHFINDER is an autonomous microscopy framework that balances exploration and optimization across structural, spectral, and measurement spaces, enabling discovery of diverse, scientifically valuable states.

Contribution

It introduces a novel multi-objective exploration and optimization framework combining novelty detection with Pareto-based acquisition for autonomous microscopy.

Findings

Expanded the accessible structure-property landscape in experiments.

Avoided premature convergence on a single optimum.

Demonstrated effectiveness on STEM EELS data and ferroelectric microscopy.

Abstract

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.