[2604.04194] PATHFINDER: Multi-objective discovery in structural and spectral spaces

Condensed Matter > Materials Science arXiv:2604.04194 (cond-mat) [Submitted on 5 Apr 2026] Title:PATHFINDER: Multi-objective discovery in structural and spectral spaces Authors:Kamyar Barakati, Boris N. Slautin, Utkarsh Pratiush, Hiroshi Funakubo, Sergei V. Kalinin View a PDF of the paper titled PATHFINDER: Multi-objective discovery in structural and spectral spaces, by Kamyar Barakati and 4 other authors View PDF 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 ...