Autonomous Probe Microscopy with Robust Bag-of-Features Multi-Objective Bayesian Optimization: Pareto-Front Mapping of Nanoscale Structure-Property Trade-Offs

2026-01-09

AI Summary

This work addresses the limitations of high-throughput characterization in combinatorial materials libraries—namely, slow acquisition speeds, shallow information content, and difficulty in extracting interpretable structure–property relationships from complex microscopy data. The authors propose an autonomous scanning probe microscopy framework that integrates atomic force and magnetic force microscopy with a physics-informed static bag-of-features representation and multi-objective Bayesian optimization (MOBO), enabling closed-loop exploration without predefined optimization targets. Applied to an Au-Co-Ni system, the method efficiently reconstructs a feature landscape consistent with dense measurements, revealing Pareto trade-offs among surface roughness, coherence, and magnetic contrast, and identifying functional compositional clusters. This demonstrates the framework’s generality and potential for real-time, interpretable multi-objective materials discovery.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Machine Learning: Feature Construction/Reformulation

Application Category

• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Combinatorial materials libraries are an efficient route to generate large families of candidate compositions, but their impact is often limited by the speed and depth of characterization and by the difficulty of extracting actionable structure-property relations from complex characterization data. Here we develop an autonomous scanning probe microscopy (SPM) framework that integrates automated atomic force and magnetic force microscopy (AFM/MFM) to rapidly explore magnetic and structural properties across combinatorial spread libraries. To enable automated exploration of systems without a clear optimization target, we introduce a combination of a static physics-informed bag-of-features (BoF) representation of measured surface morphology and magnetic structure with multi-objective Bayesian optimization (MOBO) to discover the relative significance and robustness of features. The resulting closed-loop workflow selectively samples the compositional gradient and reconstructs feature landscapes consistent with dense grid"ground truth"measurements. The resulting Pareto structure reveals where multiple nanoscale objectives are simultaneously optimized, where trade-offs between roughness, coherence, and magnetic contrast are unavoidable, and how families of compositions cluster into distinct functional regimes, thereby turning multi-feature imaging data into interpretable maps of competing structure-property trends. While demonstrated for Au-Co-Ni and AFM/MFM, the approach is general and can be extended to other combinatorial systems, imaging modalities, and feature sets, illustrating how feature-based MOBO and autonomous SPM can transform microscopy images from static data products into active feedback for real-time, multi-objective materials discovery.

Problem

• combinatorial materials libraries
• structure-property relationships
• multi-objective optimization
• nanoscale characterization
• autonomous microscopy

Innovation

• Autonomous SPM
• Bag-of-Features
• Multi-Objective Bayesian Optimization
• Pareto-Front Mapping
• Combinatorial Materials