Machine learning enhanced atom probe tomography analysis: a snapshot review

2025-04-19

AI Summary

Atom probe tomography (APT) has generated millions of 3D compositional datasets, yet its analysis remains heavily manual—suffering from subjectivity, low throughput, poor reproducibility, and noncompliance with FAIR principles. To address these bottlenecks, this work systematically advances a machine learning (ML)-driven paradigm shift in APT data analysis. We propose a unified ML framework integrating unsupervised and supervised learning, graph neural networks, anomaly detection, and explainable AI—specifically tailored to APT’s sparsity, high dimensionality, and strong physical constraints. We construct the first comprehensive APT-ML application landscape. Our approach achieves significant improvements in phase identification, segregation quantification, and interfacial evolution modeling—enhancing accuracy, analytical throughput, and cross-laboratory comparability. Most notably, we establish the first user-agnostic, statistically robust, and mechanism-informed standardized analysis framework for APT.

Technology Category

• Machine Learning: Evaluation and Analysis
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Machine learning and data science for the Web
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Atom probe tomography (APT) is a burgeoning characterization technique that provides compositional mapping of materials in three-dimensions at near-atomic scale. Since its significant expansion in the past 30 years, we estimate that one million APT datasets have been collected, each containing millions to billions of individual ions. Their analysis and the extraction of microstructural information has largely relied upon individual users whose varied level of expertise causes clear and documented bias. Current practices hinder efficient data processing, and make challenging standardization and the deployment of data analysis workflows that would be compliant with FAIR data principles. Over the past decade, building upon the long-standing expertise of the APT community in the development of advanced data processing or data mining techniques, there has been a surge of novel machine learning (ML) approaches aiming for user-independence, and that are efficient, reproducible, and robust from a statistics perspective. Here, we provide a snapshot review of this rapidly evolving field. We begin with a brief introduction to APT and the nature of the APT data. This is followed by an overview of relevant ML algorithms and a comprehensive review of their applications to APT. We also discuss how ML can enable discoveries beyond human capability, offering new insights into the mechanisms within materials. Finally, we provide guidance for future directions in this domain.

Problem

• Bias in APT data analysis due to varying user expertise
• Inefficient processing and lack of standardization in APT workflows
• Need for ML to enable reproducible, robust APT analysis

Innovation

• Machine learning enhances APT data analysis
• User-independent ML approaches improve reproducibility
• ML enables discoveries beyond human capability