Automated Materials Spectroscopy Analysis using Genetic Algorithms

TL;DR

This paper presents an open-source genetic algorithm framework for automating the analysis of materials spectroscopy data, significantly reducing manual effort and increasing efficiency in data interpretation.

Contribution

The authors developed a modular, extensible genetic algorithm software for multi-objective optimization in materials characterization, enabling automated analysis of various spectroscopy techniques.

Findings

Achieved good fitness scores with minimal human intervention.

Demonstrated effectiveness across EXAFS, XPS, and nanoindentation data.

Enhanced automation in materials data analysis workflows.

Abstract

We introduce a Genetic Algorithm (GA) based, open-source project to solve multi-objective optimization problems of materials characterization data analysis including EXAFS, XPS and nanoindentation. The modular design and multiple crossover and mutation options make the software extensible for additional materials characterization applications too. This automation of the analysis is crucial in the era when instrumentation acquires data orders of magnitude more rapidly than it can be analyzed by hand. Our results demonstrated good fitness scores with minimal human intervention.