# Unsupervised Learning of Nanoindentation Data to Infer Microstructural Details of Complex Materials

arXiv: https://arxiv.org/abs/2309.06613 · HTML: https://mlsanity.com/paper/2309.06613/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 44 references that are in the map.
- First posted on arXiv 12 Sep 2023.

## Builds on (most cited first, 2 of 2)

- [Minimum adjusted Rand index for two clusterings of a given size](https://mlsanity.com/paper/2002.03677/) (2020)
- [Automated analysis of continuum fields from atomistic simulations using statistical machine learning](https://mlsanity.com/paper/2206.08048/) (2022)

## Abstract (arXiv)

In this study, Cu-Cr composites were studied by nanoindentation. Arrays of indents were placed over large areas of the samples resulting in datasets consisting of several hundred measurements of Young's modulus and hardness at varying indentation depths. The unsupervised learning technique, Gaussian mixture model, was employed to analyze the data, which helped to determine the number of "mechanical phases" and the respective mechanical properties. Additionally, a cross-validation approach was introduced to infer whether the data quantity was adequate and to suggest the amount of data required for reliable predictions -- one of the often encountered but difficult to resolve issues in machine learning of materials science problems.