# Model-Free Cluster Analysis of Physical Property Data using Information Maximizing Self-Argument Training

## TL;DR

This paper introduces semi-supervised IMSAT, a versatile, model-free classification method that effectively classifies diverse physical property data without labeled examples, facilitating automation and AI-driven material research.

## Contribution

The paper presents a novel semi-supervised IMSAT approach capable of classifying different types of physical data without labeled training data.

## Key findings

- Successfully classifies XRD patterns and thermoelectric curves
- Operates with minimal additional information
- Accelerates automation in material data analysis

## Abstract

We present the semi-supervised IMSAT, a versatile classification method that works without labeled data and can be tuned by little additional information. We demonstrate how semi-supervised IMSAT can classify XRD patterns and thermoelectric hysteresis curves in the same way even though their shape and dimensions are different. Our algorithm will accelerate automation of big data collection and open a way to study artificial intelligent driven material development.