---
license: cc-by-nc-nd-4.0
task_categories:
- image-classification
- text-classification
tags:
- data science
- phase diagrams
- chemical structures
- crystal structures
- inorganic materials
- materials design
- machine learning
- chemistry
size_categories:
- 100K<n<1M
---
# Materials Platform for data science
Dataset includes **405,100** publications, **139,005** phase diagrams, **409,771** crystalline nanostructures, **1,075,676** physical property sets, and **189,682** material phases. It integrates decades of scientific research on **inorganic materials**, enabling computational **materials design**, **machine learning** applications, and materials informatics studies across industry and academia.

Built on data extracted from about half a million peer-reviewed scientific publications, it offers standardized data, detailed **chemical structures, crystal structures**, and extensive metadata on various materials.  - **[Get the data](https://unidata.pro/datasets/materials-platform-for-data-science/?utm_source=huggingface&utm_medium=referral&utm_campaign=materials-platform-for-data-science)**

The dataset helps researchers and engineers advance scientific discovery, predicting materials behavior, and accelerating materials innovation through data-driven research.

## 💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at [https://unidata.pro](https://unidata.pro/datasets/materials-platform-for-data-science/?utm_source=huggingface&utm_medium=referral&utm_campaign=materials-platform-for-data-science) to discuss your requirements and pricing options.

It allows researchers and engineers to explore computational chemistry, develop machine learning models for predicting materials behaviors. By combining raw data, experimental records, and computational analyses, MPDS helps scientists and materials experts design new compounds, identify similar materials, and optimize materials properties for engineering applications.

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