---
title: "Materials Platform for Data Science (MPDS) Dataset"
description: "Highly curated inorganic materials dataset with a 30-year track record, built from about half a million scientific publications and powering several commercial products. It integrates…"
url: "https://unidata.pro/datasets/materials-platform-for-data-science/"
date_modified: "2025-12-19T15:00:50+03:00"
language: "en-US"
---
Highly curated inorganic materials dataset with a 30-year track record, built from about half a million scientific publications and powering several commercial products. It integrates data from 405,100 publications, linking 139,005 phase diagrams, 409,771 crystalline nanostructures, and 1,075,676 physical property sets into 189,682 materials phases, making it a comprehensive materials platform for data science and a valuable materials project database.

## Dataset Structure

### The Numbers Section

**Numbered list:**

- **Number:** 405,100 — **Text:** Publications
- **Number:** 139,005 — **Text:** Phase diagrams
- **Number:** 409,771 — **Text:** Crystalline nanostructures
- **Number:** 1,075,676 — **Text:** Property sets
- **Number:** 189,682 — **Text:** Material phases

### Tooltips Section

**Tooltip items:**

- **Name:** Materials Science
- **Name:** Chemistry
- **Name:** Physics
- **Name:** Data Science
- **Name:** Machine Learning

### Dataset Information

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Description | Inorganic materials with a 30-year history, powering academic research and commercial applications |
| Data types | Relational SQL database, JSON export |
| Tasks | Academic and industrial R&D, materials discovery, ML modeling |
| Labeling | 100+ categories: elements, formulas, properties, symmetry, etc. |
| Language | Controlled scientific English |

**Media Slider:** - **Image in the slider:** ![](https://unidata.pro/wp-content/uploads/2025/12/mpds-dataset-example.webp)

**Link to the sample:** [Download sample](https://drive.google.com/drive/folders/1p0cCz7roytA3WB1Nzh5eF_o2EP30FMP4)

### Technical Specifications

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Format | JSON, schema |
| Searchable fields | Physical properties, chemical elements, material classes, crystal system, formula, space group, etc. |

**Source and data collection methodology:** Source and collection methodology: Data were collected by a partner of Unidata (Manually curated by experts over 30 years)

### Dataset Use Cases - Slider

**Industry Cards:**

- **Industry:** Materials Science & Academic Research — **Title:** Unified Knowledge Base for Fundamental Studies — **Text:** The materials platform for data science supports fundamental materials science by linking phase diagrams, crystal structures, and physical properties into a single materials dataset. Researchers use this materials project database to analyze inorganic materials, compare similar materials, and validate hypotheses using standardized data drawn from peer-reviewed scientific research.
- **Industry:** Computational Materials Engineering — **Title:** Data-Driven Materials Design and Simulation — **Text:** Engineers apply MPDS as a materials dataset for computational materials workflows, where materials data feeds modeling, simulation, and property prediction tasks. The platform enables materials informatics studies by combining chemical structures, crystalline materials, and metadata, helping teams design new compounds and assess performance trends before physical experiments.
- **Industry:** Machine Learning & Materials Informatics — **Title:** Training Predictive Models on Materials Data — **Text:** MPDS is widely used for machine learning in materials science, providing high-quality datasets for training models that predict materials properties and stability. Its graph-scale structure and consistent data quality make it suitable for learning models, large-scale analytics platforms, and research projects focused on discovering patterns across vast materials databases.
- **Industry:** Industrial R&D and Applied Innovation — **Title:** Accelerating Industrial Materials Development — **Text:** Industrial teams rely on this materials platform for data science to shorten development cycles in chemistry, energy, and advanced manufacturing. By accessing curated materials data and published research in one repository, companies improve data management, reduce duplication, and support materials innovation with reliable inputs for engineering and product design.

### Fact

**FAQs Heading:** FAQs

**List of Questions:**

- **Question:** Can I request a sample of the MPDS dataset before purchasing? — **Answer:** Yes. Free samples are available so researchers can review data quality, schema structure, and searchable material properties. This allows evaluation of compatibility with analytics platforms, learning models, and scientific research workflows.
- **Question:** What types of annotations and metadata are provided? — **Answer:** The dataset contains over 100 standardized metadata categories, including elements, formulas, symmetry, space groups, and physical properties. These annotations enable structured querying, materials informatics analysis, and model training.
- **Question:** What data formats are available in the MPDS dataset? — **Answer:** Data is available as a relational SQL database with JSON exports and a defined schema.
- **Question:** What language and terminology does the dataset use? — **Answer:** The dataset uses controlled scientific English and standardized materials science terminology. This consistency improves interoperability with research software and reduces ambiguity during data analysis.
- **Question:** Why is this materials dataset valuable for AI-driven materials discovery? — **Answer:** The dataset combines 30 years of expert curation with information extracted from approximately 405,100 scientific publications, creating a reliable foundation for materials discovery, machine learning, and computational materials science. Its consistent structure and extensive coverage make it suitable for developing predictive AI models for inorganic materials.
- **Question:** How was the data collected and curated? — **Answer:** Data has been manually curated by domain experts from peer-reviewed publications for over 30 years. This expert-driven process ensures high data quality, consistency, and reliability for scientific research and engineering applications.
- **Question:** How are Unidata datasets licensed? — **Answer:** Unidata follows a dual-licensing model. Free samples are provided for testing and evaluation, while full datasets are available exclusively through purchase.
- **Question:** Do Unidata datasets comply with GDPR and data privacy regulations? — **Answer:** Yes. All datasets are curated in compliance with GDPR and applicable data protection laws. Data is sourced from legally permissible and ethical scientific publications.
- **Question:** How are Unidata datasets stored and managed? — **Answer:** All datasets are securely stored on AWS cloud infrastructure and managed according to ISO 27001 and ISO 27701 standards. This ensures secure access, scalability, and long-term reliability of materials databases.

## List of Parameters

- **Title:** 405,100 — **Description:** Publications
- **Title:** 189,682 — **Description:** Material phases
- **Title:** 1,075,676 — **Description:** Property sets

[Full list of this site's AI-readable pages](https://unidata.pro/llms.txt)