datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Japanese-Materials
仓库信息
电报地址：https://t.me/vomebook ，有问题请在：https://huggingface.co/datasets/VoiceOfML/Japanese-Materials/discussions 提出。
此仓库存储日共资料：https://huggingface.co/datasets/VoiceOfML/Japanese-Materials/tree/main 。
请使用：https://voiceofml-search.hf.space/Japanese-Materials 进行文件检索（备用搜索站：https://vomebook.github.io/search/#/Japanese-Materials ）。
可使用：https://voiceofml-search.hf.space/Japanese-Materials?wide=1 进行仓库内容查看（备用站：https://voiceofml-search.hf.space/Japanese-Materials?wide=1 ）。… See the full description on the dataset page: https://huggingface.co/datasets/VoiceOfML/Japanese-Materials.MaterialsSaddles
MaterialsSaddles
A high-throughput library of converged transition states for solid-state and
surface chemistry.
Hub URL: https://huggingface.co/datasets/SciLM/MaterialsSaddles
Released by SciLM.ai: https://www.scilm.ai
33,877,257 fully converged transition states computed by
massively-parallel saddle searches on top of public materials and catalysis
datasets, using the SaddleMill
package and Meta's uma-s-1p2
machine-learning interatomic potential.
Each entry in a file is a… See the full description on the dataset page: https://huggingface.co/datasets/SciLM/MaterialsSaddles.MPM-Verse-MaterialSim-Small
Dataset Card for MPMVerse Physics Simulation Dataset
Dataset Summary
This dataset contains Material-Point-Method (MPM) simulations for various materials, including water, sand, plasticine, elasticity, jelly, rigid collisions, and melting. Each material is represented as point-clouds that evolve over time. The dataset is designed for learning and predicting MPM-based physical simulations.
Supported Tasks and Leaderboards
The dataset supports tasks such as:… See the full description on the dataset page: https://huggingface.co/datasets/hrishivish23/MPM-Verse-MaterialSim-Small.chinese-materials-science-open-intelligence
🔬 Chinese Materials Science & Metallurgy Open Intelligence Dataset
Curated open intelligence dataset providing English research briefs, authoritative DOIs, executive summaries, and high-resolution micrographs of breakthrough Chinese scientific research in Materials Science, Metallurgy, Advanced Alloys, and Mining Engineering.
[!IMPORTANT]
Data Completeness & Research Authenticity Notice:
Included in this Hugging Face Open Dataset: English structured abstracts, core… See the full description on the dataset page: https://huggingface.co/datasets/simpleG2023/chinese-materials-science-open-intelligence.nexus-materialsontolearner-materials_science_and_engineering
Materials Science And Engineering Domain Ontologies
Overview
Materials Science and Engineering is a multidisciplinary domain that focuses on the study and application of materials, emphasizing their structure, properties, processing, and performance in engineering contexts. This field is pivotal for advancing knowledge representation, as it integrates principles from physics, chemistry, and engineering to innovate and optimize materials for diverse technological… See the full description on the dataset page: https://huggingface.co/datasets/SciKnowOrg/ontolearner-materials_science_and_engineering.Materials_Project
Cite this dataset Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., and Persson, K. A. Materials Project. ColabFit, 2023. https://doi.org/10.60732/4bf2e346
This dataset has been curated and formatted for the ColabFit Exchange
This dataset is also available on the ColabFit Exchange:
https://materials.colabfit.org/id/DS_pv1f3dlo5dsc_0
Visit the ColabFit Exchange to search… See the full description on the dataset page: https://huggingface.co/datasets/colabfit/Materials_Project.MPM-Verse-MaterialSim-Large
MPM-Verse-MaterialSim-Large
Dataset Summary
This dataset contains Material-Point-Method (MPM) simulations for various materials, including water, sand, plasticine, and jelly.
Each material is represented as point-clouds that evolve over time. The dataset is designed for learning and predicting MPM-based
physical simulations. The dataset is rendered using five geometric models - Stanford-bunny, Spot, Dragon, Armadillo, and Blub.
Each setting has 10 trajectories per… See the full description on the dataset page: https://huggingface.co/datasets/hrishivish23/MPM-Verse-MaterialSim-Large.verbosity-materials
Verbosity Research Materials
公开研究材料库：7篇论文、8个任务（4个website、4个slides）、40份作品（8份作者作品、32份AI作品）。
开始阅读 / Start here
下载完整 v1 ZIP（约288 MiB）
浏览展开目录
中文入门及离线限制
作品与模型来源清单
论文及作者原始链接
解压后打开 verbosity-materials-v1/index.html 浏览。HF文件仓库不作为这些HTML的运行网站。
Slides包含20份PDF，AI作品另附可编辑HTML和本地资源；作者slides仅有PDF。
论文正文是生成时使用的快照，完整论文通过原始链接访问。
Provenance and use
The v1 snapshot comes from run materials-share-20260918a. All 40 artifacts retain
provenance; the bundle includes SHA-256… See the full description on the dataset page: https://huggingface.co/datasets/Penguin-N/verbosity-materials.MofasaDB
MofasaDB
The MofasaDB is a publicly available dataset containing 200.000+ de novo generated MOF (Metal-Organic Framework) structures from Mofasa trained on QMOF (up to 170 atoms), along with their geometry-relaxed counterparts. The database is released alongside the paper Mofasa: A Step Change in Metal-Organic Framework Generation. A user-friendly web interface for search and discovery can be accessed at https://mofux.ai/.
Database Overview
The database contains… See the full description on the dataset page: https://huggingface.co/datasets/Orbital-Materials/MofasaDB.materials-project
Dataset Card for xpanceo-team/materials-project
Dataset Summary
This dataset is a snapshot of the Materials Project (MP) materials database, exported from the MP Summary endpoint using mp-api.
Snapshot date: 2026-01-18.
The structure column stores pymatgen Structure JSON serialized as a string.
Important: For several rich/large MP sub-documents (e.g. XAS, DOS, band structure), this dataset stores only availability flags (*_is_available) rather than the full objects.… See the full description on the dataset page: https://huggingface.co/datasets/xpanceo-team/materials-project.quantum-simulation-chemistry-materials
Neura Parse — Quantum Simulation of Chemistry & Materials: Encodings, VQE/QPE & Dynamics
An application-deep, code-backed vertical on simulating quantum matter: electronic-structure problems, fermion-to-qubit encodings, Hamiltonian factorizations, ground/excited-state and real-time-dynamics algorithms, and analog simulation, with end-to-end resource estimates and honest classical-competitor accounting. Built with Qiskit Nature, OpenFermion, PennyLane-QChem, and PySCF — far… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-simulation-chemistry-materials.science_materialsmaterials-project
Dataset
Materials project (2019 dump)
This dataset contains 133420 materials with formation energy per atom.
Processed from mp.2019.04.01.json
Download
Download link: materials-project.tar.gz
MD5 checksum c132f3781f32cd17f3a92aa6501b9531
Content
Bundled in materials-project.tar.gz.
Index (index.json)
list of dict:
index (int) => index of the structure in data file.
id (str) => id of Materials Project.
formula (str) => formula.
natoms… See the full description on the dataset page: https://huggingface.co/datasets/materials-toolkits/materials-project.hf_policy_materialsMaterials-AutoLab-144
Materials AutoLab 144
Materials AutoLab 144 evaluates whether an Agent can turn a materials
experiment objective into a grounded action plan, operate through a public OPC
contract, respond to observable state and applicability feedback, and leave a
complete evidence bundle.
The 144 tasks use the frozen Mixer natural-v4 r4 question set. Each task gives
the Agent a scientific objective, initial public state, capability and action
catalogs, an OPC interface contract, output schemas… See the full description on the dataset page: https://huggingface.co/datasets/MatMaster-DP/Materials-AutoLab-144.GenText-Forensics_third_place_additional_materials
GenText-Forensics 2026 — Third-Place Additional Materials (Team MSU)
Model weights, code, and reproduction artifacts for Team MSU's third-place
solution to the ACM MM 2026 GenText-Forensics challenge
(Codabench).
The method is a decomposed chain-of-thought pipeline for detecting,
localizing, typing, and explaining forgeries in multilingual document text
images:
DTD (Document Tampering Detector) — an external pixel-level visual
tampering detector that produces a tampering… See the full description on the dataset page: https://huggingface.co/datasets/cmcshnik/GenText-Forensics_third_place_additional_materials.apple-dms-materials
Apple Dense Material Segmentation (DMS) Dataset
A pixel-level material segmentation dataset containing ~41K images with dense annotations across 57 material categories. Originally released by Apple as part of the Dense Material Segmentation (DMS) research project.
Note: This is a mirror prepared for direct use with the HuggingFace 🤗 datasets library. The source images originate from Open Images V7, and material annotations were created by Apple. Some images (~6%) from the original… See the full description on the dataset page: https://huggingface.co/datasets/AllanK24/apple-dms-materials.pes2o_materials_science_780m_refinedweb_220m_before_20190101sofc_materials_articlesThe SOFC-Exp corpus consists of 45 open-access scholarly articles annotated by domain experts.
A corpus and an inter-annotator agreement study demonstrate the complexity of the suggested
named entity recognition and slot filling tasks as well as high annotation quality is presented
in the accompanying paper.open-materials-guide-0210-embeddingsapple-dms-materials-v2
Apple Dense Material Segmentation (DMS) – Stratified 80/10/10 Split
A pixel-level material segmentation dataset containing ~41K images with dense annotations across 57 material categories. Originally released by Apple as part of the Dense Material Segmentation (DMS) research project.
This version uses a custom stratified 80/10/10 split (vs Apple's original 54/23/23) to maximise training data while maintaining representative validation and test sets.
Why a Custom Split?… See the full description on the dataset page: https://huggingface.co/datasets/AllanK24/apple-dms-materials-v2.GPT-Narratives-for-Materials
1.5 million materials narratives generated by chatbots
Dataset containing synthetically generated (by GPT-3.5) language-materials narratives.There is a significant bias in the materials studied and mentioned in the literature (Figure a). By utilizing uniform elemental distribution of materials from an open database (Figure b), we can overcome this bias and teach the language model a diverse range of knowledge (Figure c).
Details described in the following paper:… See the full description on the dataset page: https://huggingface.co/datasets/yjeong/GPT-Narratives-for-Materials.pbr-materials
PBR Materials (MonoRelief V2 core)
Auto-generated PBR material set (10 materials). Each subfolder contains a complete PBR set:
Map
File
Format
Albedo
*_albedo.png
8-bit RGB
Height
*_height.png
16-bit grayscale
Normal
*_normal.png
8-bit RGB (OpenGL, Godot 4)
Roughness
*_roughness.png
8-bit grayscale
Metallic
*_metallic.png
8-bit grayscale
Pipeline
Generated using MonoRelief V2 (Fig. 7), Marigold-only variant:
Base depth — Marigold Depth… See the full description on the dataset page: https://huggingface.co/datasets/Zakhar1746/pbr-materials.ai4sci-atomistic-materials
Crystal Potential Learning
One repository for the training data, starting model, trained checkpoints, physical
evaluation inputs/reference labels and measured results for
AI4Sci task PR #49.
The task learns one energy/force/stress potential and evaluates crystal stability,
phonons and elasticity. It complements CrystalGen's structure-generation task.
Contents
Directory
Contents
Role
training/
500,000 MPTrj+sAlex training frames; 5,000 labeled-dev frames… See the full description on the dataset page: https://huggingface.co/datasets/junlinw/ai4sci-atomistic-materials.Korea-Materialsmaterials-bandgap-prediction
Materials Science Bandgap Prediction Dataset
Dataset for predicting bandgap (eV) from chemical formula. Source: Materials Project.
Citation
@article{venugopal2026probing,
title={Probing Materials Intelligence in LLMs: From Latent Embeddings to Reliable Predictions},
author={Venugopal, Vineeth and Mahjoubi, Soroush and Olivetti, Elsa},
journal={arXiv preprint arXiv:2603.01834},
year={2026}
}
materials-figure-qa
Materials Figure QA
This repository contains 250 figure-grounded multimodal question-answer pairs from recent materials-related arXiv papers. Each item requires interpreting a rendered figure and applying materials-science reasoning. Figures with aspect ratio greater than 4:1 were removed; retained figures were downsampled so their longest dimension is at most 2048 pixels.
The filtered split preserves the original deterministic split:
validation: 128 examples
test: 122 examples… See the full description on the dataset page: https://huggingface.co/datasets/gneubig/materials-figure-qa.lematerial
Datasets curated from LeMaterial/LeMatBulkUnique - unique_pbe version 1.1
This repository is aimed to contain a curated version of the PBE calculated part of LeMaterial dataset created by Entalpic
and available at "https://huggingface.co/datasets/LeMaterial/LeMat-BulkUnique" as "unique_pbe".
The curation was done using Pymatgen version 2024.10.3.
The curated dataset is given directly in CIF format for easy readability by usual crystal data reader softwares and python packages.
The… See the full description on the dataset page: https://huggingface.co/datasets/materials-toolkits/lematerial.synthetic-superconductor-materials-dataset
synthetic-superconductor-materials-dataset
Synthetic Q&A dataset on Superconductor Materials, generated with SDGS (Synthetic Dataset Generation Suite).
Dataset Details
Metric
Value
Topic
Superconductor Materials
Total Q&A Pairs
2649
Valid Pairs
2649
Provider/Model
ollama/gpt-oss:120b
Sources
This dataset was generated from 170 scholarly papers:
#
Title
Authors
Year
Source
QA Pairs
1
Observation of a large-gap… See the full description on the dataset page: https://huggingface.co/datasets/Kylan12/synthetic-superconductor-materials-dataset.