datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
science_materialsPartNeXt-Material-Properties
PartNeXt Material & Simulation-Ready Physical Properties
Per-part material class and simulation-ready physical properties $(\nu, E, \rho)$ for
333,152 parts across 23,221 textured 3D models of
PartNeXt (NeurIPS D&B 2025).
Every property value carries per-quantity provenance back to a cited measurement source,
and ships with metadata that Genesis and
Isaac Gym can consume directly.
PartNeXt itself ships no material or physical-property labels — these annotations are new.… See the full description on the dataset page: https://huggingface.co/datasets/minsj1225/PartNeXt-Material-Properties.Material_Selection_EvalA benchmark designed to facilitate evaluation and modify the behavior of a foundation model through different existing techniques in the context of material selection for conceptual design.
The data is collected by conducting a survey of experts in the field of material selection. The same questions mentioned in keyquestions.csv are asked to experts.
This can be used to evaluate a Language model performance and its spread compared to a human evaluation.
To get into a more detailed explanation… See the full description on the dataset page: https://huggingface.co/datasets/cmudrc/Material_Selection_Eval.material_productionDataset Summary
The task is to predict whether a protein sequence fails at the protein material stage.
Data Fields
seq: a string containing the protein sequence
label: an integer label indicating whether a protein sequence fails at the protein material stage.
Original Dataset Name: biomap-research/material_production
Original Author / Organization: Biomap
Original URL: https://huggingface.co/datasets/biomap-research/material_production
Original License: Apache License 2.0
No changes were made… See the full description on the dataset page: https://huggingface.co/datasets/AI4Protein/material_production.english-vocabulary-materials
English Vocabulary Teaching Materials（雅思与初高中词汇教学资料）
中学段的英语词汇教学资料：雅思分级词汇（预备班 / 一阶 / 二阶 / 三阶）的词汇本、词测本、配套听力录音与听说读讲义，外加初高中词表。
原始材料是 PDF、MP3 和 Excel —— 词表分散在 Excel 的多张工作表里，音频按中文文件名散落各目录，PDF 里的词测没法检索。这份仓库做了两件事：60 个原始文件原样归档不做改动，另外从 Excel 抽出 8257 条结构化词条存成 CSV/JSONL，可以直接读进来做背诵、默写、出题或全文检索。
⚠️ 版权提醒
这批材料整理自绿新的教研资料，不是原创数据集，著作权归原权利人所有。仓库采用 CC BY-NC-ND 4.0 并开启 gated access：禁止商业使用、再分发、公开镜像与演绎；研究用途允许，但须按第 7 节格式署名。完整条款见第 6 节。
1. 数据总览
指标
数值
清单内文件
85（另有… See the full description on the dataset page: https://huggingface.co/datasets/SwiftieJerry/english-vocabulary-materials.crystal-material-datasetMaterials
Materials Project Computed Properties Dataset
Welcome to the Dataset!
Dive into the fascinating world of materials science with the Materials Project Computed Properties Dataset! This comprehensive collection features computed properties for a vast array of materials, sourced from the renowned Materials Project database. Whether you’re a researcher exploring new materials, a data scientist building predictive models, or a student curious about how materials behave, this… See the full description on the dataset page: https://huggingface.co/datasets/AethronPhantom/Materials.materials-scale-regime-transfer-recognition-v01Scale and Regime Transfer Recognition v01
What this dataset is
This dataset evaluates whether a system can recognize when material behavior changes across scale or processing regime.
You give the model:
A material system
A characteristic length scale or geometry
Processing or fabrication context
An observed or claimed behavior
You ask one question.
Does this behavior still apply
or has the regime changed
Why this matters
Materials science fails quietly at scale transitions.
Common failure… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/materials-scale-regime-transfer-recognition-v01.subtractive_material_operations_evalThis dataset is designed for expert evaluation (GDPval) of multimodal models’ ability to understand and execute physical, subtractive material operations described in natural language.
The primary focus is on verbs such as:
engrave
carve
chisel
cut into material
These verbs imply material removal and geometric transformation, not merely visual styling.
Evaluation Goal
The goal of this dataset is to assess whether a model:
✅ correctly performs subtractive geometry
❌ or instead substitutes the… See the full description on the dataset page: https://huggingface.co/datasets/8Planetterraforming/subtractive_material_operations_eval.celltransformer_materialsmaterials-radiation-swelling-coherence-loss-v0.1What this dataset tests
Whether a system can detect
coherence loss between
radiation dose
void swelling
dimensional stability
before structural failure.
Failure is not driven by dose alone.
It begins when predictive relationships collapse.
Required outputs
radiation_coherence_score
swelling_divergence_rate
structural_risk_flag
service_horizon_cycles
critical_region
mitigation_strategy
Use case
Reactor materials
fusion materials
space radiation structures
Predict swelling-driven instability… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/materials-radiation-swelling-coherence-loss-v0.1.Material_Selection_EvalA benchmark designed to facilitate evaluation and modify the behavior of a foundation model through different existing techniques in the context of material selection for conceptual design.
The data is collected by conducting a survey of experts in the field of material selection. The same questions mentioned in keyquestions.csv are asked to experts.
This can be used to evaluate a Language model performance and its spread compared to a human evaluation.
To get into a more detailed explanation… See the full description on the dataset page: https://huggingface.co/datasets/Frederick001/Material_Selection_Eval.smart-material-stimulus-response-coherence-baseline-mapping-v0.1What this dataset tests
Can a model map the baseline coherence between a stimulus and a smart material’s structural response when the system is healthy?
The task is not to detect failure.
The task is to define the stable manifold before fatigue begins.
Domain
shape memory alloys
self-healing polymers
electrochromic materials
responsive composites
Required outputs
baseline_coherence_score
response_latency_profile
stimulus_response_gain
variability_index
expected_cycle_lifetime
Use
Establish the… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/smart-material-stimulus-response-coherence-baseline-mapping-v0.1.materials-passivation-layer-breakdown-mapping-v0.1What this dataset tests
Whether a system can detect
loss of coherence between
electrochemical impedance
surface micro-pH
pit nucleation signals
before visible corrosion failure.
Corrosion begins as relationship collapse
not visible damage.
Required outputs
passivation_coherence_score
pit_growth_acceleration
breakdown_risk_flag
time_to_pitting
critical_surface_zone
protective_action
Use case
Marine alloys
chemical plants
energy infrastructure
aerospace
Detect passivation collapse
before pitting… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/materials-passivation-layer-breakdown-mapping-v0.1.materials-interpretation-assumption-control-v01Interpretation and Assumption Control v01
What this dataset is
This dataset evaluates whether a system handles incomplete or ambiguous materials information without inventing structure.
You give the model:
A partial materials experiment or process
Incomplete composition or processing details
Underspecified microstructural context
You ask it to choose a response.
PROCEED
CLARIFY
REFUSE
The correct move is often to stop.
Why this matters
Materials science fails quietly through assumption.
Common… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/materials-interpretation-assumption-control-v01.smart-material-failure-horizon-intervention-routing-v0.1Goal
Forecast when a smart material stops functioning as a smart system.
Then route the best intervention.
Core idea
Early drift is useful only if it changes action.
This dataset tests whether a model can:
estimate remaining effective cycles
choose an intervention strategy
estimate recoverability vs replacement
Domains
shape memory alloys
self-healing polymers
electrochromic materials
Inputs
baseline and current coherence
drift rate and variance growth
response latency and amplitude
cycle… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/smart-material-failure-horizon-intervention-routing-v0.1.synthetic-construction-material-passports
🏗️ Synthetic Construction Material Passports
A synthetic dataset representing circular-economy material assets in metropolitan buildings.
Schema
synthetic_building_id: Unique building hash.
material_category: Concrete, Steel, Timber, Glass, etc.
estimated_quantity: Quantitative mass estimate.
central_estimate / upper_estimate: Uncertainty bounds.
composite-materials-qamaterials-ceramic-embrittlement-coherence-mapping-v0.1What this dataset tests
Whether a system can detect
loss of structural coherence
between oxidation growth
microcrack propagation
and residual strength
before brittle fracture occurs.
This is not temperature threshold detection.
It is relationship collapse detection.
Required outputs
embrittlement_coherence_score
crack_network_acceleration
fracture_risk_flag
integrity_horizon_cycles
critical_zone
stabilization_action
Use case
Ultra-high-temperature ceramics
turbine materials
re-entry vehicles… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/materials-ceramic-embrittlement-coherence-mapping-v0.1.Raw-material-consumption-per-1-ton-of-juicegeneral-materials-sciencesmart-material-coherence-drift-functional-fatigue-detection-v0.1Goal
Detect when a smart material starts losing function.
Core idea
Smart materials fail when stimulus and response stop coupling.
This dataset tests whether a model can detect that drift early.
Domains
shape memory alloys
self-healing polymers
electrochromic materials
Inputs
Healthy baseline signals plus evolving drift signals.
Required outputs
coherence_drift_rate
fatigue_onset_cycle
decoherence_type
functional_variance_growth
failure_probability
recommended_monitoring_action
Decoherence… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/smart-material-coherence-drift-functional-fatigue-detection-v0.1.materials-safety-classificationsmart_materials_strain_gauge_telemetryMaterials_FineTuningMaterials_Prompt