microgen3D

Dataset Summary

microgen3D is a dataset of 3D voxelized microstructures designed for training, evaluation, and benchmarking of generative models—especially Conditional Latent Diffusion Models (LDMs). It includes both synthetic (Cahn–Hilliard) and experimental microstructures with multiple phases (2 to 3). The voxel grids range from 64³ up to 128×128×64.

The dataset consists of three microstructure types:

• Experimental microstructures
• 2-phase Cahn–Hilliard microstructures
• 3-phase Cahn–Hilliard microstructures

The two Cahn–Hilliard datasets are thresholded versions of the same simulation source.
For each dataset type, we also provide pretrained generative model weights:

• vae.pt – Variational Autoencoder
• fp.pt – Feature Predictor
• ddpm.pt – Denoising Diffusion Probabilistic Model

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📂 Dataset Overview

File Name	Size	Description
CH_three_phase.tar.gz	~5.57 GB	Full three-phase Cahn–Hilliard dataset with 3D microstructures and morphological descriptors.
CH_two_phase.tar.gz	~4.37 GB	Full two-phase Cahn–Hilliard dataset with 3D microstructures and morphological descriptors.
experimental.tar.gz	~843 MB	Experimental microstructure dataset from real-world samples, voxelized for modeling.
sample_CH_three_phase.tar.gz	~12.2 MB	Small subset of the three-phase dataset for testing/demo purposes.
sample_CH_two_phase.tar.gz	~9.59 MB	Small subset of the two-phase dataset for testing/demo purposes.

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📊 Detailed Dataset Information

CH Two-Phase Dataset

• File: CH_two_phase.tar.gz
• Total Microstructures: 47,119
• Splits: 10 (Train: 9, Validation: 1)
• Microstructure Shape: (128, 128, 64)
• Attributes per Key: 34
• Example Attributes:
  • ABS_f_D: 0.391171
  • CT_f_D_tort1: 0.293271
  • phi: 0.556
  • chi: 2.33
  • source: direct/data_chi_2.330_phi_0.556_step_235.txt

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CH Three-Phase Dataset

• File: CH_three_phase.tar.gz
• Total Microstructures: 45,980
• Splits: 10 (Train: 9, Validation: 1)
• Microstructure Shape: (128, 128, 64)
• Attributes per Key: 13
• Example Attributes:
  • Interface_AM: 113702.0
  • Interface_DM: 96692.0
  • phi: 0.514
  • chi: 2.2
  • source: data_chi_2.200_phi_0.514.h5____172.txt

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Experimental Microstructure Dataset

• File: experimental.tar.gz
• Total Microstructures: 21,421
• Train Samples: 19,278
  Validation Samples: 2,143
• Microstructure Shape: (64, 64, 64)
• Attributes per Key: 23
• Example Attributes:
  • ABS_f_D: 0.591423
  • CT_f_D_tort1: 0.159534
  • source: /work/mech-ai-scratch/nirmal/generative_model_data/experimental/grid_cut/graspi/morphs/CB_120_260.txt

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Pretrained Weights (.pt)

We provide three pretrained weight packs aligned with the dataset families:

• vae.pt — Variational Autoencoder weights
• fp.pt — Feature Predictor weights
• ddpm.pt — Latent Diffusion Model weights

Model/Weights Summary

Pack	Input shape	VAE latent size	FP input (flattened)	FP output size (# predicted attrs)	Conditioning params	Manufacturing params	DDPM max features (n_feat)
CH 2-Phase	1,128,128,64	4,8,8,4	1024	7	3	0	512
CH 3-Phase	1,128,128,64	4,8,8,4	1024	7	4	3	512
Experimental	64,64,64	1,8,8,8	512	3	3	0	512

To learn more about the attributes and their meanings, see this link.

📁 Repository Structure

microgen3D/
├── data/
│   ├── experimental.tar.gz
│   ├── ch_2phase.tar.gz
│   ├── ch_3phase.tar.gz
│   ├── sample_CH_two_phase.tar.gz
│   ├── sample_CH_three_phase.tar.gz
│   ├── experimental/                 # after extracting experimental.tar.gz
│   │   ├── dataset_info.txt
│   │   ├── train.h5
│   │   ├── val.h5
│   │   └── sample_train.h5
│   ├── ch_2phase/                    # after extracting ch_2phase.tar.gz
│   │   ├── dataset_info.txt
│   │   ├── train/                    # training split (HDF5 shards/files)
│   │   └── val/                      # validation split
│   ├── ch_3phase/                    # after extracting ch_3phase.tar.gz
│   │   ├── dataset_info.txt
│   │   ├── train/
│   │   └── val/
│   ├── ch_2phase_sample/             # after extracting sample_CH_two_phase.tar.gz
│   │   ├── dataset_info.txt
│   │   ├── train/
│   │   └── val/
│   └── ch_3phase_sample/             # after extracting sample_CH_three_phase.tar.gz
│       ├── dataset_info.txt
│       ├── train/
│       └── val/
├── models/
│   └── weights/
│       ├── experimental/
│       │   ├── vae.pt
│       │   ├── fp.pt
│       │   └── ddpm.pt
│       ├── ch_2phase/
│       │   ├── vae.pt
│       │   ├── fp.pt
│       │   └── ddpm.pt
│       └── ch_3phase/
│           ├── vae.pt
│           ├── fp.pt
│           └── ddpm.pt
└── ...

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🚀 Quick Start

🔧 Setup Instructions

# 1. Clone the repo
git clone https://github.com/baskargroup/MicroGen3D.git
cd MicroGen3D

# 2. Set up environment
python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Download dataset and weights (Hugging Face)
# Make sure HF CLI is installed and you're logged in: `huggingface-cli login`

📥 Download Examples

Using Python

from huggingface_hub import hf_hub_download
import os

# Download sample dataset
hf_hub_download(
    repo_id="BGLab/microgen3D",
    filename="data/experimental.tar.gz",   # correct remote path
    repo_type="dataset",
    local_dir=""
)

# Download experimental pretrained weights

for fname in ["weights/experimental/vae.pt",
              "weights/experimental/fp.pt",
              "weights/experimental/ddpm.pt"]:
    hf_hub_download(
        repo_id="BGLab/microgen3D",
        filename=fname,                # correct remote path
        repo_type="dataset",
        local_dir=""
    )

📂 Extract Dataset

tar -xzvf data/experimental.tar.gz -C data/ 

🏋️ Training

For inference details refer to the GitHub repository README.

Navigate to the training folder and run:

cd training
python training.py

🧠 Inference

For inference details refer to the GitHub repository README.

After training, switch to the inference folder and run:

cd ../inference
python inference.py

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📜 Citation

If you use this dataset or models, please cite:

@article{baishnab2025microgen3d,
  title={3D Multiphase Heterogeneous Microstructure Generation Using Conditional Latent Diffusion Models},
  author={Baishnab, Nirmal and Herron, Ethan and Balu, Aditya and Sarkar, Soumik and Krishnamurthy, Adarsh and Ganapathysubramanian, Baskar},
  journal={arXiv preprint arXiv:2503.10711},
  year={2025}
}

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⚖️ License

This project is licensed under the MIT License.

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