CrystalDiffusion

The maintenance of this project is handled within the following repository: https://github.com/PhysiLearn/CrystalDiffusion

Environment

You can run !pip install -r requirement.txt to install packages required.

Usage

npz2cloud.pyand npz2cloud.py in PCCD/preprocess is for data preprocessing.

run python train.py to train PCCD.

If you want use PCCD, you can run:

python train.py

You can also use it as follows:

from PCCD.Unet import Unet
from PCCD.DDPM import GaussianDiffusion
import torch
from PCCD.process import *
import numpy as np


sh = 1
image_classes = torch.Tensor([5]).to(torch.long).cuda()

iss0 = torch.Tensor().to(torch.long).cuda()
elem = torch.Tensor([[m['Ca'],m['Mg'],m['O']]]).to(torch.long).cuda()
el = ['Ca','Mg','O']

ee = torch.Tensor([1]).to(torch.long).cuda()


sampled_images = diffusion.sample(
    classes=image_classes,
    e=ee,
    iss=iss0,
    cond_scale=3.
)

data = sampled_images.to('cpu').detach().numpy()
process('./', sh, data, el)

Citation

@misc{denoising-diffusion-pytorch ,
    author  = {lucidrains},
    url     = {https://github.com/lucidrains/denoising-diffusion-pytorch}
}

@ARTICLE{2024arXiv240113192L,
       author = {{Li}, Zhelin and {Mrad}, Rami and {Jiao}, Runxian and {Huang}, Guan and {Shan}, Jun and {Chu}, Shibing and {Chen}, Yuanping},
        title = "{Generative Design of Crystal Structures by Point Cloud Representations and Diffusion Model}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Artificial Intelligence, Condensed Matter - Materials Science, Computer Science - Machine Learning, Physics - Computational Physics},
         year = 2024,
        month = jan,
          eid = {arXiv:2401.13192},
        pages = {arXiv:2401.13192},
          doi = {10.48550/arXiv.2401.13192},
archivePrefix = {arXiv},
       eprint = {2401.13192},
 primaryClass = {cs.AI},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv240113192L},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}