StructureCloud 0.0.1

Standard formating and easy access to 3D structural datasets for machine learning. currently under development...

StructureCloud

A collection of 3D Point cloud datasets commonly used for training generative modeling and molecular discovery.

Install

install hugging face dataset library

pip install datasets 

install StructureCloud (TODO: not yet on PyPi)

$ pip install StructureCloud

StructureCloud Datasets

StructureCloud is aimed at simplifying pointcloud dataset retrieval and manipulation with a simple unifying output format. The datasets in StructureCloud are chosen speifically for training models that adress problems related to 3D structures.

Usage

import numpy as np
import torch
from StructureCloud.Datasets import StructureCloudDataset as scd

# load numerical values as numpy objects
dataset_default = scd('dataset_name', split='train') 

# load numerical values as torch tensors
dataset_torch = scd('dataset_name', split='train', num_fmt=torch.tensor) 

#dataset output format is as follows
positions, features, unitcell, lables = dataset[index]

# output shapes:
# positions [N, 3] - positions in 3d space
# features [N, 1] or [N, d] - node feature/identities (ie element#, class, etc) 
# unitcell/bounding box [3,3]
# labels - a dictionary
# lables['node'] = { dict of nodewise lables : [N,d] } 							
# labels['object] = { dict of whole object labels : [d] }


#### selecting and formating data labels ###
## by default, the labels dictonary returns all additional information associated with the dataset.
## but a specific label can be selected by defining a formating function in 'label_fmt' 

get_object_target = lambda x : torch.tensor(x['object']['targets'])
get_object_smiles = lambda x : x['object']['SMILES']

qm9_regression = StructureCloudDataset('QM9', label_fmt = get_object_target)
qm9_smiles = StructureCloudDataset('QM9', label_fmt = get_object_smiles )

reg_target = qm9_regression[1000][3]
smiles = qm9_smiles[1000][3]
print(smiles)
print(reg_target)

Available Datasets

Small Organic Molecules

• QM9
• PCQM4Mv2
• GEOM

Proteins and biomolecules

• AlphaFold Homo Sapiens (proteins)
• (LP)PDBbind2020

Materials

• MP-20
• Perov-5
• Carbon-24
• MPTS-52
• PCOD2

3D objects

• coming soon...

Gaussian Splats

• coming soon..,