DeepStruc is a Conditional Variational Autoencoder which can predict the mono-metallic nanoparticle from a Pair Distribution Function.

Overview

ChemRxiv | [Paper] XXX

DeepStruc

Welcome to DeepStruc, a Deep Generative Model (DGM) that learns the relation between PDF and atomic structure and thereby solves a structure from a PDF!

  1. DeepStruc
  2. Getting started (with Colab)
  3. Getting started (own computer)
    1. Install requirements
    2. Simulate data
    3. Train model
    4. Predict
  4. Author
  5. Cite
  6. Acknowledgments
  7. License

We here apply DeepStruc for the structural analysis of a model system of mono-metallic nanoparticle (MMNPs) with seven different structure types and demonstrate the method for both simulated and experimental PDFs. DeepStruc can reconstruct simulated data with an average mean absolute error (MAE) of the atom xyz-coordinates on 0.093 ± 0.058 Å after fitting a contraction/extraction factor, an ADP and a scale parameter. We demonstrate the generative capability of DeepStruc on a dataset of face-centered cubic (fcc), hexagonal closed packed (hcp) and stacking faulted structures, where DeepStruc can recognize the stacking faulted structures as an interpolation between fcc and hcp and construct new structural models based on a PDF. The MAE is in this example 0.030 ± 0.019 Å.

The MMNPs are provided as a graph-based input to the encoder of DeepStruc. We compare DeepStruc with a similar DGM without the graph-based encoder. DeepStruc is able to reconstruct the structures using a smaller dimension of the latent space thus having a better generative capabillity. We also compare DeepStruc with a brute-force modelling approach and a tree-based classification algorithm. The ML models are significantly faster than the brute-force approach, but DeepStruc can furthermore create a latent space from where synthetic structures can be sampled which the tree-based method cannot! The baseline models can be found in other repositories: brute-force, MetalFinder and CVAE. alt text

Getting started (with Colab)

Using DeepStruc on your own PDFs is straightforward and does not require anything installed or downloaded to your computer. Follow the instructions in our Colab notebook and try to play around.

Getting started (own computer)

Follow these step if you want to train DeepStruc and predict with DeepStruc locally on your own computer.

Install requirements

See the install folder.

Simulate data

See the data folder.

Train model

To train your own DeepStruc model simply run:

python train.py

A list of possible arguments or run the '--help' argument for additional information.
If you are intersted in changing the architecture of the model go to train.py and change the model_arch dictionary.

Arg Description Example
-h or --help Prints help message.
-d or --data_dir Directory containing graph training, validation and test data. str -d ./data/graphs
-s or --save_dir Directory where models will be saved. This is also used for loading a learner. str -s bst_model
-r or --resume_model If 'True' the save_dir model is loaded and training is continued. bool -r True
-e or --epochs Number of maximum epochs. int -e 100
-b or --batch_size Number of graphs in each batch. int -b 20
-l or --learning_rate Learning rate. float -l 1e-4
-B or --beta Initial beta value for scaling KLD. float -B 0.1
-i or --beta_increase Increments of beta when the threshold is met. float -i 0.1
-x or --beta_max Highst value beta can increase to. float -x 5
-t or --reconstruction_th Reconstruction threshold required before beta is increased. float -t 0.001
-n or --num_files Total number of files loaded. Files will be split 60/20/20. If 'None' then all files are loaded. int -n 500
-c or --compute Train model on CPU or GPU. Choices: 'cpu', 'gpu16', 'gpu32' and 'gpu64'. str -c gpu32
-L or --latent_dim Number of latent space dimensions. int -L 3

Predict

To predict a MMNP using DeepStruc or your own model on a PDF:

python predict.py

A list of possible arguments or run the '--help' argument for additional information.

Arg Description Example
-h or --help Prints help message.
-d or --data Path to data or data directory. If pointing to data directory all datasets must have same format. str -d data/experimental_PDFs/JQ_S1.gr
-m or --model Path to model. If 'None' GUI will open. str -m ./models/DeepStruc
-n or --num_samples Number of samples/structures generated for each unique PDF. int -n 10
-s or --sigma Sample to '-s' sigma in the normal distribution. float -s 7
-p or --plot_sampling Plots sampled structures on top of DeepStruc training data. Model must be DeepStruc. bool -p True
-g or --save_path Path to directory where predictions will be saved. bool -g ./best_preds
-i or --index_plot Highlights specific reconstruction in the latent space. --data must be specific file and not directory and '--plot True'. int -i 4
-P or --plot_data If True then the first loaded PDF is plotted and shown after normalization. bool -P ./best_preds

Authors

Andy S. Anker1
Emil T. S. Kjær1
Marcus N. Weng1
Simon J. L. Billinge2, 3
Raghavendra Selvan4, 5
Kirsten M. Ø. Jensen1

1 Department of Chemistry and Nano-Science Center, University of Copenhagen, 2100 Copenhagen Ø, Denmark.
2 Department of Applied Physics and Applied Mathematics Science, Columbia University, New York, NY 10027, USA.
3 Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory, Upton, NY 11973, USA.
4 Department of Computer Science, University of Copenhagen, 2100 Copenhagen Ø, Denmark.
5 Department of Neuroscience, University of Copenhagen, 2200, Copenhagen N.

Should there be any question, desired improvement or bugs please contact us on GitHub or through email: [email protected] or [email protected].

Cite

If you use our code or our results, please consider citing our papers. Thanks in advance!

@article{kjær2022DeepStruc,
title={DeepStruc: Towards structure solution from pair distribution function data using deep generative models},
author={Emil T. S. Kjær, Andy S. Anker, Marcus N. Weng, Simon J. L. Billinge, Raghavendra Selvan, Kirsten M. Ø. Jensen},
year={2022}}
@article{anker2020characterising,
title={Characterising the atomic structure of mono-metallic nanoparticles from x-ray scattering data using conditional generative models},
author={Anker, Andy Sode and Kjær, Emil TS and Dam, Erik B and Billinge, Simon JL and Jensen, Kirsten MØ and Selvan, Raghavendra},
year={2020}}

Acknowledgments

Our code is developed based on the the following publication:

@article{anker2020characterising,
title={Characterising the atomic structure of mono-metallic nanoparticles from x-ray scattering data using conditional generative models},
author={Anker, Andy Sode and Kjær, Emil TS and Dam, Erik B and Billinge, Simon JL and Jensen, Kirsten MØ and Selvan, Raghavendra},
year={2020}}

License

This project is licensed under the Apache License Version 2.0, January 2004 - see the LICENSE file for details.

Owner
Emil Thyge Skaaning Kjær
Ph.D student in nanoscience at the University of Copenhagen.
Emil Thyge Skaaning Kjær
Classifies galaxy morphology with Bayesian CNN

Zoobot Zoobot classifies galaxy morphology with deep learning. This code will let you: Reproduce and improve the Galaxy Zoo DECaLS automated classific

Mike Walmsley 39 Dec 20, 2022
A high-performance anchor-free YOLO. Exceeding yolov3~v5 with ONNX, TensorRT, NCNN, and Openvino supported.

YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. For more details, please refer to our rep

7.7k Jan 06, 2023
A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model

This repository contains the similarity metrics designed and evaluated in the paper, and instructions and code to re-run the experiments. Implementation in the deep-learning framework PyTorch

Steffen 86 Dec 27, 2022
Active window border replacement for window managers.

xborder Active window border replacement for window managers. Usage git clone https://github.com/deter0/xborder cd xborder chmod +x xborders ./xborder

deter 250 Dec 30, 2022
4D Human Body Capture from Egocentric Video via 3D Scene Grounding

4D Human Body Capture from Egocentric Video via 3D Scene Grounding [Project] [Paper] Installation: Our method requires the same dependencies as SMPLif

Miao Liu 37 Nov 08, 2022
Minimal PyTorch implementation of Generative Latent Optimization from the paper "Optimizing the Latent Space of Generative Networks"

Minimal PyTorch implementation of Generative Latent Optimization This is a reimplementation of the paper Piotr Bojanowski, Armand Joulin, David Lopez-

Thomas Neumann 117 Nov 27, 2022
Deepfake Scanner by Deepware.

Deepware Scanner (CLI) This repository contains the command-line deepfake scanner tool with the pre-trained models that are currently used at deepware

deepware 110 Jan 02, 2023
Implementation of the paper "Language-agnostic representation learning of source code from structure and context".

Code Transformer This is an official PyTorch implementation of the CodeTransformer model proposed in: D. Zügner, T. Kirschstein, M. Catasta, J. Leskov

Daniel Zügner 131 Dec 13, 2022
GPT, but made only out of gMLPs

GPT - gMLP This repository will attempt to crack long context autoregressive language modeling (GPT) using variations of gMLPs. Specifically, it will

Phil Wang 80 Dec 01, 2022
An updated version of virtual model making

Model-Swap-Face v2   这个项目是基于stylegan2 pSp制作的,比v1版本Model-Swap-Face在推理速度和图像质量上有一定提升。主要的功能是将虚拟模特进行环球不同区域的风格转换,目前转换器提供西欧模特、东亚模特和北非模特三种主流的风格样式,可帮我们实现生产资料零成

seeprettyface.com 62 Dec 09, 2022
Unbalanced Feature Transport for Exemplar-based Image Translation (CVPR 2021)

UNITE and UNITE+ Unbalanced Feature Transport for Exemplar-based Image Translation (CVPR 2021) Unbalanced Intrinsic Feature Transport for Exemplar-bas

Fangneng Zhan 183 Nov 09, 2022
Collective Multi-type Entity Alignment Between Knowledge Graphs (WWW'20)

CG-MuAlign A reference implementation for "Collective Multi-type Entity Alignment Between Knowledge Graphs", published in WWW 2020. If you find our pa

Bran Zhu 28 Dec 11, 2022
PySOT - SenseTime Research platform for single object tracking, implementing algorithms like SiamRPN and SiamMask.

PySOT is a software system designed by SenseTime Video Intelligence Research team. It implements state-of-the-art single object tracking algorit

STVIR 4.1k Dec 29, 2022
EMNLP 2021 paper The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers.

Codebase for training transformers on systematic generalization datasets. The official repository for our EMNLP 2021 paper The Devil is in the Detail:

Csordás Róbert 57 Nov 21, 2022
Generative Modelling of BRDF Textures from Flash Images [SIGGRAPH Asia, 2021]

Neural Material Official code repository for the paper: Generative Modelling of BRDF Textures from Flash Images [SIGGRAPH Asia, 2021] Henzler, Deschai

Philipp Henzler 80 Dec 20, 2022
Official pytorch implementation of "DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion"

DSPoint Official implementation of "DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion". Paper link: https://arxiv.org/abs/2111.10

Ziyao Zeng 14 Feb 26, 2022
Mix3D: Out-of-Context Data Augmentation for 3D Scenes (3DV 2021)

Mix3D: Out-of-Context Data Augmentation for 3D Scenes (3DV 2021) Alexey Nekrasov*, Jonas Schult*, Or Litany, Bastian Leibe, Francis Engelmann Mix3D is

Alexey Nekrasov 189 Dec 26, 2022
Network Compression via Central Filter

Network Compression via Central Filter Environments The code has been tested in the following environments: Python 3.8 PyTorch 1.8.1 cuda 10.2 torchsu

2 May 12, 2022
Official Implementation for HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing

HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing Yuval Alaluf*, Omer Tov*, Ron Mokady, Rinon Gal, Amit H. Bermano *Denotes equ

885 Jan 06, 2023
Python library for computer vision labeling tasks. The core functionality is to translate bounding box annotations between different formats-for example, from coco to yolo.

PyLabel pip install pylabel PyLabel is a Python package to help you prepare image datasets for computer vision models including PyTorch and YOLOv5. I

PyLabel Project 176 Jan 01, 2023