Deep Learning for Collective Variables (DeepCV)

DeepCV is a tool to learn collective variables (CVs) of a chemical process for enhanced sampling simulation, using deep autoencoder neural network (DAENN) model.

Website: https://lubergroup.pages.uzh.ch/deepcv/

Main Features

1. Molecular features
   • Internal coordinates
   • SPRINT and eXtended SPRINT coordinates
2. Autoencoder (non-iterative approach)
   • Single and multi-input simple and stacked autoencoder
   • Avoid saturation
   • GPU acceleration
3. Learn CVs in expanded configurational space
   • Customized loss functions with minimaxation technique
     • Primary loss: main loss with regularization
     • Secondary loss: Additional loss to be maximized to expand the CV space
   • Self-directed expansion of configurational space
4. Generative model for generating data
   • Generative adversarial networks (GANs)
5. Can interface with PLUMED and CP2K
6. Input file generator (GUI) for PLUMED and CP2K
7. Analysis tools
   • Feature importance
   • Sampling convergence assessment

Quick installation

• Python codes

  cd deepcv/
  pip install -r requirements.txt
  pip install .

• C++ codes

  cd deepcv/
  export LIB_TF=/usr/local/tensorflow/
  
  g++ -Wall -fPIC -o deepcv.o deepcv.cpp \
      -O3 -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14 -fPIC \
      -I${LIB_TF}/include/ -L${LIB_TF}/lib \
      -ltensorflow -ltensorflow_cc -ltensorflow_framework
  
  g++ -shared -fPIC -o deepcv.so deepcv.o

  or you can use make

  make CXXFLAGS="-std=c++14 -fPIC"

Usage

The following is an example command for training model of CVs for Diels-Alder reaction using features extracted from reactant trajectory. You can call DeepCV's DAENN via either main.py API or deepcv_daenn command (register entrypoint).

python deepcv/src/main.py daenn -i input_ae_DA.json

or

deepcv_daenn -i input_ae_DA.json

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Development

• Python 3.10 or a newer version
• TensorFlow 2.16 and Keras 3 or newer version
• Use git control: git clone https://gitlab.uzh.ch/lubergroup/deepcv.git
• Please write function docstring and comment for difficult-to-understand code
• Document modules and packages you are developing
• Format codes with Black
• Send pull-request to master with an explanation, for example, what you contribute, how it works, and usefulness

Packages requirements

To install all dependencies packages of DeepCV, use conda or pip:

• Conda (recommended)
  • conda update --all -y
  • conda install --file requirements.txt
• pip
  • pip install --upgrade pip
  • pip install -r requirements.txt

Install packages separately (recommended for the developers)

• All packages are listed in requirements.txt
• DeepCV C++ makes use of JSON parser: https://github.com/nlohmann/json

In Progress

1. Variational autoencoder
2. Improve neural network algorithm for large systems e.g. metal-oxide surface
3. Improve code compatibility between TensorFlow, PLUMED, and CP2K

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Authors

1. Rangsiman Ketkaew (rangsiman.ketkaew@chem.uzh.ch)
2. Sandra Luber (sandra.luber@chem.uzh.ch)