DeepCV is a computer code that implements our developed unsupervised machine learning called deep autoencoder neural network (DAENN) for learning low-dimensional collective variables (CVs) aka slow-mode reaction coordinates of a set of molecules for enhanced sampling.

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

1. Molecular features
   • Internal coordinates
   • SPRINT and eXtended SPRINT coordinates
2. Dense autoencoder neural nets
   • 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)
   • Variational autoencoder (future work)
5. Can interface with PLUMED and CP2K
6. Input file generator (GUI) for PLUMED and CP2K
7. Analysis tools
   • Feature importance
   • Sampling convergence assessment

• 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"

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 main.py daenn -i input_ae_DA.json

or

deepcv_daenn -i input_ae_DA.json

• Python 3.6 or a 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

To install all dependencies packages of DeepCV, you can follow either following way:

1. All at once (for the users)

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

2. Install packages separately (recommended for the developers)

   • NumPy >= 1.22.2
     • E.g., pip install --upgrade numpy==1.23.4
   • TensorFlow + Keras 2.10.0
     • pip install tensorflow (CPU)
     • conda install tensorflow-gpu (+ CUDA & cuDNN)
   • NVIDIA GPU and CUDA 10.1 (for GPU enabled)
     • https://developer.nvidia.com/cuda-toolkit-archive
   • cuDNN v7.6.4 (September 27, 2019), for CUDA 10.1
     • https://developer.nvidia.com/rdp/cudnn-archive
   • pydot (for keras.utils.vis_utils.plot_model)
     • conda install pydot
   • other important packages are listed in requirements.txt

3. DeepCV C++ makes use of JSON parser

   • https://github.com/nlohmann/json

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

1. Rangsiman Ketkaew ([email protected])
2. Sandra Luber ([email protected])