deepmodeling · amcadmus · May 25, 2021 · Feb 2, 2021 · Feb 2, 2021 · May 12, 2021
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+# Rid-kit
+
+## Introduction
+
+Rid-kit is a python package for enhanced sampling via RiD(Reinforced Dynamics) method.
+
+## Installation
+
+#### Environment installation
+
+1, Install python and tensorflow (version<=1.15)
+
+2, Install tensorflow's C++ interface
+The tensorflow's C++ interface will be compiled from the source code, can be found [here](https://github.com/deepmodeling/deepmd-kit/blob/master/doc/install-tf.1.8.md).
+
+3, Install plumed2.5.2
+
+```bash
+tar -xvzf plumed-2.5.2.tgz
+cp DeePFE.cpp plumed-2.5.2/src/bias
+tf_path=$tensorflow_root
+CXXFLAGS="-std=gnu++11 -I $tf_path/include/" LDFLAGS=" -L$tf_path/lib -ltensorflow_framework -ltensorflow_cc -Wl,-rpath,$tf_path/lib/" ./configure --prefix=/software/plumed252 CC=mpicc CXX=mpicxx
+```
+Set the bashrc
+```bash
+source /software/plumed-2.5.2/sourceme.sh
+export PLUMED2_HOME=/software/plumed252
+export PATH=$PLUMED2_HOME/bin:$PATH
+export LD_LIBRARY_PATH=$PLUMED2_HOME/lib:$LD_LIBRARY_PATH
+export PKG_CONFIG_PATH=$PLUMED2_HOME/pkgconfig:$PKG_CONFIG_PATH
+export PLUMED_VIMPATH=$PLUMED2_HOME/vim:$PLUMED_VIMPATH
+export INCLUDE=$PLUMED2_HOME/include:$INCLUDE
+export PLUMED_KERNEL=/home/dongdong/software/plumed252/lib/libplumedKernel.so
+```
+
+4, Install gromacs 2019.2
+
+```bash
+tar -xzvf gromacs-2019.2.tar.gz
+cd gromacs-2019.2
+plumed patch -p
+mkdir build
+cd build
+/software/cmake312/bin/cmake .. -DCMAKE_INSTALL_PREFIX=/software/GMX20192plumed -DGMX_BUILD_OWN_FFTW=ON -DGMX_GPU=on -DGMX_SIMD=avx_256 -DGMX_PREFER_STATIC_LIBS=ON -DBUILD_SHARED_LIBS=OFF -DGMX_EXTERNAL_BLAS=off
+make -j 4
+make install
+```
+Set the bashrc
+```bash
+source /software/GMX20192plumed/bin/GMXRC.bash
+```
+
+5, Install dpdispatcher
+```
+git clone https://github.com/deepmodeling/dpdispatcher.git
+cd dpdispatcher
+python setup.py install
+```
+dpdispatcher is a tool for job submitting.
+
+
+Now you have all dependence of RiD (Gromacs, Tensorflow and a conda environment).
+~~~bash
+cd rit-kit
+python setup.py install
+~~~
+Open python, try `import rid`.
+
+Installation finishs successfully if you get no error.
+
+## Quick Start
+We offer a simple but complete example in `rid-kit/examples`
+
+Try:
+```bash
+cd examples
+python main.py rid.json -c cv.json -s machine.json -i ./mol -o ./test_examples 
+```
+
+To begin with, you should offer a rid parameters file(rid.json), a CV file(cv.json), a machine configuration file(machine.json) and a folder(mol/) containing initial conformation files in detail, and the number of conformation files should be equal to the number of walkers for parallel.
+
+All these files are presented in `examples` folder where the users can adjust the parameter as their will.
+
+The process of running will be recorded in `(work_path)/recoord.txt` in which its iteration index and task index will be written after finishing every task. If you want to rerun the process and make sure that a record file exists in the work path, the program will restart from the next one of the end of the record(just use the same command to resatrt). If a task was restarted, but a working folder (which this task should generate) has already existed, this existed folder will be backed off as `folder_name.bk00`. That is, you can restart the process at any individual task node through modifying the recording file.
+
+However, if there is NOT a record file in the working path, the whole process will restrat at the very beginning. The old one will become a back-up folder as `old_folder.bk000`.
+
+### CV selection
+In this version, the user can choose the dihedral angles as CVs. rid-kit will remove the dihedral angles of the end of the proteins automatically. In the CV file(`cv.json`), the user can write the index of the selected residues, the two dihedral angle ($\psi$ and $\phi$) will be both setted as the CV. 
+
+Plumed will output all selected angles in every md process, the user can find them in `work_path/iter.0000xx/00.enhcMD/00x/plm.out`, file `angle.rad.out` in the same path is a copy but removing the frame index.
+
+We will add more features for users to select more different (and customed) CVs.
+
+## Main procedure of RiD
+
+RiD will run in iterations. Every iteration contains tasks below:
+
+1. Biased MD;
+2. Restrained MD;
+3. Training neuro network.
+
+#### a. Biased MD
+
+Just like Metadynamics, RiD will sample based on a bias potential given by NN models. A uncertainty indicator will direct the process of adding bias potential.
+
+#### b. Restrained MD
+
+This procedure will calculate mean force based on the sampling results, which can generate data set for training. 
+
+#### c. Neuro network training
+
+A fully connected NN will be trained via sampling data. This network will generate a map from selected CV to free energy.
+
+A more detail description of RiD is published now, please see:
+
+>  J. Chem. Phys. **148**, 124113 (2018); https://doi.org/10.1063/1.5019675
+
+
+
+ # RiD settings
+
+
+
+Two necessary json files are required to get start a RiD procedure.
+
+1. rid.json for configuration of simulation.
+2. cv.json for specifying CV.
+
+### rid.json
+
+**General setting**
+
+| Parameters | Type | Description | Default/Example |
+| :----: | :----: | :----: | :----: |
+| gmx_prep | str | Gromacs preparation command | gmx grompp -maxwarn 1 |
+| gmx_run | str | Gromacs md run command | gmx mdrun -ntmpi 1 |
+| init_graph | list&str | initial graph files list | [] |
+| numb_iter | int | number of iterations | 3 |
+| numb_walkers | int | number of walkers | 2 |
+| bf_traj_stride | int | brute force trajectory stride | 500 |
+
+**Setting for biased MD**
+
+| Parameters | Type | Description | Default/Example |
+| :----: | :----: | :----: | :----: |
+| bias_trust_lvl_1 | int | trust upper lecel | 2 |
+| bias_trust_lvl_2 | int | trust lower level | 3 |
+| bias_nsteps | int | total number of steps of biased MD | 20000 |
+| bias_frame_freq | int | frame frequency for recording | 20 |
+| sel_threshold | float/int | initial threshold for selection | 2 |
+| cluster_threshold | float | * | 1.5 |
+| num_of_cluster_threshhold | int | minimum of cluster number | 8 |
+| max_sel | int | maximum of selection of clusters | 30 |
+| bias_dt | float | time interval of biased MD | 0.002 |
+| bias_temperature | float/int | temperature for biased MD | 320 |
+
+**Setting for restrained MD**
+
+| Parameters | Type | Description | Default/Example |
+| :----: | :----: | :----: | :----: |
+| res_nsteps | int | total number of steps of restrained MD | 25000 |
+| res_frame_freq | int | frame frequency for recording| 50 |
+| res_dt | float | time interval of restrained MD | 0.002 |
+| res_temperature | int | temperature for restrained MD | 320 |
+| res_kappa | float/int | force constant for restraining CV | 500 |
+| **res_traj_stride** | int | brute force trajectory stride | 500 |
+| res_ang_stride | int | step stride of angle | 5 |
+| res_prt_file | str | file name | plm.res.out |
+| init_numb_cluster_upper | int | upper limit of cluster selection | 26 |
+| init_numb_cluster_lower | int | lower limit of cluster selection | 16 |
+| conf_start | int | the index of the first conformation selected | 0 |
+| conf_every | int | the stride of conformation selection | 1 |
+
+**Setting for training and neuro network**
+
+| Parameters | Type | Description | Default/Example |
+| :----: | :----: | :----: | :----: |
+| numb_model | int | number of nn models | 4 |
+| neurons | list&int | number of nodes for each layer | [256, 128, 64, 32] |
+| resnet | bool | whether to use Resnet | True |
+| batch_size | int | batch size | 128 |
+| numb_epoches | int | total number of epochs for every training | 2000 |
+| starter_lr | float | initial learning rate | 0.0008 |
+| decay_steps | int | decay steps of lr | 120 |
+| decay_rate | float | decay rate of lr | 0.96 |
+| **res_iter** | int | after this iteration, old data will be reduced | 13 |
+| res_numb_epoches | int | restrat setting | 2000 |
+| res_starter_lr | float | restrat setting | 0.0008 |
+| res_olddata_ratio | int/float | restrat setting | 7 |
+| res_decay_steps | int | restrat setting | 120 |
+| res_decay_rate | float | restrat setting | 0.96 |
+
diff --git a/abnn/lib/BatchJob.py b/abnn/lib/BatchJob.py
diff --git a/abnn/lib/MachineLocal.py b/abnn/lib/MachineLocal.py