RiD (Reinforced dynamics)

0. Content

1. Introduction
1. Installation
- 2.1. Environment installation
  - 2.1.1. Install python and tensorflow
  - 2.1.2. Install tensorflow's C++ interface
  - 2.1.3. Install plumed2.5.2
  - 2.1.4. Install gromacs 2019.2
1. Quick Start
- 3.1. RiD work_path generation
- 3.2. Run RiD
1. Main procedure of RiD
  - 4.1. a. Biased MD
  - 4.2. b. Restrained MD
  - 4.3. c. Neural network training
1. RiD settings
- 5.1. rid.json

1. Introduction

RiD (Reinforced Dynamics) is an enhanced sampling method.

2. Installation

Note:

If you want to set environments by hand, please follow settings 2.1.1 ~ 2.1.4. (The install time is about half a day)

If you want to set the environment of RiD through the one-click installer, we offer a link. (password: b4np)to download, just run it with shell. (The install time is about ten minutes)

2.1. Environment installation

2.1.1. Install python and tensorflow (version<=1.15)

2.1.2 Install tensorflow's C++ interface

The tensorflow's C++ interface will be compiled from the source code, can be found here.

2.1.3 Install plumed2.5.2

You need copy compiled DeePFE.cpp to the plumed directory. This file locats at install/DeePFE.cpp

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

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

2.1.4 Install gromacs 2019.2

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

source /software/GMX20192plumed/bin/GMXRC.bash

3. Quick Start

Two steps in this version of RiD. The first is the generation of RiD work directory, and then run RiD in the work directory.

3.1. RiD work_path generation

Let's begin with a simple example, ala2, which has a sequence (1ACE, 2ALA, 3NME).The cv.json file can be set as:

{
    "_comment":	   " dihedral angles: phi, psi ",
    "dih_angles" : [ [ {"name" : ["C"],  "resid_shift" : -1},
		       {"name" : ["N"],  "resid_shift" : 0},
		       {"name" : ["CA"], "resid_shift" : 0},
		       {"name" : ["C"],  "resid_shift" : 0} ], 
		     [ {"name" : ["N"],  "resid_shift" : 0}, 
		       {"name" : ["CA"], "resid_shift" : 0},
		       {"name" : ["C"],  "resid_shift" : 0},
		       {"name" : ["N"],  "resid_shift" : 1} ]
		   ],
    "alpha_idx_fmt":	"%03d",
    "angle_idx_fmt":	"%02d"
}

"dih_angles" is our defination of dihedral angles ($\phi$, $\psi$) by default.

cd source
python gen.py rid ./jsons/default_gen.json ./jsons/CV.json ./mol/alan/amber99sb/01/ -o ala2.rid

3.2. run RiD

cd ala2.rid
python rid.py rid.json

This case would run for several hours on one GPU.

The parameters in "rid.json" are described in the following.

In addition, we provide the cases we studied in "examples".

Just change to the directory and type "python rid.py rid.json".

The results would be saved in the current directory.

4. Main procedure of RiD

RiD will run in iterations. Each iteration contains tasks below:

Biased MD
Restrained MD
Training neural network

4.1. a. Biased MD

Different from Metadynamics, RiD will sample based on a bias potential given by NN models. The uncertainty indicator will direct the process of adding bias potential.

4.2. b. Restrained MD

This procedure will calculate mean force based on the sampling results, which can generate data set for training.

4.3. c. Neural 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:

[1] Zhang, L., Wang, H., E, W.. Reinforced dynamics for enhanced sampling in large atomic and molecular systems[J]. The Journal of chemical physics, 2018, 148(12): 124113.

[2] Wang, D., Zhang, L., Wang, H., E, W.. Efficient sampling of high-dimensional free energy landscapes using adaptive reinforced dynamics[J]. arXiv preprint arXiv:2104.01620, 2021.

5. RiD settings

File "rid.json" for configuration of simulation.

5.1. rid.json

General setting

Parameters	Type	Description	Default/Example
gmx_prep	str	Gromacs preparation command	gmx grompp
gmx_run	str	Gromacs md run command	gmx mdrun
init_graph	list&str	initial graph files list	[]
numb_iter	int	number of iterations	12

Setting for biased MD

Parameters	Type	Description	Default/Example
numb_walkers	int	number of walkers	8
bias_trust_lvl_1	int	trust lower level	2
bias_trust_lvl_2	int	trust upper 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	15
max_sel	int	maximum of selection of clusters	30

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
conf_start	int	the index of the first conformation selected	0
conf_every	int	the stride of conformation selection	1

Setting for training and neural network

Parameters	Type	Description	Default/Example
numb_model	int	number of nn models	4
neurons	list&int	number of nodes for each layer	[200, 200, 200, 200]
resnet	bool	whether to use Resnet	True
batch_size	int	batch size	128
numb_epoches	int	total number of epochs for every training	12000
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

y1zhou/rid