Update README.md

Add the run/install time.

docs/README.md

@@ -6,7 +6,7 @@
 [![codecov](https://codecov.io/gh/njzjz/mddatasetbuilder/branch/master/graph/badge.svg)](https://codecov.io/gh/njzjz/mddatasetbuilder)
 [![Research Group](https://img.shields.io/website-up-down-green-red/http/computchem.cn.svg?label=Research%20Group)](http://computchem.cn)
 
-MDDatasetBuilder is a script to build molecular dynamics (MD) datasets for neural networks from given LAMMPS trajectories automatically.
+MDDatasetBuilder is a script to construct reference datasets for the training of neural network potentials from given LAMMPS trajectories.
 
 Neural Network Based in Silico Simulation of Combustion Reactions, arXiv:1911.12252
 
@@ -16,17 +16,19 @@ Neural Network Based in Silico Simulation of Combustion Reactions, arXiv:1911.12
 
 ## Installation
 
-You can [install Anaconda or Miniconda](https://conda.io/projects/continuumio-conda/en/latest/user-guide/install/index.html) to obtain conda, and install [openbabel](https://github.com/openbabel/openbabel) first with conda:
+Firstly, the latest version of [Anaconda or Miniconda](https://conda.io/projects/continuumio-conda/en/latest/user-guide/install/index.html) shoule be installed. And then use conda to install [openbabel](https://github.com/openbabel/openbabel):
 
 ```sh
 conda install openbabel -c conda-forge
 ```
 
-Then install mddatasetbuilder with pip:
+Then install mddatasetbuilder can be installed with pip:
 ```
 pip install git+https://github.com/tongzhugroup/mddatasetbuilder
 ```
 
+The installation process should be very quick, taking only a few minutes on a “normal” desktop computer. 
+
 ## Usage
 ### Simple example
 
@@ -38,19 +40,21 @@ datasetbuilder -d dump.ch4 -b bonds.reaxc.ch4_new -a C H O -n ch4 -i 25
 
 Here, `dump.ch4` is the name of the dump file. `bonds.reaxc.ch4_new` is the name of the bond file, which is optional. `C H O` is the element in the trajectory. `ch4` is the name of the dataset. `25` means the time step interval and the default value is 1.
 
-Then you can calculate generated Gaussian files (assume you have already install Gaussian 16):
+Then you can generate Gaussian input files for each structure in the dataset and calculate the potential energy & atomic forces (assume the Gaussian 16 has already been installed.):
 
 ```bash
 qmcalc -d dataset_ch4_GJf/000
 qmcalc -d dataset_ch4_GJf/001
 ```
 
-Next, prepare a DeePMD dataset and use [DeePMD-kit](https://github.com/deepmodeling/deepmd-kit) to train a model.
+Next, prepare a DeePMD dataset and use [DeePMD-kit](https://github.com/deepmodeling/deepmd-kit) to train a NN model.
 
 ```bash
 preparedeepmd -p dataset_ch4_GJf -a C H O
 cd train && dp train train.json
 ```
 
+The runtime of the software depends on the amount of data. It is more suited to running on a server rather than desktop computer.
+
 ### DP-GEN
 The MDDatasetBuilder package has been integrated into [DP-GEN](https://github.com/deepmodeling/dpgen) software.