deepmodeling, deepmd, dpti, free energy, phase diagram

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github: https://github.com/deepmodeling/dpti
relating docs: https://www.yuque.com/dpti/manual/ilzmlb

🌾OutPut show

We could use dpti to calculate out the Press-Volume phase diagram of metals.
The picture below shows the metal Sn phase diagram results calculated by one of the authors.

The left subgraph shows the experiment phase diagram results.(see:https://aip.scitation.org/doi/10.1063/1.4872458)

The middle subgraph shows the DP phase diagram based on SCAN functional DFT calculation results.

The right subgraph shows the DP phase diagram base on PBE functional DFT calculation results.

🏞brief Introduction

dpti (deep potential thermodynamic integration) is a python package for calculating free energy, doing thermodynamic integration and figuring out pressure-temperature phase diagram for materials with molecular dynamics (MD) simulation methods.

The user will get Gibbs (Helmholtz) free energy of a system at different temperature and pressure conditions. With these free energy results, the user could determine the phase transition points and coexistence curve on the pressure-volume phase diagram.

🦴software introduction

At first, dpti is a collection of python scripts to generate LAMMPS input scripts and to anaylze results from LAMMPS logs.

In dpti, there are many MD simulations tasks and scripts need to be run sequentially or concurrently. Before and after these MD simulation tasks, we may run a lot of MD scirpts to prepare the input files or analyze the logs to extract the useful data.

Then the dpti developers use apache-airflow to resolve the MD tasks dependencies and managing running tasks.

📃Installation

dpti use apache-airflow as workflow framework, and dpdispatcher to interact with the HPC systems (slurm or PBS).

airflow use realation database (PostgreSQL, MySQL or Sqlite) as backend to store the metadata, DAGs definetion and nodes state etc.

install dpti and dpdispatcher.

git clone the following packages and install.
https://github.com/deepmodeling/dpdispatcher
https://github.com/deepmodeling/dpti

cd dpti/
python setup.py install

cd dpdispatcher/
python setup.py install

configure apache-airflow.

airflow user manual: https://airflow.apache.org/docs/apache-airflow/stable/index.html

# airflow will create at ~/airflow
airflow -h
cd ~/airflow

# airflow will initialize datebase with sqlite
airflow db init

# create a user
airflow users create \
    --username airflow \
    --firstname Peter \
    --lastname Parker \
    --role Admin \
    --email [email protected]
    
 # you will be requested to enter the password here.
 
 
 # start airflow's webserver to manage your workflow use "-D" option to daemon it
 airflow webserver --port 8080 --hostname 127.0.0.1
 
 # start airflwo scheduler
 airflow scheduler
 
 # if ariflow web server start at the personal computer,
 # you could go to http://localhost:8080/ to view it
 # if airflow runs on remote server 
 # you could use ssh to conntect to server
 # ssh -CqTnN -L localhost:8080:localhost:8080 [email protected]

🚀Quick Start

 # copy dpti'workflow file  
 cp /path-to-dpti/workflow/DpFreeEnergy.py ~/airflow/dags/
 
 # create a workdir and copy example files
 cp /path-to-dpti/examples/*json /path-to-a-work-dir/
 
 # start our airflow job
 cd /path-to-a-work-dir/
 cat ./airflow.sh
 
 airflow dags trigger  TI_taskflow  --conf $(printf "%s" $(cat FreeEnergy.json))
 

🕹install postgresql database

Airflow use relation database as backend. And PostgreSQL is widely used in airflow community.

install database

airflow's introduction on how to set up database backend: https://airflow.apache.org/docs/apache-airflow/stable/howto/set-up-database.html

# install apache-airflow postgresql module
pip install apache-airflow-providers-postgres

# install postgresql
yum install postgresql

# enable postgresql service
systemctl start postgresql

# enter posgresql
psql

create database and database user

CREATE DATABASE airflow_db1;
CREATE USER airflow_user1 WITH PASSWORD 'airflow_user1';
GRANT ALL PRIVILEGES ON DATABASE airflow_db1 TO airflow_user1;

configure airflow configure file to connect database

configure ~/airflow/airflow.cfg

# change the following item with the sql above
# sql_alchemy_conn = sqlite:////home/fengbo/airflow/airflow.db
# sql_alchemy_conn = postgres://airflow:airflow@localhost:5432/airflow
sql_alchemy_conn = postgresql+psycopg2://<user>:<password>@<host>:<port>/<db_name>

configure a

reset db and webserver scheduler

# reset db
airflow db init
# -D flag represent daemonize
airflow webserver # -p 8080 -D
airflow scheduler # -D

airflow webserver

TODO

💪extra info

The backend of this software is based on the software airflow. The following command can start the calculation.

The first command is used for calculate the free energy of solid.
The second command is used for calculate the free energy of liquid.

airflow trigger_dag HTI_taskflow --conf $(printf "%s" $(cat FreeEnergy.json))
airflow trigger_dag TI_taskflow --conf $(printf "%s" $(cat FreeEnergy.meam.json))

1. We usually want to calculate the free energy of a metal at a specific pressure or temperature. And the crystal structure of the metal can be various. For example, we want to calculate the free energy of metal Sn of bcc structure at 200 K and 50000 bar (5GPa). In order to caculate the per atom free energy of metal Sn. First, We must prepare a configuration file named bcc.lmp and modify the FreeEnergy.json or FreeEnergyLiquid.json and modify the key-value pair like "structure": "bcc", "target_temp": 200, "target_press" : 50000. And decide whether to integrate along the t(temperature) path or along the p(pressure) path . Modify the "path" key-value pair for this. The key-value pair "ensemble" for lammps MD simulation. Usually the ensemble shoule be consistent with the crystal intrinsic structure. That means we should set "npt-iso" for structure "bcc" to keep the simulation box changes simultaneously in x, y, z directions.
2. modify the ti.t.json or ti.p.json, and change the key-value pair "temps" or "press" . For ti.t.json, the tar_temp of FreeEnergy.json must be in the list which the key-value pair "temps" of ti.t.json represents. And similarly for ti.p.json, the tar_press of FreeEnergy.json must be in the list which the key-value pair "temps" of ti.t.json represents.
3. Use the command airflow trigger_dag  mentioned above. This command will start a airflow dag.This dag is wrote and maintained by the dpti software developer. It is used to make the calculation to be done more autocally . The user could monitor the task state and calculation procedure at a website. The user can also rerun, restart, delete the whole calculation or some part of the calculations.
4. Wait until the calculation finish. Usually the whole procedure continues for about 6 to 10 hours. The calculations will be done autocally.
5. Find the results in Results Show part. The user could use the tables and data of it and plot the curve.

📌Results Show

For each step, the result files are located at the corresponding location.
For example, we start a calculation at /home/user1/metal_Sn/1_free_energy/400K-0bar-bcc-t

For NPT MD simulation, the result file will locate at /home/user1/metal_Sn/1_free_energy/400K-0bar-bcc-t/NPT_sim/result

For TI simulation the result will locate at /home/fengbo/4_Sn/1_free_energy/400K-0bar-bct-t/TI_sim/result

You may want to use the result file and datas of TI_sim/result and plot the free_energy vs T curve for different structure and find the crossing point.

The user can ssh contect to the aliyun cloud server by command like  ssh -L localhost:8080:localhost:8080 [email protected]  and visit http://localhost:8080/ to monitor the free energy calculation tasks procedure.

💎The procedure of the free energy calculations

To calculate out Gibbs (or Helmholtz) free energy of the materials, there are four steps.

1. NPT MD simulation
2. NVT MD simulation
3. Hamiltonian thermodynamic integration
4. thermodynamic integration

NPT simulation

Run a long MD simulation and we will get the lattice constant and the best simulation box for the simulations next.

NVT simulation

Run a long MD simulation with the end structure of NPT simulations. We will know whether the box is reasonable enough from the results of this MD simulation.

Hamiltonian thermodynamic integration (HTI)

We will know the Gibbs (or Helmholtz) free energy at the specific temperature or pressure condition.

thermodynamic integration (TI)

Integrating along the isothermal or isobaric path, We will know the free energy at different pressure and temperature.

🌾JSON file settings

There are diffefrent json files desinged for different usage.

1. FreeEnergy.json   control the whole workflow
2. npt.json
3. nvt.json
4. hti.json or hti.liquid.json
5. ti.t.json or ti.p.json

FreeEnergy.json

FreeEnergy calculation settings for solid

Field	Type	Example	Discription
target_temp	positive integer	200	the temperature of HTI
target_press	non-negative integer	50000	unit :bar.the pressure of HTI
work_base_dir	string	"/home/user1/metal_Sn"	see note1. work  directory.
ti_path	"t" or "p"	"t"	thermodynamic integration along temperature or pressure
conf_lmp	string	"bct.lmp"	see note1. the materials structure to be calculated
ens	string	"npt-iso"	MD simulation ensemble in lammps
if_liquid	bool	false	if simulate liquid

note:

1. the conf_lmp file must be in the work_base_dir.

npt.json

the settings used in MD NPT simulations.

Field	Type	Example	Description
equi_conf	string	"conf.lmp"	do not change this pair
model	string	"graph.pb"	do not change this pair
mass_map	list of float	[118.71]	relative atom mass
nstep	integer	1000000	MD simulation steps in the lammps NPT simulation
timestep	float	0.002	lammps script time_step. unit: picosecond
ens	string	"npt-iso"	lammps MD ensemble setting
pres	positive integer	50000	pressure in MD simulation (unit:bar; 1GPa==10000bar)
tau_t	float	0.2	Tdamp in lammps fix npt command
tau_p	float	2.0	Pdamp in lammps fix npt command
thermo_freq	positive integer	10	statistic frequency

nvt.json

the settings used in MD NPT simulations

Field	Type	Example	Description
equi_conf	string	"conf.lmp"	do not change this pair
model	string	"graph.pb"	do not change this pair
mass_map	list of float	[118.71]	relative atom mass
nstep	integer	1000000	MD simulation steps in the lammps NPT simulation
timestep	float	0.002	lammps script time_step. unit: picosecond
ens	string	"npt-iso"	lammps MD ensemble setting
pres	positive integer	50000	pressure in MD simulation (unit:bar; 1GPa==10000bar)
tau_t	float	0.2	Tdamp in lammps fix npt command
tau_p	float	2.0	Pdamp in lammps fix npt command
thermo_freq	positive integer	10	statistic frequency

hti.json

For solid, the settings used in Hamiltonian thermodyniamics integration (HTI)

Field	Type	Example	Description
equi_conf	string	"conf.lmp"	do not change this pair
ncopies	list of integer	[1,1,1]	do not change this pair
lambda_lj_on	list of arange	["0.000:0.100:0.0125", "0.100:0.200:0.025", "0.200:1.000:0.2", "1"]	the lambda value used in 00.lj_on numerial integration
lambda_deep_on	list of arange	["0.00:0.05:0.010", "0.05:0.15:0.02", "0.15:0.35:0.040", "0.35:1.00:0.065", "1"]	the lambda value used in 01.deep_on numerial integration
lambda_spring_off	list of arange	["0.000:0.750:0.125", "0.750:0.900:0.050", "0.900:0.960:0.020", "0.960:1.00:0.010", "1"]	the lambda value used in 02.spring_off numerial integration
protect_eps	float (usuall small positive number)	1e-06	the minimum lambda number used in numerial integration
model	string	"graph.pb"	do not change this pair
mass_map	list of float	[118.71]	relative atomic mass
spring_k	float	0.02	spring constant used in Einstein solid.
soft_param	dictionary	{"sigma_0_0":2,7, "epsilon":0.030, "activation":0.5, "n":1.0, "alpha_lj":0.5, "rcut":6.0}	see: note1 below
crystal	"frenkel" or "vega	"frenkel"	different  Einstein solid approximation method
langevin	bool	true	whether use langevin  thermostat
nsteps	integer	200000	MD steps in each simulation
timestep	float	0.002	time_step in lammps MD simulation (unit: picosecond)
thermo_freq	integer	10	statistic frequency
stat_skip	integer	10000	skip the first n steps in statistic
stat_bsize	integer	200	batch size in statistic
temp	integer	400	the target temperature in HTI calculation

note:

1. the parameter defined by lammps pair_style lj/cut/soft and pair_coeff command. see lammps lj/cut/soft
2. sigma_0_0 means the sigma value for the lammps atom type 0 and atom type 0.

hti.liquid.json

For solid, the settings used in Hamiltonian thermodyniamics integration (HTI).

Field	Type	Example	Description
equi_conf	string	"conf.lmp"	do not change this pair
ncopies	list of integer	[1,1,1]	do not change this pair
lambda_soft_on	list of arange	["0.000:0.030:0.003", "0.030:0.060:0.005", "0.060:0.160:0.010", "0.160:0.300:0.020", "0.300:1.000:0.050", "1"]	the lambda value used in 00.soft_on numerial integration
lambda_deep_on	list of arange	["0.000:0.006:0.002", "0.006:0.030:0.004", "0.030:0.100:0.010", "0.100:0.400:0.030", "0.400:1.000:0.060", "1"]	the lambda value used in 01.deep_on numerial integration
lambda_soft_off	list of arange	["0.000:0.750:0.125", "0.750:0.900:0.050", "0.900:1.000:0.020", "1"]	the lambda value used in 02.soft_off numerial integration
protect_eps	float (usually small positive number)	1e-06	the minimum lambda number used in numerial integration
model	string	"graph.pb"	do not change this pair
mass_map	list of float	[118.71]	relative atomic mass
spring_k	float	0.02	spring constant used in Einstein solid.
soft_param	dictionary	{"sigma_0_0":2,7, "epsilon":0.030, "activation":0.5, "n":1.0, "alpha_lj":0.5, "rcut":6.0}	see: note1 below
crystal	"frenkel" or "vega	"frenkel"	different  Einstein solid approximation method
langevin	bool	true	whether use langevin  thermostat
nsteps	integer	200000	MD steps in each simulation
timestep	float	0.002	time_step in lammps MD simulation (unit: picosecond)
thermo_freq	integer	10	statistic frequency
stat_skip	integer	10000	skip the first n steps in statistic
stat_bsize	integer	200	batch size in statistic
temp	integer	400	the target temperature in HTI calculation

note:

1. the parameter defined by lammps pair_style lj/cut/soft and pair_coeff command. see lammps lj/cut/soft
2. sigma_0_0 means the sigma value for the lammps atom type 0 and atom type 0.

ti.t.json

the settings used in thermodynamic integration (TI) for constant pressure and changeable temperature

Field	Type	Example	Description
equi_conf	string	"conf.lmp"	do not change this pair
copies	list of integer	[1,1,1]	do not change this pair
model	string	"graph.pb"	do not change this pair
mass_map	list of float	[118.71]	relative atom mass
nstep	integer	200000	MD simulation steps in the lammps NPT simulation
timestep	float	0.002	lammps script time_step. unit: picosecond
ens	string	npt-aniso	lammps MD simulation ensemble setting
path	"t" or "p"	"t"	do not change this pair for ti.t.json
temp_seq	list of arange	["200:1400:20", 1400]	temperature list to be calculated. The HTI tar_temp must be in it.
pres	integer	50000	the target pressure of HTI calculation
tau_t	float	0.2	lammps Tdamp
tau_p	float	2.0	lammps Pdamp
thermo_freq	integer	10	statistic frequency
stat_skip	integer	5000	skip the first n steps in statistic
stat_bsize	integer	200	statistic batch size

ti.p.json

the settings used in thermodynamic integration (TI) for constant temperature and changeable pressure

Field	Type	Example	Description
equi_conf	string	"conf.lmp"	do not change this pair
copies	list of integer	[1,1,1]	do not change this pair
model	string	"graph.pb"	do not change this pair
mass_map	list of float	[118.71]	relative atom mass
nstep	integer	200000	MD simulation steps in the lammps NPT simulation
timestep	float	0.002	lammps script time_step. unit: picosecond
ens	string	npt-aniso	lammps MD simulation ensemble setting
path	"t" or "p"	"t"	do not change this pair for ti.t.json
temp	integer	800	temperature to be calculated
pres_seq	list of arange	[0:100000:2000, 100000]	the pressure list to be calculated. The HTI tar_pres must be in it.
tau_t	float	0.2	lammps Tdamp
tau_p	float	2.0	lammps Pdamp
thermo_freq	integer	10	statistic frequency
stat_skip	integer	5000	skip the first n steps in statistic
stat_bsize	integer	200	statistic batch size

gdi.json

The gdi.json is used for gibbs-duham integration. When you know the one point at the two phase coexisting-line. You can do gibbs-duham integration to get the whole phase boundry

Field	Type	Example	Description
phase_i	dict	{"name": "PHASE_0", "equi_conf":"bct.lmp", "ens":"npt-xy"}	phase 1 information
phase_ii	dict	{"name": "PHASE_1", "equi_conf":"liquid.lmp", "ens":"npt-iso"}	phase 2 information
model	str	"graph.pb"
mass_map	list of float	[118.71]	relative atomic mass
nsteps	integer	100000	MD steps in simulation
timestep	float	0.002	MD timestep (in ps)
tau_t	float	0.1	MD NPT tau_t
tau_p	float	1.0	MD NPT tau_p
thermo_freq	integer	10	MD thermo frequency
stat_skip	integer	5000	skip the first 5000 steps in lammps log
stat_bsize	integer	100	statistic batch size

👀Troubleshooting

TODO