Package: tcflow-tmp
Table of contentsAbout ThermalConductivity-WorkflowWorkflow FrameworkPrepare Input FileAbout ThermalConductivity-WorkflowThermalConductivity-Workflow is designed to compute thermal conductivity of different materials based on Non-Equilibrium & Equilibrium Molecular Dynamics Simulations. It is supported bydflow, a Python framework for constructing scientific computing workflows.About dflowWith the power of dflow, users can easily minitor the whole workflow of ThermalConductivity tasks and dispatch their tasks to various computational resources. Before you use it, you should have dflow installed on your host computer (your PC or a remote server).It it necessary to emphasize that, the computational nodes and monitor nodes are seperated. With dflow, you can deploy dflow and ThermalConductivity on your PC and achieve expensive computation on other resources (like Slurm and Cloud Platform) without any further effort.Instructions of dflow installation are provided in detail on itsGithub page. Prerequisites of dflow usage are Docker and Kubenetes, where their main pages (Docker&Kubenetesinclude how you can install them. Besides, dflow repo also provides with easy-install shell scripts ondflow/scriptsto install Docker & Kubenetes & dflow and make port-forwarding.Environment InstallationThe overall workflow logic is as follows: The necessary preparation files for the workflow are uploaded to a remote server (currently, Bohrium provides free resources for this part). Then, the remote server distributes the tasks to different supercomputing resources and computing environments based on the set parameters. In practice, we need to prepare two parts of the environment.tcflow, the necessary package in the host machinecomputational environment, needed in both host and computing machineInstallation of task submission environment.The task submission environment can be from any device, whether it is a personal PC or a connected supercomputing head node. Its main task is to package the necessary preparation files and upload them to the remote server. This environment can be set up using pip installation.pipinstalltcflow==0.0.22Installation of computing environmentThe computational environment is supported by several third-party python packages. Dflow provides a framework for scheduling computing resources, but the actual computing environment needs to be configured in advance.
For users ofBohrium, this step is relatively easy as we provide relevant images, which can be directly called.*Please note that registering a Bohrium account is essential for the visualization interface of the subsequent workflow, regardless of whether Bohrium computing resources are called or not.For users of other computing resources, we have packed the required environment for most of the computational steps, which can be easily installed via pip.
Similar to the task submission environment:pipinstalltcflow==0.0.22In the workflow, the computing environment for LAMMPS needs to be prepared by the user. If you are using dp+LAMMPS, you can refer to theEasy InstallorInstall LAMMPSdocumentation provided by DeepModeling for instructions on installing LAMMPS in conjunction with Deep Potential (dp).
For other cases or custom configurations, you can follow the specific requirements and instructions provided by LAMMPS. The official LAMMPS documentation (https://lammps.sandia.gov/doc/Manual.html) provides comprehensive information on how to install LAMMPS for different platforms and configurations. You can refer to the documentation that matches your specific setup and requirements.*Example of LAMMPS environment installation, using conda for dp+LAMMPS in a GPU environment, referring toEasy Install.condacreate-ndeepmddeepmd-kit=*=*gpulibdeepmd=*=*gpulammpscudatoolkit=11.6horovod-chttps://conda.deepmodeling.com-cdefaultsWorkflow FrameworkEMD(Equilibrium Molecular Dynamics Simulations)The process of computing thermal conductivities with EMD is as follows:graph TD
A[Initial Structure and Force Field] -->|LAMMPS| B{NVT}
B --> C[Structure 1]
B --> D[Structure 2]
B --> E[...]
B --> F[Structure N]
C -->|randon velocity| C1{NVE}
D -->|randon velocity| D1{NVE}
E -->|randon velocity| E1{NVE}
F -->|randon velocity| F1{NVE}
C1 --> G[Heat Fluxes and Mass Fluxes]
D1 --> G[Heat Fluxes and Mass Fluxes]
E1 --> G[Heat Fluxes and Mass Fluxes]
F1 --> G[Heat Fluxes and Mass Fluxes]
G -->|Sportran| H{Cesptral Analysis}
H --> I[Theraml Conductivity]NEMD(Non-Equilibrium Molecular Dynamics Simulations)The process of computing thermal conductivities with NEMD is as follows:graph TD
A[Initial Cell] -->B{Cell Expansion along One Axis}
B -->|e.g. 6x3x2| C[Cell 1]
B -->|e.g. 15x3x2| D[Cell 2]
B -->|...| E[...]
B -->|e.g. 50x3x2| F[Cell N]
C -->|Langevin| C1{NEMD}
D -->|Langevin| D1{NEMD}
E -->|Langevin| E1{NEMD}
F -->|Langevin| F1{NEMD}
C1 --> C2[Temperature Gradient and Kappa 1]
D1 --> D2[Temperature Gradient and Kappa 2]
E1 --> E2[Temperature Gradient and Kappa ...]
F1 --> F2[Temperature Gradient and Kappa N]
C2 -->G{Linear extrapolation}
D2 -->G{Linear extrapolation}
E2 -->G{Linear extrapolation}
F2 -->G{Linear extrapolation}
G -->H[Thermal Conductivity]Prepare Input FilesFirstly, we introduce the input files required for the ThermalConductivity.Input files in example/ have been prepared.
The following files is needed:data.lammps, the input structure file of lammps formatparameters.json, the settings for simulations and tasksmachine.json, the settings for computing resourcesforce field(if needed), the force field file used in lammps MDPrepare parameters.jsonparameters.json of EMDparameters.json for emd in /example/emd/H2O{"type_map":["H","O"],"mass_map":[1,16],"temperature":408,"time_step":0.0002,"thermo_print_interval":10,"traj_print_interval":1000,"structure":"./data.water","is_two-body-potential":false,"force_field":["frozen_model_compressed.pb"],"load_force_field":["pair_style deepmd frozen_model_compressed.pb","pair_coeff * *"],"num_configurations":10,"NVT_steps":1000000,"NVE_steps":1800000,"md_command":"mpirun -n 1 lmp < in.lammps"}Description of parametersParametersTypeDescriptiontype_maplist of strGive the name to each type of atoms.mass_maplist of floatGive the mass to each type of atoms.time_stepfloatSet the timestep size for subsequent molecular dynamics simulations.unit:psthermo_print_intervalintOutput thermodynamics every "thermo_print_interval" timesteps.themotraj_print_intervalintDump structures on timesteps which are multiples of "traj_print_interval".dumpstructurestrThe file path of structure in lammps format.is_two-body-potentialbool"true" for the two-body force field. For example,L-J potential.force_fieldlist of strThe file paths of force field needed for simulationload_force_fieldlist of strThe command to load a force field in an LAMMPS input file.num_configurationsintThe number of independent simulation trajectories.NVT_stepsintThe steps for NVT equilibrium before the NVE simulation.NVE_stepsintThe steps for NVE simulation.聽All the data generated will be used to calculate thermal conductivity.parameters.json of NEMDparameters.json for emd in /example/nemd/Ar{"type_map":["Ar"],"mass_map":[40],"time_step":0.001,"supercell":[[5,1,1],[6,1,1],[8,1,1],[10,1,1]],"temperature":60,"temperature_difference":10,"linear_scale":[0.05,0.95],"thermo_print_interval":1000,"traj_print_interval":1000,"structure":"./data.ar","force_field":[],"load_force_field":["pair_style lj/cut 10.0","pair_coeff * * 1.032e-2 3.405"],"langevin_damp":1.0,"NVT_steps":1000000,"NEMD_equilibrium_steps":2000000,"NEMD_production_steps":10000000,"md_command":"mpirun -n 1 lmp < in.lammps"}description of parametersParametersTypeDescriptiontype_maplist of strGive the name to each type of atoms.mass_maplist of floatGive the mass to each type of atoms.time_stepfloatSet the timestep size for subsequent molecular dynamics simulations.unit:pssupercelllist of ternary arraysSuper cells to be calculated.The direction of heat flow is fixed along the x-axis.temperaturefloatThe average temperature of the system.temperature_differencefloatThe temperature difference between the heat source(sink) and the average temperature.linear_scale[float,float]The linear range of temperature distribution between the heat source and the heat sink.thermo_print_intervalintOutput thermodynamics every "thermo_print_interval" timesteps.themotraj_print_intervalintDump structures on timesteps which are multiples of "traj_print_interval".dumpstructurestrThe file path of structure in lammps format.force_fieldlist of strThe file paths of force field needed for simulationload_force_fieldlist of strThe command to load a force field in an LAMMPS input file.langevin_dampfloatThe parameter of fix/langevin in LAMMPS.fix/langevinThe聽_damp_聽parameter is specified in time units and determines how rapidly the temperature is relaxed. For example, a value of 100.0 means to relax the temperature in a timespan of (roughly) 100 time units (蟿聽or fs or ps - see the聽units聽command). The damp factor can be thought of as inversely related to the viscosity of the solvent. I.e. a small relaxation time implies a high-viscosity solvent and vice versa.NVT_stepsintThe steps for NVT equilibrium.NEMD_equilibrium_stepsintThe steps for equilibrium stage of NEMD. It should be long enough to generate a stable temperature gradientNEMD_production_stepsintThe steps for production stage of NEMD. All the data generated will be used to calculate thermal conductivitymd_commandstrThe command to run the lammpsPrepare machine.jsonmachine.json for nemd in /example/nemd/Ar{"machine_type":"cluster","email":"","password":"","program_id":1234,"host":"","port":22,"username":"","ssh_password":"","remote_root":"/data/home/test/ar","md_machine_dict":{"batch_type":"slurm","local_root":"./","context_type":"SSHContext","clean_asynchronously":false},"md_resources_dict":{"number_node":1,"cpu_per_node":1,"gpu_per_node":0,"queue_name":"","group_size":1,"custom_flags":["#SBATCH --time=0-1000:00:00",],"source_list":["activate deepmd_2.1.4"],"batch_type":"Slurm"},"analysis_machine_dict":{"batch_type":"slurm","local_root":"./","context_type":"SSHContext","clean_asynchronously":false},"analysis_resources_dict":{"number_node":1,"cpu_per_node":1,"gpu_per_node":0,"queue_name":"","group_size":1,"custom_flags":["#SBATCH --time=0-1000:00:00",],"source_list":["activate deepmd_2.1.4"],"batch_type":"Slurm"}}description of parametersParametersTypeDescriptionmachine_typestrThe type of user's computing resouces.If usingBohrium,it should be "bohrium".The others should be "cluster"emailstrThe username of the Bohrium account. It can be email or telephone number.passwordstrThe password of the Bohrium accountprogram_idintThe program id of corresponding pragramhoststrThe host for connecting to remote computing resources.portintThe port for connecting to remote computing resourcesusernamestrThe username for connecting to remote computing resourcesssh_passwordstrThe password for connecting to remote computing resourcesremote_rootstrThe working path in remote computing resourcesmd_machine_dictjsonMachine parameters for running md.GPU is recommended.Refer to the description in dpdispatcher.machine parametersmd_resouces_dictjsonResources parameters for running md.GPU is recommended.Refer to the description in dpdispatcher.resources parametersanalysis_machine_dictjsonMachine parameters for analysis part. The description is the same as md_machine_dictanalysis_resouces_dictjsonResources parameters for analysis part. The description is the same as md_resources_dictFor Bohrium usersBohrium integrates well with dflow for resource scheduling, and the parameter configuration is relatively simpler. An example used on the Bohrium platform is as follows:{"machine_type":"bohrium","email":"","password":"","program_id":1234,"lammps_image":"registry.dp.tech/dptech/lammps:23Jun2022-dp2.1.5","tc_image":"registry.dp.tech/dptech/thermal-conductivity:v0.12","cpu_scass_type":"","gpu_scass_type":""}description of parametersParametersTypeDescriptionmachine_typestrThe type of user's computing resouces.If usingBohrium,it should be "bohrium".The others should be "cluster"emailstrThe username of the Bohrium account. It can be email or telephone number.passwordstrThe password of the Bohrium accountprogram_idintThe program id of corresponding pragramlammps_imagestrThe container image in bohrium platform for running the LAMMPStc_imagestrThe container image in bohrium platform for this workflowcpu_scass_typestrThe machine type of cpu resources for analysis.You can find itheregpu_scass_typestrThe machine type of gpu resources for running md.You can find ithere