Source code for dpgen.data.reaction

"""input: trajectory
00: ReaxFF MD (lammps)
01: build dataset (mddatasetbuilder)
02: fp (gaussian)
03: convert to deepmd data
output: data.
"""

import glob
import os
import random

import dpdata

from dpgen import dlog
from dpgen.dispatcher.Dispatcher import make_submission_compat
from dpgen.generator.run import create_path, make_fp_task_name
from dpgen.remote.decide_machine import convert_mdata
from dpgen.util import load_file, normalize, sepline

from .arginfo import init_reaction_jdata_arginfo

reaxff_path = "00.reaxff"
build_path = "01.build"
fp_path = "02.fp"
data_path = "03.data"

trj_path = "lammpstrj"
ff_path = "ffield.reax"
data_init_path = "data.init"
control_path = "lmp_control"
lmp_path = "in.lmp"
dataset_name = "dpgen_init"








[docs]
def make_lmp(jdata):
    rdata = jdata["reaxff"]
    lmp_string = """units real
atom_style charge
read_data data.init
pair_style reax/c lmp_control
pair_coeff * * ffield.reax {type_map}
velocity all create {temp} {rand}
fix 1 all nvt temp {temp} {temp} {tau_t}
fix 2 all qeq/reax 1 0.0 10.0 1.0e-6 reax/c
dump 1 all custom {dump_freq} lammpstrj id type x y z
timestep {dt}
run	{nstep}
""".format(
        type_map=" ".join(jdata["type_map"]),
        temp=rdata["temp"],
        rand=random.randrange(1000000 - 1) + 1,
        tau_t=rdata["tau_t"],
        dump_freq=rdata["dump_freq"],
        dt=rdata["dt"],
        nstep=rdata["nstep"],
    )
    return lmp_string






[docs]
def run_reaxff(jdata, mdata, log_file="reaxff_log"):
    work_path = reaxff_path
    reaxff_command = "{} -in {}".format(mdata["reaxff_command"], lmp_path)
    run_tasks = glob.glob(os.path.join(work_path, "task.*"))
    run_tasks.sort()
    run_tasks = [os.path.basename(ii) for ii in run_tasks]

    make_submission_compat(
        mdata["reaxff_machine"],
        mdata["reaxff_resources"],
        [reaxff_command],
        work_path,
        run_tasks,
        1,
        [],
        [ff_path, data_init_path, control_path, lmp_path],
        [trj_path],
        outlog=log_file,
        errlog=log_file,
        api_version=mdata.get("api_version", "0.9"),
    )










[docs]
def run_build_dataset(jdata, mdata, log_file="build_log"):
    work_path = build_path
    # only compatible with new dpdispatcher
    build_ntasks = mdata["build_resources"]["cpu_per_node"]
    fp_ntasks = mdata["fp_resources"]["cpu_per_node"]
    build_command = '{cmd} -n {dataset_name} -a {type_map} -d {lammpstrj} -c {cutoff} -s {dataset_size} -k "{qmkeywords}" --nprocjob {nprocjob} --nproc {nproc}'.format(
        cmd=mdata["build_command"],
        type_map=" ".join(jdata["type_map"]),
        lammpstrj=trj_path,
        cutoff=jdata["cutoff"],
        dataset_size=jdata["dataset_size"],
        qmkeywords=jdata["qmkeywords"],
        nprocjob=fp_ntasks,
        nproc=build_ntasks,
        dataset_name=dataset_name,
    )
    run_tasks = glob.glob(os.path.join(work_path, "task.*"))
    run_tasks.sort()
    run_tasks = [os.path.basename(ii) for ii in run_tasks]

    make_submission_compat(
        mdata["build_machine"],
        mdata["build_resources"],
        [build_command],
        work_path,
        run_tasks,
        1,
        [],
        [trj_path],
        [f"dataset_{dataset_name}_gjf"],
        outlog=log_file,
        errlog=log_file,
        api_version=mdata.get("api_version", "0.9"),
    )










[docs]
def run_fp(jdata, mdata, log_file="output", forward_common_files=[]):
    fp_command = mdata["fp_command"]
    fp_group_size = mdata["fp_group_size"]
    work_path = fp_path

    fp_tasks = glob.glob(os.path.join(work_path, "task.*"))
    fp_tasks.sort()
    if len(fp_tasks) == 0:
        return

    fp_run_tasks = fp_tasks

    run_tasks = [os.path.basename(ii) for ii in fp_run_tasks]

    make_submission_compat(
        mdata["fp_machine"],
        mdata["fp_resources"],
        [fp_command],
        work_path,
        run_tasks,
        fp_group_size,
        [],
        ["input"],
        [log_file],
        outlog=log_file,
        errlog=log_file,
        api_version=mdata.get("api_version", "0.9"),
    )






[docs]
def convert_data(jdata):
    s = dpdata.MultiSystems(
        *[
            dpdata.LabeledSystem(x, fmt="gaussian/log")
            for x in glob.glob(os.path.join(fp_path, "*", "output"))
        ],
        type_map=jdata["type_map"],
    )
    s.to_deepmd_npy(data_path)
    dlog.info(f"Initial data is avaiable in {os.path.abspath(data_path)}")






[docs]
def gen_init_reaction(args):
    jdata = load_file(args.PARAM)
    mdata = load_file(args.MACHINE)

    jdata_arginfo = init_reaction_jdata_arginfo()
    jdata = normalize(jdata_arginfo, jdata)

    mdata = convert_mdata(mdata, ["reaxff", "build", "fp"])
    record = "record.reaction"
    iter_rec = -1
    numb_task = 7
    if os.path.isfile(record):
        with open(record) as frec:
            for line in frec:
                iter_rec = int(line.strip())
        dlog.info("continue from task %02d" % iter_rec)
    for ii in range(numb_task):
        sepline(str(ii), "-")
        if ii <= iter_rec:
            continue
        elif ii == 0:
            link_reaxff(jdata)
        elif ii == 1:
            run_reaxff(jdata, mdata)
        elif ii == 2:
            link_trj(jdata)
        elif ii == 3:
            run_build_dataset(jdata, mdata)
        elif ii == 4:
            link_fp_input()
        elif ii == 5:
            run_fp(jdata, mdata)
        elif ii == 6:
            convert_data(jdata)
        with open(record, "a") as frec:
            frec.write(str(ii) + "\n")