ABACUS Pseudopot-Nao Square

About

ABACUS (Atomic-orbital Based Ab-initio Computation at UStc) Pseudopot (pseudopotential)-Nao (numerical atomic orbital) Square (APNS) is a project for continuously generating pseudopotential and numerical atomic orbital test data for ABACUS user. The project is based on the ABACUS project, which is a high-performance ab initio simulation software for electronic structure calculation.

Features

APNS is designed as a test data generator for ABACUS user. It can generate pseudopotential and numerical atomic orbital test data for ABACUS user.

To run on your local machine

WARNING: In principle APNS is not designed to be run on your local machine. However, if you want to run it on your local machine, you can follow the instructions below.

Prerequisites

Prerequisites differ from workflows, currently there are three workflows. Mutual prerequisites are listed below:

Python 3.6 or higher
numpy
scipy

Following lists prerequisites for each workflow:

Test

urllib3 (< 2.1.0)
mp_api
pymatgen
seekpath

Analysis

matplotlib
lbg (lebegue, developed by Deeptechnology Inc., for downloading groups of jobs from Bohrium Supercomputing Cloud platform)

Orbgen

pytorch
torch_optimizer
torch_complex

Installation

BEFORE INSTALLATION, WE STRONGLY RECOMMEND YOU TO CREATE A NEW VIRTUAL ENVIRONMENT.

python3 -m virtualenv apnsvenv
source apnsvenv/bin/activate

in .gitignore, a line is already set for ignoring virtual environment folder entitled with substr venv. Then you can install the package. To install, run:

pip install .

, add -e to install in editable mode.

pip install -e .

Usage

Very first configuration

For APNS >= 1.0.0, no additional configuration is needed anymore, just run APNS, it will refresh the archive for pseudopotential and numerical atomic orbital (not implemented yet) automatically. However, you need to pay attention to the file ./download/pseudopotentials/rules.json, it is for the configuration of pseudopotential archive. Once you add new kind of pseudopotential, you should always add a new rule to distinguish those pseudopotentials, like:

{
    "rules": [
        {
            "kind": "sg15",
            "version": "1.2",
            "appendix": "fr",
            "re.folder": ".*",
            "re.file": "^([A-Z][a-z]?)(_ONCV_PBE_FR-1\\.0\\.upf)$"
        }
    ]
}

, in which re.folder and re.file are regular expressions for matching the folder and file name of the pseudopotential.

Run APNS

prepare input.json like this:

{
    "global": {
        "test_mode": "pseudopotential",
        "software": "ABACUS",
        "work_dir": "./",
        "pseudo_dir": "./download/pseudopotentials",
        "orbital_dir": "./download/numerical_orbitals",
        "save_log": true
    },
    "calculation": {
        "basis_type": "pw",
        "functionals": ["PBE"],
        "ecutwfc": [100],
        "cal_force": 1,
        "cal_stress": 1,
        "scf_nmax": 500
    },
    "extensive": {
        "characteristic_lengths": [0.0]
    },
    "systems": ["Cs", "Ba", "Hf", "Ta", "W", "Re", "Os", "Ir", "Pt", "Au", "Hg", "Tl", "Pb", "Bi", "Po", "At", "Rn"],
    "materials_project": {
        "api_key": "your Materials Project API key",
        "n_structures": 1,
        "theoretical": false,
        "most_stable": true
    },
    "pseudopotentials": {
        "kinds": ["all"],
        "versions": [""],
        "appendices": [""]
    },
    "numerical_orbitals": {
        "types": ["DZP"],
        "rcuts": [7, 8, 9, 10],
        "appendices": [""]
    }
}

APNS also supports specifying pseudopotentials section for each element:

    "pseudopotentials": {
        "kinds": {"Cs": ["sg15"], "Ba": ["sg15", "dojo"]},
        "versions": {"Cs": ["1.2"], "Ba": ["all"]},
        "appendices": {"Cs": ["fr"], "Ba": [""]},
    },

Then run:

python main.py -i input.json

After jobs are done, you can download with lbg developed by DPTechnology Inc.:

lbg jobgroup download <group_id>

However if it is the first time you run lbg, you need to configure it first:

lbg config account

, your Bohrium account and password are needed. Then edit another file named input_analysis.json:

{
    "global": {
        "test_mode": "analysis",
        "pseudo_dir": "./download/pseudopotentials",
        "orbital_dir": "./download/numerical_orbitals"
    },
    "analysis": {
        "search_domain": "./11845898",
        "items": [
            "driver_EcutwfcConv_20240319"
        ]
    }
}

, in which search_domain should be the folder (either relative or absolute path) in which APNS will search jobs to analysis, and items are the items you want to analyze. APNS encourages users to write their own analysis workflow, once complete, add to the list items, then driver will run them one-by-one. Then run:

python main.py -i input_analysis.json

The element in items should be coded with respect to some workflow regulations, they are:

always have an enter like

import argparse
def entry():
    parser = argparse.ArgumentParser(description="APNS pseudopotential convergence test")
    # add -i
    parser.add_argument("-i", "--input", type=str, help="input json file")
    args = parser.parse_args()
    return args.input

def run():
    path = entry()
    # your code here

if __name__ == "__main__":
    run()

Then driver of analysis workflow will call the analyzer with

def run(finp: str):
    with open(finp, "r") as f:
        inp = json.load(f)

    for item in inp["analysis"]["items"]:
        
        item = item.replace("\\", "/").split("/")[-1]
        item = item + ".py" if not item.endswith(".py") else item
        item = "apns/module_analysis/drivers/" + item
        if os.path.exists(item):
            os.system("python " + item + " -i" + inp["analysis"]["search_domain"])
        else:
            print("Warning: user-defined analysis item \"", item, "\" not found, skip.")

Author information

APNS is mainly developed and maintained by the ABACUS-AISI (Artificial Intelligence for Science Institute, BEIJING) team.

aroundpeking / abacus-pseudopot-nao-square Goto Github PK