{"id":48600732,"url":"https://github.com/asif-iqbal-bhatti/vasp-grace-tensorpotential","last_synced_at":"2026-04-25T10:06:02.673Z","repository":{"id":349978188,"uuid":"1204695779","full_name":"Asif-Iqbal-Bhatti/vasp-grace-tensorpotential","owner":"Asif-Iqbal-Bhatti","description":"A wrapper to use GRACE MLIP as a drop-in replacement for VASP backend","archived":false,"fork":false,"pushed_at":"2026-04-08T14:53:40.000Z","size":9239,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-04-08T16:30:05.567Z","etag":null,"topics":["ase","grace-tensorpotential","mace-torch","mlip","vasp"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Asif-Iqbal-Bhatti.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2026-04-08T08:42:15.000Z","updated_at":"2026-04-08T14:54:31.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/Asif-Iqbal-Bhatti/vasp-grace-tensorpotential","commit_stats":null,"previous_names":["asif-iqbal-bhatti/vasp-grace","asif-iqbal-bhatti/vasp-grace-tensorpotential"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/Asif-Iqbal-Bhatti/vasp-grace-tensorpotential","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Asif-Iqbal-Bhatti%2Fvasp-grace-tensorpotential","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Asif-Iqbal-Bhatti%2Fvasp-grace-tensorpotential/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Asif-Iqbal-Bhatti%2Fvasp-grace-tensorpotential/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Asif-Iqbal-Bhatti%2Fvasp-grace-tensorpotential/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Asif-Iqbal-Bhatti","download_url":"https://codeload.github.com/Asif-Iqbal-Bhatti/vasp-grace-tensorpotential/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Asif-Iqbal-Bhatti%2Fvasp-grace-tensorpotential/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31575755,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-08T14:31:17.711Z","status":"ssl_error","status_checked_at":"2026-04-08T14:31:17.202Z","response_time":54,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ase","grace-tensorpotential","mace-torch","mlip","vasp"],"created_at":"2026-04-08T22:01:39.932Z","updated_at":"2026-04-25T10:06:02.666Z","avatar_url":"https://github.com/Asif-Iqbal-Bhatti.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# vasp-grace\n\n`vasp-grace` is a lightweight Python package that allows you to use the **GRACE Machine Learning Potentials** as a drop-in replacement for VASP. By mimicking the VASP executable, it seamlessly integrates GRACE with higher-level materials science workflows (e.g., Phonopy, USPEX, CALYPSO) that normally parse VASP inputs and outputs.\n\n## Features\n- **Direct VASP Compatibility:** Reads standard `POSCAR` and `INCAR` files.\n- **Modular Architecture:** Organized into logical modules for easy extension and maintenance.\n- **Geometry Optimization:** Supports VASP's `IBRION` and `ISIF` tags for structural optimization natively via ASE.\n- **Molecular Dynamics:** Supports multiple ensemble types (NVE, NVT, NPT) via ASE.\n- **Phonon Calculations:** Finite-displacement phonons with band structure and DOS plotting.\n- **NEB:** Nudged Elastic Band calculations using ASE.\n- **Output Generation:** Writes standard `OUTCAR`, `OSZICAR`, and `CONTCAR` files mimicking VASP output structures.\n- **Backend:** Native Python integration using the GRACE `tensorpotential` ASE calculator (GPU accelerated via TensorFlow).\n- **pip-installable:** Install as a standard Python package with `pip install .`\n\n## Installation\n\n### Quick Start\n\n1. Install the package:\n   ```bash\n   pip install -e .\n   ```\n\n2. Use from command line:\n   ```bash\n   vasp-grace --poscar POSCAR --incar INCAR\n   ```\n\n### Detailed Instructions\n\nSee [INSTALL.md](INSTALL.md) for comprehensive installation and usage guide.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fasif-iqbal-bhatti%2Fvasp-grace-tensorpotential","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fasif-iqbal-bhatti%2Fvasp-grace-tensorpotential","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fasif-iqbal-bhatti%2Fvasp-grace-tensorpotential/lists"}