ATST-Tools

ATST-Tools is an ASE transition-state workflow toolkit for ABACUS and DeePMD-kit. Run calculations from YAML with the atst command, or use the Python API to integrate the same workflows into another program.

atst run config.yaml

Chinese guide: 中文 · GitHub · Gitee. Examples: GitHub · Gitee.

What You Can Run

Experimental constant-potential fixed-cell relaxation, NEB and ASE-driven MD use the explicit compensated_gate boundary. See the configuration reference for setup and supported boundaries.

Local commands prepare paths and ABACUS inputs, inspect trajectories, and summarize existing results. See the CLI reference. A transition-state candidate can be screened with atst vibration validate using saved vibration results; workflow completion alone does not establish a validated TS. Current support and experimental limits are summarized in the feature matrix.

Installation

Requires Python 3.10 or later:

pip install atst-tools
atst --version

The default installation includes abacuslite>=1.2.1,<2 and Sella; it does not install mpi4py. ABACUS calculations also need an ABACUS executable and the pseudopotential/orbital files referenced by the configuration. DP calculations need a compatible model. Image-parallel runs require a matching MPI runtime; DMF additionally requires cyipopt and IPOPT. See the MPI setup guide for image-parallel setup and the examples guide for backend inputs.

Quick Start

Obtain examples at the tag matching your installed version (shown for 2.3.2):

git clone --branch v2.3.2 https://github.com/QuantumMisaka/atst-tools.git
cd atst-tools

First try a local path-preparation command; it needs no ABACUS, DP model or GPU:

cd examples/09_lightweight_cli
atst neb make inputs/init.xyz inputs/final.xyz 3 -o first_chain.traj --method linear
atst neb summary inputs/neb_result.extxyz --n-max 1 --tail 5
cd ../..

first_chain.traj contains five initial-guess frames. To inspect a calculation configuration, enter its example directory so relative input paths resolve:

cd examples/06_relax_H2-Au
atst config validate config.yaml --print-normalized
atst run --dry-run config.yaml
# After configuring the ABACUS runtime and compute resources:
atst run config.yaml

This example uses four ABACUS MPI ranks, the GPU cusolver solver and a one-step optimization limit. Validation and dry-run do not launch ABACUS; check the resulting convergence status separately. Follow the examples guide to inspect results and choose other methods or DP variants. A complete walkthrough is also available in the Chinese guide.

Documentation

The primary documentation is in English, with a Chinese user guide. The documentation index organizes user, developer and project status material. GitHub is the development source; Gitee is a manually updated mirror. PyPI distributes the Python package; obtain examples from the repository.

References

If you publish work that uses the transition-state workflows in ATST-Tools, please cite the underlying methods alongside ATST-Tools itself:

• ATST-Tools and the double-to-single (D2S) transition-state workflow: Liu, Z.-Q.; Deng, Z.; Zhao, H.; Wang, H.; Chen, M.; Jiang, H. FT²DP: Large Atomic Model Fine-Tuned Machine Learning Potential for Accelerating Atomistic Simulation of Iron-Based Fischer–Tropsch Synthesis. J. Mater. Inform. 5 (2), 27 (2025). https://doi.org/10.20517/jmi.2024.105
• Sella saddle-point search: Hermes, E. D.; Sargsyan, K.; Najm, H. N.; Zádor, J. Sella, an Open-Source Automation-Friendly Molecular Saddle Point Optimizer. J. Chem. Theory Comput. 18 (11), 6974-6988 (2022). https://doi.org/10.1021/acs.jctc.2c00395
• CCQN (cone-shaped constrained quasi-Newton) transition-state optimization: Wu, Y.; Wang, H. Cone-Shaped Constrained Quasi-Newton Method: Efficient and Robust Single-Ended Transition State Optimization Algorithm. J. Chem. Theory Comput. 21 (18), 9054-9065 (2025). https://doi.org/10.1021/acs.jctc.5c01015

License

LGPL-v3 License.