TASTET — Tool for Atomistic Structure selection Through Efficient Triage

TASTET is a Python toolkit for selecting representative structures from molecular and materials datasets. At its core it does three things:

1. Parse input structures into an ASE database.
2. Represent each structure in a similarity kernel space encoded by SOAP descriptors.
3. Select a space-filling subset of structures for high-fidelity calculations.

Two secondary tools support this workflow: kernel-PCA visualization of the kernel space, and unsupervised/supervised hyperparameter grid searches that tune the SOAP and kernel representation.

TASTET is built on ASE (structures and databases) and DScribe (SOAP descriptors).

Documentation

Full documentation (getting started, API reference, and use-case walkthroughs) is hosted at tastet.readthedocs.io.

Installation

pip install tastet               # from PyPI
pip install -e .                 # library only, from a clone
pip install -e ".[examples]"     # also run the bundled examples (installs RDKit)
pip install -e ".[docs]"         # also build the Sphinx docs

Quickstart

import pandas as pd
from ase.io import read
from tastet.soap_utils import compute_soap
from tastet.kernel import compute_kernel
from tastet.selection import select_structures

structures = read("structures.traj", index=":")

# Represent the structures in a SOAP-encoded kernel space
soap_list = compute_soap(structures, r_cut=4.0, n_max=6, l_max=6, sigma=0.1)
K = compute_kernel(soap_list, method="rematch", metric="linear", alpha=0.5)

# Select 10 space-filling representatives directly from the kernel space
meta = pd.DataFrame({"configuration_id": range(1, len(structures) + 1)})
selected, pool, selected_indices = select_structures(K, meta, k=10, method="fps")
print(selected_indices)

Use cases

Two complete, self-contained example pipelines live under examples/:

• examples/nanoclusters/ — Cu nanoclusters on a surface (single-kernel mode).
• examples/rh_complex/ — Rh complex conformers (tensor-product mode, with a supervised round-2 workflow).

Each is driven by three scripts — config.py, prepare.py, and run.py — sharing the same CLI steps (db, grid_search, soap, kernel, kpca, select). See the Use Cases docs for worked walkthroughs.

Package structure

tastet/
├── soap_utils.py    # compute_soap — SOAP descriptors
├── kernel.py        # compute_kernel — average / REMatch kernels
├── kpca.py          # fit_kpca — KernelPCA wrapper + KPCAResult dataclass
├── io.py            # database, SOAP, and kernel save/load helpers
├── distance.py      # kernel-induced distance distributions
├── selection.py     # diverse structure selection (FPS / k-medoids)
├── pipeline.py      # shared soap/kernel/kpca/grid_search/select steps
├── cka.py           # centered kernel alignment scoring
├── metrics/         # scorer protocol + CKA scorer
├── sweep/           # SOAP × kernel grid search (single- and multi-channel)
└── plotting/        # kPCA scatter, heatmaps, distance plots, styling

Citation

If you use TASTET in your work, please cite the accompanying preprint on ChemRxiv: doi:10.26434/chemrxiv.15008498/v1.

License

TASTET is released under the MIT License — see LICENSE. © 2026 Alejandro Cañete-Arché and the CCEM Group.