dscribe

What it is and what it does

DScribe is a materials-science-focused Python library that converts atomic structures into fixed-size numerical descriptors suitable for machine learning pipelines. It implements multiple descriptor types—including SOAP (Smooth Overlap of Atomic Positions), Coulomb matrix, ACSF (Atom-centered Symmetry Functions), and MBTR (Many-body Tensor Representation)—each capturing different aspects of atomic geometry and chemistry. The library wraps numpy, scipy, and scikit-learn for numerical computation and can parallelize descriptor creation across multiple processes via joblib.

Typical use involves loading or constructing atomic structures (via ASE), instantiating a descriptor class with domain-specific parameters, and calling create() to generate fixed-size feature vectors or sparse arrays. The package supports both spectrum calculation and derivative computation with respect to atomic positions, making it suitable for force-field fitting and sensitivity analysis. It targets materials scientists and machine-learning practitioners working on atomistic property prediction, structure similarity, and visualization tasks.

Use it for:

• Generate SOAP descriptors for training machine-learning models to predict material properties from atomic structure.
• Compute Coulomb matrices for similarity-based clustering or retrieval of structurally related molecules and crystals.
• Calculate descriptor derivatives to fit interatomic potentials or analyze sensitivity of predictions to atomic positions.
• Parallelize descriptor creation across multiple CPU cores when processing large datasets of structures.
• Visualize and compare atomic structures in a fixed-dimensional descriptor space for exploratory analysis.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

DScribe transforms atomic structures into fixed-size numerical fingerprints (descriptors) for use in machine learning, visualization, and similarity analysis of materials.

Yes, with conditions. DScribe is production-stable and permissively licensed, with pre-built wheels for modern Python versions and no known vulnerabilities. Install friction is moderate due to compiled dependencies and a dependency chain of 6 packages. The 321-day release gap and aging maintenance status suggest it is stable but not actively developed; suitable for established workflows but verify compatibility with your specific scipy/scikit-learn versions before adoption in new projects.

Install

dscribe on PyPI

pip install dscribe

uv add dscribe

poetry add dscribe

Installing dscribe

Before you install

Medium install friction due to compiled C/C++ components and multiple dependencies (numpy, scipy, ase, scikit-learn, joblib, sparse). Pre-built wheels available for Python 3.9–3.13 on macOS, Linux, and Windows. Last release was 321 days ago; maintenance status is aging.

License in practice

Apache License 2.0 is permissive and allows commercial use, modification, and distribution with minimal restrictions. You must include a copy of the license and state significant changes.

Quickstart

pip install dscribe

from ase.build import molecule
from dscribe.descriptors import SOAP

water = molecule("H2O")
soap_desc = SOAP(species=["H", "O"], r_cut=5, n_max=8, l_max=6)
soap = soap_desc.create(water, centers=[0])

Requires ASE (Atomic Simulation Environment) to construct or load atomic structures; compiled C/C++ extensions require a compatible build toolchain on source installs.

Verify before relying

• Whether the package is actively maintained or in maintenance-only mode given the 321-day gap since last release.
• Performance characteristics and scalability limits for large numbers of structures or high-dimensional descriptor spaces.
• Compatibility with recent versions of scipy and scikit-learn beyond what the fact sheet confirms.

Package facts

Evidence: dscribe-2.1.2-cp310-cp310-macosx_10_9_x86_64.whl; dscribe-2.1.2-cp310-cp310-macosx_11_0_arm64.whl; dscribe-2.1.2-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; dscribe-2.1.2-cp310-cp310-musllinux_1_2_x86_64.whl; dscribe-2.1.2-cp310-cp310-win32.whl; dscribe-2.1.2-cp310-cp310-win_amd64.whl; dscribe-2.1.2-cp311-cp311-macosx_10_9_x86_64.whl; dscribe-2.1.2-cp311-cp311-macosx_11_0_arm64.whl; dscribe-2.1.2-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; dscribe-2.1.2-cp311-cp311-musllinux_1_2_x86_64.whl; dscribe-2.1.2-cp311-cp311-win32.whl; dscribe-2.1.2-cp311-cp311-win_amd64.whl; dscribe-2.1.2-cp312-cp312-macosx_10_13_x86_64.whl; dscribe-2.1.2-cp312-cp312-macosx_11_0_arm64.whl; dscribe-2.1.2-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; dscribe-2.1.2-cp312-cp312-musllinux_1_2_x86_64.whl; dscribe-2.1.2-cp312-cp312-win32.whl; dscribe-2.1.2-cp312-cp312-win_amd64.whl; dscribe-2.1.2-cp313-cp313-macosx_10_13_x86_64.whl; dscribe-2.1.2-cp313-cp313-macosx_11_0_arm64.whl

Keywords: descriptor, machine learning, atomistic structure, materials science

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