{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/9"},{"label":"Physics","url":"https://skillfed.io/packages/category/scientific-engineering-physics"}],"enrichment":{"capability":"DScribe transforms atomic structures into fixed-size numerical fingerprints (descriptors) for use in machine learning, visualization, and similarity analysis of materials.","skillfed_tags":["materials-science","descriptor-generation","machine-learning-features"],"use_cases":["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."],"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\u2014including SOAP (Smooth Overlap of Atomic Positions), Coulomb matrix, ACSF (Atom-centered Symmetry Functions), and MBTR (Many-body Tensor Representation)\u2014each 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.\n\nTypical 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.","worth_installing":"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."},"id":"dscribe","links":{"html":"https://skillfed.io/packages/dscribe","md":"https://skillfed.io/packages/dscribe.md","pypi":"https://pypi.org/project/dscribe/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-09-27","license_spdx":null,"license_treatment":"permissive","name":"dscribe","python_support":"supports_current","summary":"A Python package for creating feature transformations in applications of machine learning to materials science."},"popularity":{"monthly_downloads":76695,"position":14599,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.1.2"}