QUESTS (Quick Uncertainty and Entropy via STructural Similarity) is a Python library providing model-free uncertainty and entropy estimation methods for interatomic potentials. It employs a structural descriptor and information-theoretical strategy that is fast to compute, relying only on distances between atoms within an environment. The library is actively maintained, with its latest version being 2026.2.22, and supports Python 3.8 and newer. It's primarily used for analyzing datasets in atomistic machine learning, offering metrics like dataset entropy, diversity, and information gap.
pip install questsVerified import paths — ran on the pinned version, not inferred.
This quickstart demonstrates how to generate atomic descriptors from a list of `ase.Atoms` objects and then calculate the dataset's entropy. The `ase` library is a core dependency for handling atomic structures. The `rc` parameter (cutoff radius) in `get_atomic_descriptors` is crucial and should be chosen appropriately for your specific materials system.
Ensure `torch` is installed if you intend to use `quests.gpu` modules. Otherwise, use functions from `quests.entropy` for CPU-based computation. `pip install torch` or `pip install quests[gpu]`.
Carefully consider the physical context of your materials when choosing `rc`. It should typically be larger than the longest bond lengths in your system but not excessively large to avoid unnecessary computation. Refer to the documentation or literature on structural descriptors for guidance on appropriate values for your specific application.