Ensemble Analyzer: An Open-Source Python Framework for Automated Conformer Ensemble Refinement
Abstract
Accurate prediction of molecular properties often requires considering the full conformational ensemble rather than a single optimized structure. While modern sampling tools have revolutionized conformational sampling by enabling the rapid generation of ensembles, the subsequent refinement at higher levels of theory remains computationally demanding and technically complex. Existing workflows typically rely on ad hoc scripts and manual intervention, limiting reproducibility and accessibility. Here, we present Ensemble Analyzer (EnAn), an open-source Python framework designed to automate the refinement and analysis of conformational ensembles. Built on the Atomic Simulation Environment (ASE), EnAn integrates seamlessly with widely known quantum chemistry engines such as ORCA and Gaussian, providing a modular and extensible architecture that streamlines the entire pipeline. EnAn also supports automated generation and comparison of electronic and vibronic spectra, enabling rapid visualization and interpretation. By minimizing manual data handling and standardizing workflows, EnAn effectively manages reproducible exploration of complex conformational spaces.
Fields of Study & Subjects
Machine Learning in Materials Science
Computational Drug Discovery Methods
Molecular spectroscopy and chirality
Publication details
- Journal / source
- ChemRxiv
- Publisher
- American Chemical Society (ACS)
- Publication date
- 5 January 2026
- Document type
- Preprint