ORCA is a versatile ab initio quantum chemistry software package for electronic structure calculations and molecular property prediction. Recent releases have introduced substantial performance improvements, including algorithmic speedups, optimized parallel efficiency, and enhanced memory management. In addition, ORCA’s methodological capabilities have been expanded across electronic structure methods, molecular property calculations, and multi-scale approaches. These developments enable accurate and efficient simulations of systems with increasing size and complexity and facilitate the generation of large, high-quality datasets, such as Open Molecules 2025 (OMol25).[1]
In this context, seamless integration of quantum chemistry software into modern Python-based workflows has become increasingly important. To address this need, we have developed the open-source ORCA Python Interface (OPI),[2] which provides direct programmatic access to ORCA’s extensive methodological toolkit from within Python. OPI offers an ORCA-native abstraction of methods, basis sets, and calculation types, enabling fine-grained control over ORCA-specific functionality through a modular, object-oriented API.
In this talk, we highlight how ORCA can be used together with OPI to build efficient quantum-chemical workflows. Application examples demonstrate how ORCA, in combination with OPI, enables automated high-throughput calculations across large molecular libraries as well as detailed analyses of chemical phenomena based on quantum-chemical data.
Christoph Plett and Anneke Dittmer