We present an open-source chemoinformatic workflow to generate and analyze complex abiological chemical networks to discover novel compounds and autocatalytic processes. We demonstrate this pipeline's capabilities against a well-studied model system.

Prebiotic chemistry often involves the study of complex systems of chemical reactions that form large networks with a large number of diverse species. Such complex systems may have given rise to emergent phenomena that ultimately led to the origin of life on Earth. The environmental conditions and processes involved in this emergence may not be fully recapitulable, making it difficult for experimentalists to study prebiotic systems in laboratory simulations. Computational chemistry offers efficient ways to study such chemical systems and identify the ones most likely to display complex properties associated with life. Here, we review tools and techniques for modelling prebiotic chemical reaction networks and outline possible ways to identify self-replicating features that are central to many origin-of-life models.

Relativistic effects have a profound impact on the electronic structure of the heaviest elements, and consequently on their chemical and physical properties. In order to investigate these effects, we developed an ion mobility spectrometer equipped with a cryogenic drift tube to measure the reduced mobilities of the ions of monoatomic lanthanides and actinides. As a benchmark, we conducted the first systematic study of lutetium cations ( Lu + ) drifting in helium at 298 K, resolving ground and metastable electronic states using electronic state chromatography. We compared the state-specific mobilities with predictions based on state-of-the-art calculations, and they show very good agreement. Our work paves the way for atomic structure studies in the region of the superheavy elements and provides a powerful experimental approach for exploring ion-neutral interactions and electronic configuration effects in heavy element systems.