The progression from matter to life on Earth must have involved something akin to Darwinian evolution: molecules became increasingly more complex and functional and eventually organized themselves into self-replicating systems. To investigate the principles of chemical evolution necessary for this complexification and organization, this grant supports efforts by Wilhelm Huck, a Professor of Physical Organic Chemistry (Radboud University, NL), to develop an experimental platform capable of ‘prebiotic evolution.’ Huck intends to develop a machine-learning based and robotic ‘chemical evolution machine’ that leverages changes in the (experimental) environment to evolve chemical networks towards functions important to life. The project focuses on a prominent prebiotic chemical reaction (formose) and aims to evolve networks that achieve three goals regarded as important to the rise of life on Earth: enhanced yield of ribose (a key building block of RNA); self-catalysis (the emergence of molecules (catalysts) within a network that enhance the chemical reactivity of the network); and self-compartmentalization (the emergence of compartments that encapsulate the chemical mixture from which the compartments emerge). Huck and his team will leverage a machine-learning guided robotic system to evolve chemical networks towards targeted properties. The experimental apparatus will expose chemical systems to various conditions (reactant and catalyst choices and concentrations, variations in temperature and pH) chosen by the learning algorithms. Networks will be selected based on how closely they approximate a targeted property (ribose yield, self-catalysis, self-compartmentalization). This process will be repeated again and again, and it’s expected that the learning algorithms will become increasingly more effective at identifying conditions that lead to the targeted property. A successful project will uncover the mechanisms by which environment change nudges a formose chemical network towards functionality, while also establishing a workflow that can be used to discover how other chemical networks did or could achieve functions important to living systems.