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Here, we make use of Bayesian statistics to create a normative framework that automates an observation-inference-design loop to run "optimal" experiments which converge on a set of parameter values given a strong guiding model. 

Chemical networks describe the web of intermediates between reactants and products, increasing exponentially with tree depth. In this work, we identify "optimal" reaction pathways to navigate such pathways based on differing criteria - minimizing cost, maximizing energy efficiency, minimizing environmental impact, amongst others.