Multi-fidelity integrated computational-experimental design of self-assembling π-conjugated optoelectronic peptides

In this work we employ multi-fidelity Bayesian optimization to fuse experimental and computational datastreams for the design of self-assembling π-conjugated peptides with emergent optoelectronic properties. We consider a family of peptides composed of a central π-core flanked by oligopeptide wings that have been demonstrated to self-assemble into supramolecular pseudo-1D nanoaggregates with emergent optoelectronic properties. Exhaustive traversal of the molecular design space of π-cores and peptide wings by either simulation or experiment is prohibitively expensive. This motivated the construction of a multi-fidelity Bayesian optimization platform to fuse cheap, high-volume, and approximate simulation data with expensive, low-volume, and accurate experimental data to rationally traverse the design space and efficiently identify molecules with engineered optoelectronic properties. New molecules identified by this active learning platform for experimental synthesis and testing yield superior optoelectronic properties compared to the best performing previous candidates.