Active learning identifies optimal π-conjugated peptide chemistries for optoelectronics

In this work we perform active learning discovery within an embedded chemical space of pi-conjugated peptides using coarse-grained molecular dynamics simulation to discover molecules with emergent optoelectronic behavior. Molecules with oligopeptide wings flanking a pi-conjugated core have surfaced as an extensible building block for self-assembling electronic devices due to overlaps between pi-orbitals in supramolecular assemblies leading to optical and electronic properties with the potential to operate in bio-compatible frameworks. However, a combinatorial explosion in the molecular design space of possible peptide sequences render brute force trial-and-error discovery impossible through either experiment or simulation. By deploying an activate learning procedure over a variational autoencoder learned space of pi-conjugated peptides molecules are iteratively selected for computational screening by balancing exploration of undersampled regions and exploitation in high confidence regions of chemical space. This protocol efficiently navigates this large chemical space to ultimately identify promising pi-conjugated peptide chemistres with optimal optoelectronic behavior for further computational testing and experimental synthesis.