This  project  integrates  experiment,  simulation,  and  data-science  to  engineer  supramolecular optoelectronic peptidic semiconductors.

Hybrid   computational-experimental   screening.  We  established  a  multi-fidelity  machine learning-enabled  screening  platform  for  the  data-driven  discovery  of  novel  π-conjugated peptides with superior optoelectronic properties [1]. All-atom molecular dynamics simulations and experimental UV-vis spectroscopy measurements were operated as parallel screening loops and used to train hybrid computational-experimental data-driven model. The model was used with Bayesian optimization to iteratively design the next round of molecules for computational and experimental screening. After testing 1181 molecules by simulation and 28 by experiment, we discovered and validated two novel molecules with superior spectral properties.

Extensions to “high-performance” peptidic organic semiconductors. Much of our past work has entailed the development of careful structure-property relationships of pi-peptide assembly based upon well-established pi-electron core structures.  Over the past year, we extended our suite  of  pi-electron  core  structures  into  more  high-performance  n-type  (electron  transporting) materials.    We  extended  our  Pd-mediated  on-resin  dimerization  synthesis  approach  to  the creation  of  diketopyrrolopyrrole  (DPP)  cores,  a  common  motif  encountered  in  many  current photovoltaic materials due to the excellent charge transporting ability of the DPP core [2].  Varied alkyl groups could be included without negatively impacting photophysical properties.