Transfer Learning of Full Molecular Weight Distributions via High-Throughput Computer-Controlled Polymerization.

The skew and shape of the molecular weight distribution (MWD) of polymers have a significant impact on polymer physical properties. Standard summary metrics statistically derived from the MWD only provide an incomplete picture of the polymer MWD. Machine learning (ML) methods coupled with high-throughput experimentation (HTE) could potentially allow for the prediction of the entire polymer MWD without information loss. In our work, we demonstrate a computer-controlled HTE platform that is able to run up to 8 unique variable conditions in parallel for the free radical polymerization of styrene. The segmented-flow HTE system was equipped with an inline Raman spectrometer and offline size exclusion chromatography (SEC) to obtain time-dependent conversion and MWD, respectively. Using ML forward models, we first predict monomer conversion, intrinsically learning varying polymerization kinetics that change for each experimental condition. In addition, we predict entire MWDs including the skew and shape as well as SHAP analysis to interpret the dependence on reagent concentrations and reaction time. We then used a transfer learning approach to use the data from our high-throughput flow reactor to predict batch polymerization MWDs with only three additional data points. Overall, we demonstrate that the combination of HTE and ML provides a high level of predictive accuracy in determining polymerization outcomes. Transfer learning can allow exploration outside existing parameter spaces efficiently, providing polymer chemists with the ability to target the synthesis of polymers with desired properties.

Charge mobility and recombination in a new hole transporting polymer and its photovoltaic blend.

The charge mobility in a new hole transporting polymer, poly(2,6-bis(thiophene-2-yl)-3,5-dipentadecyldithieno[3,2-b;2',3'-d]thiophene) (PBTDTT-15), and its blend with (6,6)-phenyl-C(70)-butyric acid methyl ester (PC(70)BM) in a weight ratio of 1:3 at ambient atmosphere condition was investigated using time-of-flight (TOF) photoconductivity and photoinduced charge extraction by linearly increasing voltage (PhotoCELIV) techniques. The bulk heterojunction based photovoltaic (PV) blend (PBTDTT-15:PC(70)BM (1:3)) exhibited a promising power conversion efficiency (PCE) of 3.23% under air mass 1.5 global (AM 1.5G) illumination of 100mW/cm(2). The charge mobility and recombination properties of the best performing cells were investigated. The hole mobility in the pure PBTDTT-15 was in the range of 4 x 10(-4) cm(2)/(V s), which was reduced almost 5 times in the PBTDTT-15:PC(70)BM (1:3) blend. The PhotoCELIV transient observed for the photovoltaic (PV) blend was dominated by electrons, with the charge mobility of the order of 10(-3) cm(2)/(V s), and a weak shoulder at a long time scale due to holes. The effective bimolecular recombination coefficient (beta) obtained for the PV blend deviated significantly from the Langevin recombination coefficient (beta(L)) indicating a phase-separated morphology. The obtained results indicate that the PBTDTT-15:PC(70)BM blend can be potential for organic solar cell applications.