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.

Ordering-Dependent Hydrogen Evolution and Oxygen Reduction Electrocatalysis of High-Entropy Intermetallic Pt4 FeCoCuNi.

Disordered solid-solution high-entropy alloys have attracted wide research attention as robust electrocatalysts. In comparison, ordered high-entropy intermetallics have been hardly explored and the effects of the degree of chemical ordering on catalytic activity remain unknown. In this study, a series of multicomponent intermetallic Pt4 FeCoCuNi nanoparticles with tunable ordering degrees is fabricated. The transformation mechanism of the multicomponent nanoparticles from disordered structure into ordered structure is revealed at the single-particle level, and it agrees with macroscopic analysis by selected-area electron diffraction and X-ray diffraction. The electrocatalytic performance of Pt4 FeCoCuNi nanoparticles correlates well with their crystal structure and electronic structure. It is found that increasing the degree of ordering promotes electrocatalytic performance. The highly ordered Pt4 FeCoCuNi achieves the highest mass activities toward both acidic oxygen reduction reaction (ORR) and alkaline hydrogen evolution reaction (HER) which are 18.9-fold and 5.6-fold higher than those of commercial Pt/C, respectively. The experiment also shows that this catalyst demonstrates better long-term stability than both partially ordered and disordered Pt4 FeCoCuNi as well as Pt/C when subject to both HER and ORR. This ordering-dependent structure-property relationship provides insight into the rational design of catalysts and stimulates the exploration of many other multicomponent intermetallic alloys.