Substituent Impact on Quinoxaline Performance and Degradation in Redox Flow Batteries.

Aqueous redox flow batteries (RFBs) are attractive candidates for low-cost, grid-scale storage of energy from renewable sources. Quinoxaline derivatives represent a promising but underexplored class of charge-storing materials on account of poor chemical stability in prior studies (with capacity fade rates >20%/day). Here, we establish that 2,3-dimethylquinoxaline-6-carboxylic acid (DMeQUIC) is vulnerable to tautomerization in its reduced form under alkaline conditions. We obtain kinetic rate constants for tautomerization by applying Bayesian inference to ultraviolet-visible spectroscopic data from operating flow cells and show that these rate constants quantitatively account for capacity fade measured in cycled cells. We use density functional theory (DFT) modeling to identify structural and chemical predictors of tautomerization resistance and demonstrate that they qualitatively explain stability trends for several commercially available and synthesized derivatives. Among these, quinoxaline-2-carboxylic acid shows a dramatic increase in stability over DMeQUIC and does not exhibit capacity fade in mixed symmetric cell cycling. The molecular design principles identified in this work set the stage for further development of quinoxalines in practical, aqueous organic RFBs.

High-throughput screening of tribological properties of monolayer films using molecular dynamics and machine learning.

Monolayer films have shown promise as a lubricating layer to reduce friction and wear of mechanical devices with separations on the nanoscale. These films have a vast design space with many tunable properties that can affect their tribological effectiveness. For example, terminal group chemistry, film composition, and backbone chemistry can all lead to films with significantly different tribological properties. This design space, however, is very difficult to explore without a combinatorial approach and an automatable, reproducible, and extensible workflow to screen for promising candidate films. Using the Molecular Simulation Design Framework (MoSDeF), a combinatorial screening study was performed to explore 9747 unique monolayer films (116 964 total simulations) and a machine learning (ML) model using a random forest regressor, an ensemble learning technique, to explore the role of terminal group chemistry and its effect on tribological effectiveness. The most promising films were found to contain small terminal groups such as cyano and ethylene. The ML model was subsequently applied to screen terminal group candidates identified from the ChEMBL small molecule library. Approximately 193 131 unique film candidates were screened with approximately a five order of magnitude speed-up in analysis compared to simulation alone. The ML model was thus able to be used as a predictive tool to greatly speed up the initial screening of promising candidate films for future simulation studies, suggesting that computational screening in combination with ML can greatly increase the throughput in combinatorial approaches to generate in silico data and then train ML models in a controlled, self-consistent fashion.