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Metformin is used globally to treat type II diabetes, has demonstrated anti-ageing and COVID mitigation effects and is a major anthropogenic pollutant to be bioremediated by wastewater treatment plants (WWTPs). Metformin is not adsorbed well by activated carbon and toxic N-chloro derivatives can form in chlorinated water. Most earlier studies on metformin biodegradation have used wastewater consortia and details of the genomes, relevant genes, metabolic products, and potential for horizontal gene transfer are lacking. Here, two metformin-biodegrading bacteria from a WWTP were isolated and their biodegradation characterized. Aminobacter sp. MET metabolized metformin stoichiometrically to guanylurea, an intermediate known to accumulate in some environments including WWTPs. Pseudomonas mendocina MET completely metabolized metformin and utilized all the nitrogen atoms for growth. Pseudomonas mendocina MET also metabolized metformin breakdown products sometimes observed in WWTPs: 1-N-methylbiguanide, biguanide, guanylurea, and guanidine. The genome of each bacterium was obtained. Genes involved in the transport of guanylurea in Aminobacter sp. MET were expressed heterologously and shown to serve as an antiporter to expel the toxic guanidinium compound. A novel guanylurea hydrolase enzyme was identified in Pseudomonas mendocina MET, purified, and characterized. The Aminobacter and Pseudomonas each contained one plasmid of 160 kb and 90 kb, respectively. In total, these studies are significant for the bioremediation of a major pollutant in WWTPs today. 

Data‐driven, machine learning (ML)‐assisted approaches have been used to study structure‐property relationships at the atomic scale, which have greatly accelerated the screening process and new material discovery. However, such approaches are not easily applicable to modulating material properties of a soft material in a laboratory with specific ingredients. Moreover, it is desirable to relate material properties directly to the experimental recipes. Herein, a data‐driven approach to tailoring mechanical properties of a soft material is demonstrated using ML‐assisted predictions of mechanical properties based on experimental synthetic recipes. Polyurethane (PU) elastomer is used as a model soft material to demonstrate the approach and experimentally varied mechanical properties of the PU elastomer by modulating the mixing ratio between components of the elastomer. Twenty‐five experimental conditions are selected based on the design of experiment and use those data points to train a linear regression model. The resulting model takes desired mechanical properties as input and returns synthetic recipes of a soft material, which is subsequently validated by experiments. Lastly, the prediction accuracies of different machine learning algorithms is compared. It is believed that the approach is widely applicable to other material systems to establish experimental conditions and material property relationships for soft materials.