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We propose a multiple descriptor multiple kernel (MultiDK) method forefficient molecular discovery using machine learning. We show that the MultiDKmethod improves both the speed and the accuracy of molecular propertyprediction. We apply the method to the discovery of electrolyte molecules foraqueous redox flow batteries. Using \emph{multiple-type - as opposed tosingle-type - descriptors}, more relevant features for machine learning can beobtained. Following the principle of the 'wisdom of the crowds', thecombination of multiple-type descriptors significantly boosts predictionperformance. Moreover, MultiDK can exploit irregularities between molecularstructure and property relations better than the linear regression method byemploying multiple kernels - more than one kernel functions for a set of theinput descriptors. The multiple kernels consist of the Tanimoto similarityfunction and a linear kernel for a set of binary descriptors and a set ofnon-binary descriptors, respectively. Using MultiDK, we achieve averageperformance of $r^2 = 0.92$ with a set of molecules for solubility prediction.We also extend MultiDK to predict pH-dependent solubility and apply it tosolubility estimation of quinone molecules with ionizable functional groups asstrong candidates of flow battery electrolytes.

MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular  Discovery and Its Application to The Discovery of Organic Flow Battery  Electrolytes