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

TL;DR: The MultiDK method improves both the speed and accuracy of molecular property prediction and applies the method to the discovery of electrolyte molecules for aqueous redox flow batteries to obtain more relevant features for machine learning.

Abstract: We propose a multiple descriptor multiple kernel (MultiDK) method for efficient molecular discovery using machine learning. We show that the MultiDK method improves both the speed and accuracy of molecular property prediction. We apply the method to the discovery of electrolyte molecules for aqueous redox flow batteries. Using multiple-type—as opposed to single-type—descriptors, we obtain more relevant features for machine learning. Following the principle of “wisdom of the crowds”, the combination of multiple-type descriptors significantly boosts prediction performance. Moreover, by employing multiple kernels—more than one kernel function for a set of the input descriptors—MultiDK exploits nonlinear relations between molecular structure and properties better than a linear regression approach. The multiple kernels consist of a Tanimoto similarity kernel and a linear kernel for a set of binary descriptors and a set of nonbinary descriptors, respectively. Using MultiDK, we achieve an average performance of ...