Uncovering Molecular Structure – Redox Potential Relationships for Organic Electrode Materials: A Hybrid DFT – Machine Learning Approach

Abstract

Density functional theory and machine learning are used to investigate the structure-electrochemical performance relationships of organic moieties for use in Li-ion batteries. Namely, DFT calculations are performed to predict the redox potential of several novel organic molecules with an accuracy within ~0.1 V of experimental measurements. However, despite its ability to provide valuable insight regarding the electrochemical properties of novel organic molecules, our high efficacy DFT modeling protocol demands significant computational time and is therefore impractical for the vast screening of novel material candidate. As a result, we explore machine learning as a strategy for the accelerated discovery of novel organic materials. More critically, we use machine learning as a method for assessing the various structure-electrochemical relationships which can provide a more general guideline for the design of organic electrode materials. We are employing different learning models, including artificial neural networks, gradient-boosting regression, and kernel methods (such as kernel ridge regression and Gaussian process regression), via three different pipelines with varying sophistication with the aim of generating an advanced ML scheme for the accurate prediction and analysis of electrochemical activity. In addition to incorporating structural fingerprints, we are exploring an active learning framework, namely using the efficient global optimization scheme, to explore the materials space strategically using publicly available datasets. Through this approach, we can discover new electrode materials that could 1) have a higher probability for achieving enhanced electrochemical properties and 2) increase our learning model’s performance by increasing our dataset’s representation of the material space. Additionally, we are implementing a high-throughput virtual screening (HTVS) pipeline which consists of several surrogate learning models with increasing levels of fidelity. The material candidates are pruned at each stage to discard samples that are unlikely to possess a desirable redox potential for cathodic application, and the remaining samples move on to the next model. In this way, only a subset of the original dataset which has a higher probability for lying within the desired redox potential range is pursued using the most computationally expensive model.