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Summary Lithium ion cells, when cycled, exhibit a two‐stage degradation behavior characterized by a first linear stage and a second nonlinear stage where degradation is rapid. The multitude of degradation phenomena occurring in lithium ion batteries complicates the understanding of this two‐stage degradation behavior. In this work, a simple and intuitive model is presented to analyze the coupled effect of resistance growth and the shape of the state of charge (SOC)‐open circuit voltage (OCV) relationship in representing the complete degradation behavior. The model simulations demonstrate that a single resistance that increases linearly on cycling can capture the transition from slow to fast degradation, primarily due to the shape of the SOC‐OCV curve. Further, the model simulations indicate that the shape of the degradation curve depends strongly on the magnitude of current at the end of discharge of the cycling protocol. To verify these observations, specific experiments are designed with minimal capacity loss but with shrinking operating voltage ranges that result in shrinking operating OCV range. The results of the experiments validate the observations of model simulations. Further, long‐term cycling experiment with a commercial lithium ion cell shows that the operating OCV range shrinks substantially with aging and is a major reason for the observed accelerated degradation. The analysis of the present work provides significant insights towards developing simple semiempirical models suitable for battery life management in microcontrollers.

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Much of computational materials science has focused on fast and accurate forward predictions of materials properties, for example, given a molecular structure predict its electronic properties. This is achieved with first principles calculations and more recently through machine learning approaches, since the former is computation-intensive and not practical for high-throughput screening. Searching for the right material for any given application, though follows an inverse path—the desired properties are given and the task is to find the right materials. Here we present a deep learning inverse prediction framework, Structure Learning for Attribute-driven Materials Design Using Novel Conditional Sampling (SLAMDUNCS), for efficient and accurate prediction of molecules exhibiting target properties. We apply this framework to the computational design of organic molecules for three applications, organic semiconductors for thin-film transistors, small organic acceptors for solar cells and electrolyte additives with high redox stability. Our method is general enough to be extended to inorganic compounds and represents an important step in deep learning based completely automated materials discovery.