Data science, hailed as the fourth paradigm of science, is a rapidly growing field which has served to revolutionize the fields of bio-informatics and climate science and can provide significant speed improvements in the discovery of new materials, mechanisms, and simulations. Data science techniques are often used to analyze and predict experimental data, but they can also be used with simulated data to create surrogate models. Chief among the data science techniques in this application is machine learning (ML), which is an effective means for creating a predictive relationship between input and output vector pairs. Physics-based battery models, like the comprehensive pseudo-two-dimensional (P2D) model, offer increased physical insight, increased predictability, and an opportunity for optimization of battery performance which is not possible with equivalent circuit (EC) models. In this work, ML-based surrogate models are created and analyzed for accuracy and execution time. Decision trees (DTs), random forests (RFs), and gradient boosted machines (GBMs) are shown to offer trade-offs between training time, execution time, and accuracy. Their ability to predict the dynamic behavior of the physics-based model are examined and the corresponding execution times are extremely encouraging for use in time-critical applications while still maintaining very high (∼99%) accuracy. Data science, also known as data-intensive scientific discovery, is hailed as the fourth paradigm of science.1 A field focused on extracting knowledge or understanding from data, it includes the subdomains of machine learning, classification, data mining, databases, and data visualization. In the age of internet-scale data, these techniques are not only powerful, but also necessary to extract the signal from the noise and to have the throughput to do so in a reasonable amount of time. It has revolutionized the fields of bio-informatics, climate science, word recognition, advertising, medicine, and is finding more applications daily. In Google's Translate application, substantial improvements over previous methods were achieved using artificial neural network (ANN) structures, making 60% fewer errors than the previous state-ofthe-art algorithm.2 In climate science, where models are sophisticated and numerous, data science techniques are used to determine which of 20 models will give the best prediction on future and historical data, the accuracy of which surpasses the accuracy of the average of all models, the current benchmark.3 As chemical engineers are increasingly tasked with the analysis of more complex data sets, these same data science tools which have revolutionized other fields become more relevant. 4 When data sets grow, they must be managed intentionally in order to be useful. Data management, a subfield of data science, fills this role and gives the tools to be able to correct for missing data points, ensure consistency of the data, and transform the content of the data such that it is suitable for use in other aspects of data science....
