@misc{3faccff0283241c19524651c76e59ac3,
title = "Tutorial: Machine Learning and Artificial Intelligence in Batteries",
abstract = "Machine learning (ML) promises to compress the time needed to characterize battery performance, lifetime and safety. By coupling ML with physical models and metrics, that learning can bridge across materials, chemistries and cell designs. This tutorial will discuss the most popular ML techniques and resources and review recent work in the electrochemical literature. Applications include materials discovery, image recognition for quantitative microscopy analysis, fast charge algorithm development and life prediction.",
keywords = "artificial intelligence, battery, design, image recognition, life prediction, lithium-ion, machine learning, microscopy, neural network, testing",
author = "Kandler Smith and Paul Gasper and Andrew Schiek and Francois Usseglio-Viretta and Eric Dufek and Ross Kunz and Kevin Gering",
year = "2020",
language = "American English",
series = "Presented at the 2020 International Battery Seminar & Exhibit, 28-30 July 2020",
type = "Other",
}