Tutorial EN02—Critical Computational Methods for Solid State Batteries

Sunday, November 26, 2023
1:30 PM - 5:00 PM
Hynes, Level 2, Room 206

Instructors: Ji Qi, University of California San Diego; Anton Van der Ven, University of California Santa Barbara; Yan (Eric) Wang, Samsung Advanced Institute of Technology; Xin Li, Harvard University

Solid state battery is a rapidly growing field with potentially significant impact to global electrification. The battery system is sufficiently new and complex to enable abundant opportunities to create on the interface between experiment and computation. This tutorial session aims to bring prestigious computational researchers in both academia and industry to give lectures for the current progress and future perspective of critical computational methods for the development of solid state batteries. The audience will learn the success of the state-of-the-art computational approaches and the remaining gap with experiment to be filled by further computational development.

More specifically, the learning objectives will be on computational approaches (including density functional theory computation and machine learning) to make ground state structure predictions with changing lithium compositions, interface decomposition predictions at solid-solid interface, predicton and design of solid state superionic conductors.

Tutorial Schedule

1:30 pm
Computational Methods for the Prediction of Battery
Anton Van der Ven; University of California Santa Barbara, United States

2:15 pm
Ab Initio High-Throughput Discovery and Design of Next Generation Solid Electrolyte Materials with Superior Conductivity for SSBs
Yan (Eric) Wang; Samsung Advanced Institute of Technology, United States

3:00 pm

3:30 pm
Machine Learning and High-Throughput Discovery and Design of Next Generation Electrode and Superionic Materials and Their Interfaces for SSBs
Ji Qi; University of California San Diego, United States

4:15 pm
Computational Design of Interface Voltage Stability from Mechanic Constriction Principle for Advanced Performance of All SSBs
Xin Li; Harvard University, United States

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MRS publishes with Springer Nature


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