Mechanistic Design of High-Capacity Cation-Rich Anion-Redox Cathodes with First-Principles Simulation, Compton Measurements and Scientific Machine Learning

Abstract

Approved for Public ReleaseWe propose to search the design space of Li-rich TM oxide systems to identify the best composition activating highly stable and reversible anionic and cationic redox for high capacity. We aim to develop robust design rules through conducting meticulous theoretical and experimental analysis on model systems and comparing with coin cell testing. We shall feed these rules into machine learning architectures to efficiently search the design space of 3d metals and predicting the degree of irreversibility in each composition. Rapid cell testing and Compton scattering evaluation of the most promising materials shall validate and improve the design rules for a more optimized search in the next iteration cycle. This work leverages our unique capability, publishedin Nature, 5 enabling tomographic reconstruction of the oxygen orbitals using DFT and X-ray Compton scattering measurements. Specifically, we will focus on 3d TMOs in a binary system (# Li2TMaO3 ## LiTMbO2, TMa and TMb are transition metals, # and # represent thecompositions of the individual binary components) known for high capacities and energy densities,.16 An example is the case of TMa = Ti, TMb = Mn, # = 1, # = 1 demonstrated in our earlier work.5 Since anionic redox is also accompanied by undesirable irreversibilities like oxygen evolution and capacity fade, we will start by investigating the underlying mechanisms in Ni-rich.

Document Details

Document Type
DoD Grant Award
Publication Date
Jun 13, 2024
Source ID
N000142412376

Entities

People

  • Venkatasubramanian Viswanathan

Organizations

  • Board of Regents of the University of Michigan
  • Office of Naval Research
  • United States Navy

Tags

Readers

  • Battery Technology and Engineering
  • Electrochemical Engineering/ Fuel Cell Technologies
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Space