2023/02/17 by G. H. Teichert, Teichert, G. H., S. Das +13 · 2 citations
Biochemistry, Genetics and Molecular Biology · Materials Science · #Advanced Electron Microscopy Techniques and Applications #Algorithm #Atomic units #Bridging (networking) #Computation #Computational Engineering #Computer science #Electron and X-Ray Spectroscopy Techniques #FOS: Computer and information sciences #Finance #Machine Learning in Materials Science #Phase transition #Physics #Quantum mechanics #Statistical mechanics #Statistical physics #Thermodynamics #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2302.08991
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/02/17 · openalex created_date 2023/02/21 · openalex updated_date 2026/08/06
LixTMO2 (TM=Ni, Co, Mn) forms an important family of cathode materials for Li-ion batteries, whose performance is strongly governed by Li composition-dependent crystal structure and phase stability. Here, we use LixCoO2 (LCO) as a model system to benchmark a machine learning-enabled framework for bridging scales in materials physics. We focus on two scales: (a) assemblies of thousands of atoms described by density functional theory-informed statistical mechanics, and (b) continuum phase field models to study the dynamics of order-disorder transitions in LCO. Central to the scale bridging is the rigorous, quantitatively accurate, representation of the free energy density and chemical potentials of this material system by coarsegraining formation energies for specific atomic configurations. We develop active learning workflows to train recently developed integrable deep neural networks for such high-dimensional free energy density and chemical potential functions. The resulting, first principles-informed, machine learning-enabled, phase-field computations allow us to study LCO cathodes' phase evolution in terms of temperature, morphology, charge cycling and particle size.