[User Case Presentation] Development of Next-Generation Lithium-Ion Batteries

We will introduce a case of innovative material search implemented in the development of next-generation lithium-ion batteries by the CEO of Eonix.

Eonix is a startup focused on the rapid design of next-generation materials for energy storage technologies targeting home appliances, grid storage, and electric vehicles. CEO Don DeRosa, Ph.D., explains how combining high-throughput screening and physics-based modeling can transform the material discovery process for building better batteries.

basic information

Our Materials Science Suite can accommodate a wide range of materials research fields. ■ Property predictions using Density Functional Theory (DFT) calculations and first-principles calculations in periodic systems HOMO/LUMO/pKa/solvent effects/IR/Raman/UV-vis/VCD/NMR/oxidation/reduction potential/triplet excited state energy/TADF S1-Tx gap/fluorescence/phosphorescence/vibrational calculations/structure optimization/transition state calculations/reaction pathway analysis/adsorption energy/bond dissociation energy/electron and hole mobility/reorientation (rearrangement, reconfiguration) energy ■ Property predictions using Molecular Mechanics (MM), Molecular Dynamics (MD), and Coarse-Grained MD Density/conformation analysis/cross-linked structures/Young's modulus/viscosity/surface tension/glass transition temperature (Tg)/molecular diffusion/thermal expansion/crystal morphology/swelling/stress-strain curves/solubility parameters Methods available for use in machine learning Generation of various descriptors and fingerprints/Partial Least Squares regression (PLS)/Multiple Linear Regression (MLR)/Principal Component Regression (PCR)/Kernel PLS/Bayesian classification/Recursive Partitioning (RP) analysis/Self-Organizing Maps/Tg, dielectric constant, boiling point, vapor pressure prediction models/genetic algorithms/active learning