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Accelerating research and development of next-generation battery materials through atomic-level simulations and machine learning.

Our computational chemistry platform is capable of addressing a wide range of materials research fields. ■ Prediction of physical properties through 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 ■ Prediction of physical properties 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 (PLS) regression/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

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

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