Promotion of Organic Electronics Materials Development

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last updated：Oct 01, 2025

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Efficient development of organic electronics materials using the integrated platform Materials Science Suite.

Organic electronics materials are required to have good optoelectronic properties and chemical stability as individual molecules, as well as desirable morphology and thermodynamic properties in the aggregated phase. The Materials Science Suite provides atomic-scale simulations applicable to these systems based on quantum chemistry, molecular dynamics, and machine learning, supporting efficient material development through the insights and theoretical interpretations obtained. *For more details, please refer to the PDF document or feel free to contact us.*

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Our Materials Science Suite is capable of addressing a wide range of materials research fields. ■ Property predictions using Density Functional Theory (DFT) calculations and first-principles calculations for 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/crosslinked 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