[Presentation of Materials] A materials research DX tool chosen by leading companies in various industries.

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last updated：Nov 25, 2025

シュレーディンガー

Leading companies in various industries, such as Panasonic, Bridgestone, Canon, Samsung, and L'Oréal, have adopted it.

Schrödinger's advanced physics-based modeling and AI/machine learning software supports the development of a wide range of materials, including polymer materials, batteries, semiconductors, organic electronics, catalysts, pharmaceutical formulations, cosmetics, metals, alloys, and ceramics.

Related Link - https://www.schrodinger.com/materials-science/

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■Density Functional Theory (DFT) calculations and first-principles calculations for periodic systems for physical property predictions 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 ■Molecular Mechanics (MM) method, Molecular Dynamics (MD) method, and coarse-grained MD for physical property predictions 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 applicable in machine learning Various descriptor and fingerprint generation/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 *For more details, please feel free to contact us.