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Towards the realization of faster new material development.

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 methods 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

Support for property prediction, analysis, and design based on molecular structure and nanoscale structure through large-scale statistical analysis of experimental data and high-precision nanoscale simulations.

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 for periodic systems HOMO/LUMO/pKa/solvent effects/IR/Raman/UV-vis/VCD/NMR/oxidation/reduction potentials/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 mobilities/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 (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

A GPU-assisted high-speed molecular dynamics engine that supports the rapid and high-precision prediction of physical property values of polymers and resins.

The GPU-accelerated molecular dynamics calculation engine Desmond implements numerous acceleration algorithms, achieving microsecond-order calculations within a week. The high-precision parameter OPLS4, developed through proprietary research and development, accommodates various molecular structures by developing a vast array of force field parameters from millions of compounds. 【Diverse Polymer Structure Builder Including Crosslinked Resins】 Homopolymers, block copolymers, random copolymers, dendrimers Creation of amorphous structures, interactive crosslinked structure creation features Semicrystalline polymer structures, mixtures of multiple polymers and low molecules Interface structures between dissimilar materials, etc. 【Physical Property Prediction and Analysis Tools】 Density / cohesive energy density / heat of vaporization / specific heat / solubility parameters Glass transition temperature / linear expansion coefficient Stress-strain curves / yield points / elastic constant tensors / Young's modulus / Poisson's ratio Diffusion coefficients of low molecules in polymers Surface tension / interaction energy between dissimilar materials Infiltration rate of low molecules into polymers (water absorption rate in the case of water) SAXS diffraction patterns / structure factors / radial distribution functions Visualization and analysis of polymer electrolyte-ion interactions Dielectric constant / refractive index / Abbe number

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

An integrated platform that supports the development/analysis of semiconductors and related technologies with high speed and high precision.

Our computational chemistry platform is capable of addressing a wide range of materials research fields. ■ Property prediction 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 ■ Property prediction 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

Instant answers to your materials informatics concerns! The evolved AI platform LiveDesign accelerates new material development.

*For more details, please feel free to contact us.* ■Supports various entities Low molecular weight Peptides, oligos, RNA formulations Inorganic complexes, catalysts, organic EL Additives, metal complexes ■It is also possible to integrate the computational methods of Schrödinger's Materials Science Suite. Organic electronics Polymeric materials Consumer goods Catalysts and reaction systems Semiconductors Energy recovery and storage Complex formulations Metals, alloys, ceramics

Physics-based simulation and machine learning software for a wide range of users, from beginners to experts in computational chemistry.

Product Group Desmond: Predicts various material properties such as glass transition temperature, water absorption rate, Young's modulus, refractive index, and dielectric constant using high-speed molecular dynamics (MD) programs. AutoQSAR: Enables the construction and application of machine learning models to easily predict the physical properties of organic molecules through an automated workflow. MS CG LiveDesign: An AI platform for materials informatics. OPLS4: Proprietary high-precision force field parameters. Jaguar: A rich automated quantum mechanics calculation workflow. *For more details, please refer to the PDF document.*