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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

Case studies on inorganic solids and polymers! Designing new compounds in a cost-effective and time-efficient manner.

【Case Studies】 ■ In the prediction of the glass transition temperature (Tg) of polymers included in the bicerano dataset, the prediction model using customized polymer descriptors significantly reduced the root mean square error (RMSE) from 38K to 11K compared to using general descriptors. ■ A model was constructed that can accurately predict the 29Si NMR chemical shifts of silicon nitride using periodic three-dimensional descriptors. ■ A model using 140 descriptors based on chemical composition and structure accurately predicted the bulk modulus of 1181 inorganic crystals (over 60 elements) with high precision (R2=0.913). ■ The band gaps of 3000 transparent conductive oxides included in the NOMAD 2018 Kaggle challenge were accurately predicted by a deep learning prediction model built using 10 SOAP-PCA 3D descriptors and AutoQSAR/DeepChem. ■ A deep learning prediction model capable of predicting the water solubility of 8773 molecules collected from literature was constructed using AutoQSAR/DeepChem (R2=0.88). *Please feel free to contact us.

Predict the evaporation or sublimation temperature with an accuracy of ±9°C on average, calculating hundreds of complexes per second.

【Published Items】 ■Challenges in Volatility Prediction ■Approaches Using Machine Learning ■Volatility Prediction Results of Inorganic and Organic Metal Complexes *For more details, please refer to the PDF document or feel free to contact us.

Quickly transform data into knowledge based on informatics! Contributing to the field of advanced materials development.

Our Materials Science Suite can accommodate a wide range of materials research fields. ■ Property predictions through 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/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

Supporting material research and development through high-speed molecular simulations! Here is an overview of our products.

We provide a digital chemistry platform managed by physics-based modeling, amplified by machine learning, and optimized through team-based intelligence. [Catalog Products] ■ Platform Software: MS Maestro, LiveDesign ■ Simulation Engines: Desmond, MacroModel, Jaguar ■ Application-Specific Applications - Molecular Dynamics & Molecular Mechanics: OPLS4, MS Transport, MS CG, MS Penetrant Loading, MS Morph - Quantum Mechanics: GA Optelectronics, Quantum ESPRESSO GUI - Multiscale: MS Mobility, MS Reactivity, MS Dielectric - Machine Learning: AutoQSAR, MS Informatics *For more details, please feel free to contact us.

By performing it on a single graphical interface, calculations can be done efficiently!

Our computational chemistry platform is capable of addressing 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) 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

Identifying promising candidate substances! Useful for selecting compounds that meet the conditions for device optimization.

Regarding the hole mobility and charge mobility in amorphous and crystalline structures, calculations can be performed using two different methods based on Marcus theory: kinetic Monte Carlo method and a method based on electronic coupling. Additionally, TDDFT calculations that take spin-orbit coupling into account can be used to predict the light absorption and emission of molecules. *For more details, please refer to the PDF materials or feel free to contact us. [Contents of the materials] Calculations of properties related to organic electronics (HOMO, LUMO energy levels, electron and hole reorganization energy, triplet excited state energy) for NPB, mCP, Ir(ppy)3, and AlQ3 using the Jaguar program. Glass transition temperature (Tg) and coefficient of thermal expansion (CTE) of the organic hole transport material TPD through NPT molecular dynamics calculations using the Desmond program. Predictions of charge mobility for hole transport materials NPB, CzC, 2TnATA, TCTA, TPD, spiro-TPD, o-BPD, m-BPD, and p-BPD.