Numerous achievements in the analysis of batteries, catalysts, resins, liquid crystals, adhesives, rubber, inks, etc.
Simulation platform for material development It enables modeling tailored to phenomena ranging from nanometers to micrometers. The support after implementation is also comprehensive, and it has been adopted by many companies in the country.
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basic information
【Analysis Targets】 Batteries / Catalysts / Resins / Liquid Crystal Materials / Adhesives / Rubber / Inks / Slurries / Composites 【Evaluable Physical Properties】 Mechanical Properties (Elastic Modulus, Young's Modulus, Poisson's Ratio, Viscoelasticity) / Thermal Properties (Glass Transition Temperature, Melting Point, Specific Heat, Thermal Conductivity) / Electrical Properties (Dielectric Constant, Dielectric Loss, Conductivity) / Affinity (Solubility, SP Value, Interaction Energy, Surface Tension) 【Product Functions】 SIESTA Modeler (First Principles Calculation) / COGNAC Modeler (Molecular Dynamics Calculation) / GENESIS Modeler (Molecular Dynamics, Free Energy Calculation) / SUSHI Modeler (Mean Field Method) / MUFFIN Modeler (Finite Element Analysis) / NAPLES・PASTA Modeler (Rheological Analysis) / Machine Learning QSPR (Property Estimation using Machine Learning Models)
Applications/Examples of results
For more details, please refer to the PDF document or feel free to contact us.
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Company information
JSOL has established complex and advanced simulation technologies that were difficult to realize, in collaboration with our customers, contributing to the manufacturing field. In the CAE field, we provide a variety of solutions ranging from software development to technical support, consulting, and contracted analysis, supporting everyone's "manufacturing." The areas where simulation plays a significant role are diverse, and our company particularly challenges difficult phenomena such as highly nonlinear and extremely large-scale computational models, as well as complex boundary conditions.
![[Example] Polymer/Metal Surface Interactions in MaterialsStudio](https://image.mono.ipros.com/public/product/image/ead/2000560227/IPROS78838175995461250789.png?w=280&h=280)
![Machine Learning Force Fields for Organic and Inorganic Systems [Materials Provided]](https://image.mono.ipros.com/public/product/image/2091471/IPROS7281308962660174824.jpg?w=280&h=280)
