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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.
![[Example] Polymer/Metal Surface Interactions in MaterialsStudio](https://image.mono.ipros.com/public/product/image/ead/2000560227/IPROS78838175995461250789.png?w=280&h=280)
Introduction of interaction examples between "polymer/metal surfaces" in Materials Studio.
Case Study Utilizing "Materials Studio" ◇ The interaction at interfaces in polymers is important in a wide range of product fields. For example, adhesives, coatings, composite materials, films, lubricants, paints, and printing inks. The properties at interfaces are of interest to researchers in these fields. Utilizing one of the modules, "Forcite Plus," helps to understand the structure of interfaces. In this case study, we simulated the interaction between alumina (Al2O3) and polyparanitrostyrene. 【Features】 ■ Also optimal for "Materials Informatics" ■ Simulation software that streamlines material development It can be used by those engaged in research, development, design, and manufacturing across various industries. ■ Helps in the development of new materials more efficiently and easily. ■ Supports various types of materials. ■ All tasks, including crystal structure creation, calculation condition setting, and calculation result display, can be performed on a single GUI screen. *For more details, please feel free to contact us. Wavefront Co., Ltd. Sales Department MAIL: sales@wavefront.co.jp
![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)
A message-passing machine learning force field with DFT-level accuracy accelerates organic, inorganic, and hybrid material simulations with high precision.
Machine learning force fields, also known as "machine learning interatomic potentials," have emerged as an important tool for achieving cost-effective atomic-level simulations of diverse chemical systems, often achieving accuracy comparable to density functional theory (DFT) at significantly lower computational costs. Recent advancements in message-passing networks have overcome the challenge faced by traditional MLFFs of being limited in the types of elements they can accommodate. Furthermore, the introduction of atomic charges and electrostatic interactions using charge equilibration methods allows for precise reproduction of multiple charge states, ionic systems, and electronic response characteristics, achieving even higher accuracy by explicitly considering long-range interactions. Our MLFF architecture, "MPNICE," incorporates explicit electrostatics for accurate charge representation. We also provide a set of pre-trained models trained on materials covering the entire periodic table (89 elements). MPNICE emphasizes high throughput performance, enabling long-duration and large-scale atomic-level simulations that were difficult to achieve with traditional methods while maintaining high accuracy.
