Do you face any of these challenges?
- Looking to apply simulation or AI technologies to the development of materials, components, or systems for mobility and aerospace applications
- Wanting to leverage in-house performance and manufacturing data for smarter, data-driven design and production
- Seeking to harness the latest digital technologies to enhance safety, durability, and environmental performance in transportation and aviation
Comprehensive Software Suite and Robust Support to Realize Materials DX

JSOL offers a broad portfolio of simulation and AI technologies essential for materials development, including applications across the mobility and aerospace sectors—such as lightweight composites (e.g., CFRP) for aircraft and rail vehicles, high-performance polymers for marine and urban transport systems, coatings and adhesives for extreme environments, and battery and fuel cell materials for next-generation electric mobility. Each software package is designed for ease of use, and our expert support services—built on extensive experience—help even first-time users quickly integrate these tools into their work and research.
Our J-OCTA is built on OCTA, an open-source platform developed through a national industry–academia collaboration project in Japan. Today, it is widely used by manufacturing companies and research institutions around the world. By leveraging the open-source community, we create opportunities for user-to-user communication and collaboration directly through JSOL’s software.
When required, JSOL can also coordinate and support the creation of research frameworks that bring together companies, universities, and other partners—empowering joint innovation in materials development.
Comprehensive Software Suite and Robust Support to Realize Materials DX
- 01.Proven Track Record in Industry and Academia
- 02. Software for Multiscale Simulation
- 03. Software for Materials Informatics

In the mobility sectors—covering aircraft, rail vehicles, ships, and urban transportation—a wide variety of materials are used. In recent years, key trends such as electrification, decarbonization, and weight reduction through the use of materials like CFRP have driven the need for enhanced material properties. Applications include lightweight composites, high-performance polymers, coatings and adhesives for extreme environments, and advanced battery and fuel cell materials for next-generation mobility. To evaluate these diverse properties, it is essential to consider the multiscale nature of materials and employ a broad range of analytical methods and technologies.
With JSOL’s simulation software suite, you can run simulations tailored to each scale: from nanometers, capturing electronic states and molecular structures, to micrometers, modeling phase separation and composite materials. By linking these tools, it is possible to analyze the mechanisms behind the performance of advanced materials directly through simulation.
Today, there is growing demand for data-driven materials development using AI technologies, under concepts such as Materials Informatics and Process Informatics. JSOL’s AI-enabled software allows you to predict properties based on molecular and crystal structures, composition ratios, and processing environments—and even perform inverse design. When experimental data is scarce, high-throughput simulations can be executed to supplement datasets and accelerate discovery.
01Proven Track Record in Industry and Academia
Use Cases
02Software for Multiscale Simulation
In materials design, it is essential to determine which scale—from nanometers to micrometers—has the greatest impact on material properties.
No single simulation method can cover all scales and phenomena. That’s why we offer multiscale-ready software, combining multiple engines (solvers) into a single materials design solution.
This enables comprehensive simulation capabilities to analyze the multiscale characteristics of a wide range of materials.
Learn more on each product’s dedicated page.

03Software for Materials Informatics
J-OCTA MI-Suite includes machine learning capabilities for predicting material properties using molecular/crystal structures and various conditions as explanatory variables, along with related features such as molecular descriptor calculations, access to public databases, and analysis of each variable’s contribution.
When experimental data are insufficient, the software can supplement them with simulation results. It also provides API functionality to support database construction using physical simulations.
Data selection and learning condition settings for machine learning can be performed via dialogs, without the need for programming. Multiple pre-trained models are included, allowing users to start Materials Informatics projects even without their own data.
