Development of New Materials Using Materials Informatics (MI)
Development of new materials using MI
Materials Informatics (MI) is a game-changing technology, fueled by massive amounts of data, that propels Artificial Intelligence (AI) forward in the search for novel materials. MI has attracted attention worldwide thanks to its emerging status as an essential technology for the future.
ENEOS aims to discover and develop innovative materials through the integration of experiments, simulations, and AI to improve various fields, such as renewable energy, catalysts, and lubricants.
AI × Simulation Platform: Development and Application of MatlantisTM
There is a growing search for innovative materials to help achieve carbon neutrality. Existing digital technologies, such as molecular simulation and AI, are becoming increasingly important. ENEOS and Preferred Networks, Inc. have jointly developed MatlantisTM[1-2] - a versatile atomistic level simulator. MatlantisTM can calculate the energy and physical properties of molecules, crystals, and other types of materials at extremely high speeds and discover a wide range of new materials beyond what was possible previously.
Matlantis Corporation was founded by both companies and has been providing MatlantisTM under a Software as a Service (SaaS) model since 2021 in Japan. Furthermore, the service has also been provided in the US from April 2023 and in Europe from December of that same year. We are contributing to the development of new materials around the world.
Case Studies Focused MatlantisTM and MI
ENEOS has taken up the challenge of accelerating materials research and fostering innovation in R&D using MatlantisTM.
(1) Virtual Screening of New Methanol Synthesis Catalysts Using MatlantisTM
Using MatlantisTM, we elucidate reaction mechanisms on complex catalyst surfaces and search for high-performance catalysts. In virtual screening for novel catalysts in methanol synthesis, calculations that would take years with conventional simulations were completed in just a few weeks. Experimental results confirmed that the proposed catalyst significantly outperforms existing catalysts. We will drive efficient catalyst development by leveraging this platform using MatlantisTM.
(2) Lubricant and Grease Design
Using advanced simulation technologies such as MatlantisTM, we design lubricants and greases. After estimating actual structures using analytical data, we perform large-scale simulations with LightPFP, enabling more realistic reproduction of phenomena. An example of the results applied to grease is shown in the figure. We were able to clarify for the first time the factors that cause differences in grease performance based on molecular structure and establish design guidelines. We are also promoting the creation of next-generation lubricants by utilizing this simulation technology for a wide range of applications, including lubricants for automobiles, home appliances, and industrial processes. (Click here for the Lubricant R&D page)
(3) Analysis of Stereoselectivity in Butadiene Coordination Polymerization
In developing functional polymer materials, we are building multiscale simulation technology based on MatlantisTM. As an example, we analyzed the polymerization mechanism of polybutadiene using the RedMoon method [3-4], which handles chemical reactions. The results reproduced the preferential selection of cis structures observed in prior experiments, confirming that the isomerization process of monomers coordinated to the catalyst is a key factor in cis structure selection. We are advancing material design to achieve desirable polymer properties and structures by utilizing the established methodology and other scale simulation technologies.
(4) Linked with the Reaction Auto-search Program “GRRM20”
OOur company, alongside HPC SYSTEMS and Matlantis Corporation, have jointly developed GRRM20 with Matlantis. This functionality uses GRRM on MatlantisTM to complete the automatic exploration of chemical reaction pathways at an unprecedented speed.
