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 of Matlantis™
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 Matlantis™[1-2] - a versatile atomistic level simulator. Matlantis™ 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 Matlantis™ and MI
ENEOS has taken up the challenge of accelerating materials research and fostering innovation in R&D using Matlantis™.
(1) Virtual Screening of New Methanol Synthesis Catalysts Using Matlantis™
Using Matlantis™, 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 Matlantis™.

(2) Lubricant and Grease Design
Using advanced simulation technologies such as Matlantis™, 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) Development of High-performance Materials through Multiscale Simulation
We develop multiscale simulation and materials discovery technologies based on Matlantis™ that bridge structure and properties across microscopic, mesoscopic, and macroscopic scales.
These technologies enable both the elucidation of complex phenomena emerging at distinct spatial and temporal scales, as well as the design of novel materials that enhance product performance while reducing process costs to form a mutually reinforcing cycle.
As an illustrative example, a novel method for screening additives that facilitate efficient polymerization has been developed, which led to a significant reduction in the number of required experiments.
In this example, the design of high-performance materials is accelerated through the development of coarse-grained potentials using adhesion analysis of material interfaces using Matlantis™ and mechanical property evaluation using LightPFP.

(4) Linked with the Reaction Auto-search Program “GRRM20”
Our 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.

Related Video
Press Release
PFN and ENEOS Release v7 of PFP Neural Network Potential for Universal Atomistic Simulator Matlantis324KB
PFCC and Mitsubishi Corporation to Grow International Sales of Matlantis Atomistic Simulator under Business-and-Capital Alliance
PFCC Launches Matlantis Atomistic Simulator as Cloud-Based Service227KB
Related Link
Original Paper
- 1S. Takamoto, C. Shinagawa, D. Motoki, et al. Towards universal neural network potential for material discovery applicable to arbitrary combinations of 45 elements. Nat Commun 13, 2991 (2022)
- 2Matlantis (https://matlantis.com/en/), software as a service style material discovery tool.