CAMBRIDGE, Mass. and TOKYO, Sept. 17, 2026 (GLOBE NEWSWIRE) — Matlantis, a leading provider of AI-powered atomistic simulation for industrial materials R&D, today announced that ENEOS Holdings Corporation (“ENEOS HD”) is using Matlantis PFP, the company’s general-purpose machine learning potential to accelerate the discovery of new catalyst materials with NVIDIA ALCHEMI.
Materials discovery has traditionally relied on a combination of computational modeling, laboratory experiments, and trial and error. As the number of potential materials grows, researchers need computational methods that can evaluate large candidate spaces before committing resources to physical synthesis and testing. This is especially true for oxygen evolution reaction (OER) catalysts, which are critical to hydrogen production through water electrolysis.
By combining PFP with NVIDIA ALCHEMI, ENEOS HD evaluated approximately 100 million candidate structures for OER catalysts, identifying priority candidates for synthesis and experimental validation. The effort reduced a discovery process that traditionally took years to just a few months, demonstrating how AI-powered atomistic simulation and GPU-accelerated computing can enable materials researchers to explore significantly larger candidate spaces in less time.
“The development of innovative materials is becoming increasingly important to achieving a carbon-neutral society,” said Takeshi Ibuka, General Manager, AI Innovation Department, ENEOS Holdings Corporation. “This work demonstrates how large-scale computational screening can fundamentally change the way we discover and develop materials, allowing us to explore possibilities that would have been difficult to reach through conventional approaches. ENEOS will continue to push the boundaries of materials development by applying advanced computational technologies to some of the most important challenges facing a sustainable future.”
Scaling Materials Discovery with AI and Accelerated Computing
PFP is a general-purpose machine learning interatomic potential designed to accelerate atomistic simulations while maintaining quantum-level accuracy. Supporting all 96 chemical elements, PFP can be applied across a broad range of elements, material systems, and industrial applications using a single model. This enables researchers to evaluate diverse candidate materials at scale without relying on separate models for individual material systems.
NVIDIA ALCHEMI provides an accelerated computing platform for scaling computational workflows across chemistry and materials science. Together, the technologies enable high-throughput atomistic simulation across material spaces that would be impractical to evaluate using conventional computational approaches alone.
“The collaboration between ENEOS, NVIDIA and Matlantis demonstrates what is possible when industry leaders bring together deep materials expertise, advanced computing and AI,” said Daisuke Okanohara, President and CEO of Matlantis. “Together, we are helping move materials discovery beyond the limits of traditional approaches and opening new opportunities for innovation across industries. We look forward to building on this work with ENEOS and NVIDIA to accelerate the development of the materials needed to address some of the world’s most pressing challenges.”
“AI and accelerated computing are opening a new frontier in materials science, enabling researchers to explore chemical spaces that were previously out of reach,” said Dion Harris, senior director, HPC, Cloud and AI Infrastructure at NVIDIA. “ENEOS’ work integrating NVIDIA ALCHEMI with Matlantis PFP demonstrates how high-throughput atomistic simulation can accelerate the search for OER catalysts and help advance the materials a sustainable future depends on.”
About Matlantis Inc.
Matlantis Inc. was established in 2021 as a joint venture between Preferred Networks, Inc. and ENEOS Corporation (the company changed its name in July 2025). Matlantis provides its general-purpose atomistic simulator, MatlantisTM, as a cloud service. Its proprietary machine learning interatomic potential (MLIP), Matlantis PFP, enables calculations up to tens of thousands of times faster than conventional approaches.
Matlantis is currently used by more than 150 companies, universities, and research institutions in Japan, and has also been adopted internationally by companies including Volkswagen and Hyundai Motor Company. The platform supports materials development across a wide range of fields, including catalysts, batteries, semiconductors, alloys, lubricants, ceramics, and chemicals.
Website: https://matlantis.com/en/
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