Matlantis

Matlantis Announces Major Upgrade to Its Universal Atomistic Simulator for Materials Discovery, Opens Dedicated U.S. Office

Training datasets for Matlantis's core AI technology are now developed using r²SCAN, doubling accuracy in atomistic simulations compared to previous version

CAMBRIDGE, Mass., July 16, 2025 (GLOBE NEWSWIRE) -- Matlantis Inc., the U.S. hub of the materials‑discovery arm of Japan’s leading AI company Preferred Networks, Inc. (PFN), today announced a major update to its Matlantis™ universal atomistic simulator, and the opening of its office in Cambridge, Massachusetts for accelerating adoption of AI‑driven materials research across North America. The update introduces the new Version 8 of PFN’s proprietary AI technology named PFP (Preferred Potential), which enables researchers across industries to accelerate discovery, improve predictive performance, and unlock new frontiers in materials science with unprecedented levels of simulation accuracy.

PFP Version 8 marks a significant milestone as the first universal machine learning interatomic potential (MLIP) to be trained with datasets developed with a new method called r²SCAN (restored-regularized strongly constrained and appropriately normed) functional. PFP versions up to 7 relied on datasets generated with a method called PBE (Perdew-Burke-Ernzerhof) functional, which has also been widely adopted by MLIPs other than PFP. It is known, however, that PBE has certain limitations in simulation accuracy—how closely computer-based simulations of materials' behavior align with real-world experimental results.

The introduction of the r²SCAN method is the culmination of PFN's continuous efforts over the past couple of years to overcome the accuracy limitations of the PBE-based approach. Developing training datasets with the r²SCAN method is more computationally intensive, requiring three to five times the computing time compared to the PBE method. However, because PFP Version 8 is now trained with the datasets built with r²SCAN as well as PBE, Matlantis users can achieve up to doubled the simulation accuracy in the same timeframe as the previous version.

“This update represents a significant breakthrough,” said Daisuke Okanohara, CEO of Matlantis. “In 2021, we were the world’s first to launch a commercial simulator using a universal MLIP, and now our simulator, Matlantis, is the first globally to incorporate r²SCAN that ensures high simulation accuracy. We believe this will further pave the way for the era of computer-based materials discovery. We will continue to support researchers in North America and the rest of the world to discover innovative and sustainable new materials.”

Jointly invested by PFN and ENEOS, Japan’s largest energy company, and Mitsubishi Corporation, Matlantis has already been used by over 100 industrial and academic leaders worldwide since its launch in July 2021. Today, Matlantis is among the first commercially available AI‑powered platforms purpose‑built for atomistic simulation at industrial scale—offering a single MLIP that spans 96 elements (from hydrogen to curium) and delivers DFT (density functional theory)‑level accuracy up to 20 million times faster.

Matlantis enables research teams to:

• Perform simulations from the first day of use:
  Matlantis is provided as a cloud-based software-as-a-service (SaaS). Users can access it via a browser and start searching for new materials from the first day of use. Because Matlantis’s machine learning interatomic potential (MLIP) has already been trained with massive datasets, users can immediately focus on material discovery without spending time building machine learning models.

• Search a wide variety of undiscovered materials
  As a universal atomistic simulator, Matlantis covers a wide variety of materials for batteries, semiconductors, catalysts and more, without the need to change AI models depending on their types.

• Accelerate materials discovery
  With Matlantis, researchers can complete simulations in just a few hours that would otherwise require years of conventional DFT calculations. This speed-up transforms iterative design in materials discovery, reshaping the R&D process so that computational insights lead experiments, instead of merely validating them afterward.

• Achieve higher simulation accuracy than ever before
  With the new training datasets built with the r²SCAN method, Matlantis can simulate material properties with higher accuracy than common MLIPs in the same timeframe, further narrowing the gap between simulations and experiments.

“With PFP 8.0 we finally have a universal machine learning interatomic potential that keeps the best DFT‑level fidelity while spanning most of the periodic table,” said Matlantis Technical Advisor Prof. Ju Li, Ph.D., widely recognized for his work on atomistic modeling and materials research. “That accuracy‑plus‑speed combination lets engineers generate phase diagrams or screen multi‑component systems in hours or several days rather than weeks or months—work that directly informs alloy design, battery materials, and other high‑value applications. Establishing a U.S. office means we can collaborate even more closely with industrial and academic partners here, shorten feedback loops, and bring new Matlantis capabilities to market faster.”

Dr. Katsushisa Yoshida, Director, Deputy Head of Research Center for Computational Science and Informatics, Resonac, said: “We are excited to hear about the major update to Matlantis and the opening of their new U.S. office. We greatly look forward to how the evolution of this platform will further accelerate our own materials development.”

PFP 8.0 is developed using PFN's supercomputer and AI Bridge Cloud Infrastructure (ABCI) 2.0 and 3.0 provided by Japan’s National Institute of Advanced Industrial Science and Technology (AIST) and AIST Solutions Co., Ltd. The use of ABCI 3.0 is supported by the ABCI 3.0 Development Acceleration Program.

About Matlantis
Jointly developed by PFN and ENEOS, Matlantis is a universal atomistic simulator that supports large-scale material discovery by reproducing new materials’ behavior at an atomic level on the computer. PFN and ENEOS have incorporated a deep learning model into a conventional physical simulator to increase the simulation speed by tens of thousands of times and to support a wide variety of materials. Matlantis was launched in July 2021 as a cloud-based software-as-a-service by Matlantis Inc. (formally named Preferred Computational Chemistry), a company jointly invested by PFN, ENEOS and Mitsubishi Corporation. Matlantis is used by over 100 companies and organizations for discovering various materials including catalysts, batteries, semiconductors, alloys, lubricants, ceramics and chemicals.
For more information, please visit: https://matlantis.com

Media Contact:
Janabeth Ward
Scratch Marketing + Media for Matlantis
matlantis@scratchmm.com