Preferred Networks

Preferred Networks, Inc. (PFN)'s official account
Japanese account: @PreferredNetJP

[Preferred Networks @PreferredNet]

PFN has teamed up with Mitsubishi Heavy Industries (MHI) to make Japan-made AI for mission-critical fields by combining MHI’s hardware, control & simulation technologies with PFN’s AI expertise. We also aim to establish a capital alliance within FY2026. https://t.co/rDngxDOM0O

[トヨタ 未来創生センター | Toyota Frontier Research Center @TOYOTA_FRC]

#FrontierResearchCenter and Preferred Networks, Inc. are conducting joint research on physical AI that leverages large-scale data. Today, PFN announced a collaborative research initiative to accelerate inference processing by integrating PFN’s MN‑Core™ L series—an inference-

[Preferred Networks @PreferredNet]

We have started joint research with Toyota's Frontier Research Center to accelerate physical AI using our upcoming ultra-high-bandwidth AI inference chips in the MN-Core L Series for robots which typically require high-speed on-premise inference
https://t.co/iIeD7RhljA

[Preferred Networks @PreferredNet]

PFN Co-Founder & CEO @hillbig will speak at Imec Technology Forum #ITFWorld2026 in Antwerp, Belgium at 4:30pm CET today. Theme: "Chips for AI, AI for Chips: Redesigning AI from Materials up" Abstract: https://t.co/X5fA8kJwWZ

[Preferred Networks @PreferredNet]

Visit the PFN desk in the Microsoft Japan booth (A103, West 3) at #SusHiTechTokyo2026 today and tomorrow! PFN's global biz VP and MS Japan's CTO will be on Green Stage (1st floor) today at 17:15-18:00 to discuss integration of Azure between our PLaMo LLM
https://t.co/a4mhhbtdg5

[Preferred Networks @PreferredNet]

Hiring now: MN-Core runtime software engineer https://t.co/vuAfKFd1Sr

[Preferred Networks @PreferredNet]

Hiring now: We started looking for engineers who can develop the LLM serving engine with us for our MN-Core™ L series of proprietary inference AI chip.
See reply for other MN-Core-related positions👇 https://t.co/heSbL4cm7n

[Preferred Networks @PreferredNet]

Hiring now: MN-Core compiler engineer
https://t.co/VwBNYU5nqj

[Preferred Networks @PreferredNet]

Applications are now open for the our 2026 Summer Internship! We welcome students who want to create new technologies, products and services using machine learning and other computer science technologies. The application deadline is Sunday, April 19 JST. https://t.co/4OCn70WrFL

[OptunaAutoML @OptunaAutoML]

Optuna can also be applied in material science. A blog post has been published detailing its use case in crystal structure prediction.
https://t.co/mcIGqTf0Fe

[Preferred Networks @PreferredNet]

PFN, IIJ and JAIST will start operating AImod, the direct liquid cooling modular data center built in IIJ's Shiroi DCC, in April 2026. We will jointly test the liquid cooling system and high-density integration of PFN's AI chips in the MN-Core™ series. https://t.co/ozwP8EDei3

[Preferred Networks @PreferredNet]

PFN Co-Founder and CEO Daisuke Okanohara @hillbig has been invited to speak at #ITFWorld2026, the annual forum to be held by the international nanoelectronics R&D hub Imec between May 19-20 in Antwerp, Belgium. The session theme will be announced soon!
https://t.co/bvgEQFNFzG

[CuPy @CuPy_Team]

Announcing CuPy v14! 🚀

🔹 NumPy v2 semantics
🔹 CUDA Pip Wheels support
🔹 bfloat16 & structured dtype
🔹 Enhanced NumPy/SciPy API coverage

Read more on our blog for the full details! 👇

[Matlantis™ @matlantis_en]

Discover unknown stable crystals at unprecedented speed. Introducing Matlantis CSP. 💎🚀
We are thrilled to announce the launch of Matlantis CSP (Crystal Structure Prediction), a new capability that rapidly identifies previously unknown stable crystal structures from the enormous search space of atomic configurations and compositions within a given elemental system.

Materials R&D has long depended on repeated synthesis experiments, even when the likelihood of success is low. Matlantis CSP introduces a computational screening step earlier in the process to rule out what is physically implausible in advance.

By combining Matlantis’ core technology—the universal machine learning potential PFP—with proprietary algorithms, Matlantis CSP enables you to: ✅ Screen out experimentally unviable configurations before experimentation. ✅ Explore complex composition spaces with approx. 3–6× greater efficiency than random search. ✅ Discover novel materials that do not exist in current databases.

We are honored to have Honda R&D as an early adopter.
“We have high expectations for CSP as a technology that will dramatically improve exploration efficiency in materials development,” said Mitsumoto Kawai, Chief Engineer, Device Process, Innovative Research Excellence, Honda R&D Co., Ltd. “Through CSP, crystal structure searches—including multi-component systems and metastable structures that were previously impractical—have become feasible. Being able to narrow down promising crystal structures and compositions with high confidence before experiments will not only increase the probability of realizing next-generation materials but also shorten development timelines.”

Learn how Matlantis CSP can transform your materials discovery: 👇
https://t.co/659CMRyvWI
#Matlantis #CrystalStructurePrediction

[Preferred Networks @PreferredNet]

Watch PFN co-founder @hillbig talk about AI in Japan as a guest lecturer at Columbia Business School Center on Japanese Economy and Business @ColumbiaCJEB in December last year https://t.co/8iTfrjCio3

[Preferred Networks @PreferredNet]

PFN was selected as a partner for JICA's PoC for the National Bank of Cambodia🇰🇭. PFN will develop an AI model that forecasts the liquidity of the local currency to support Cambodia's Riel promotion efforts to achieve independent monetary policy. https://t.co/oIXLYGD01t

[Preferred Networks @PreferredNet]

Pre-register to our Summer 2026 Internship Program in Japan to receive invitations for applications from PFN! We welcome students who wish to take on technological challenges in algorithms, mathematics and implementation. https://t.co/3J2kZhPqKK

[Preferred Networks @PreferredNet]

Here's our paper on PARC, a coding agent that can autonomously execute long-horizon tasks. PARC assesses its actions and outcomes by itself to detect and correct errors. We demonstrated its problem-solving capability in materials science and Kaggle tasks. https://t.co/WvwIGTrgNO

[Preferred Networks @PreferredNet]

PFN's Japanese translation-specific LLM Plamo™ Translate will be adopted by Japan's "Gennai" government AI project as a high-quality, Japan-made on-premise AI for supporting government employees. https://t.co/XzTOkgNPju

[Preferred Networks @PreferredNet]

Do you need some extra bandwidth (for LLM inference)? If you’re attending #SC25 in St. Louis, meet us at the booth 4311 and check out our MN-Core™︎ series of AI processors!