PFP
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Preferred Networks Inc
- Organisation type
- Industry
- Country
- Japan
- Published
- 30 May 2022
- Authors
- So Takamoto, Chikashi Shinagawa, Daisuke Motoki, Kosuke Nakago, Wenwen Li, Iori Kurata, Taku Watanabe, Yoshihiro Yayama, Hiroki Iriguchi, Yusuke Asano, Tasuku Onodera, Takafumi Ishii, Takao Kudo, Hideki Ono, Ryohto Sawada, Ryuichiro Ishitani, Marc Ong, Taiki Yamaguchi, Toshiki Kataoka, Akihide Hayashi, Nontawat Charoenphakdee, Takeshi Ibuka
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Materials science
- Task
- Materials design
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Hosted access (no API)
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
- Citations
- 284
Sources
Where this record came from and when it was last checked.
- Reference
- Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements
- Last updated
- 28 November 2025
What the numbers mean
About this model
PFP was published by Preferred Networks Inc, in Japan, in May 2022. industry is the category the publisher falls under.
It works in Materials science, and is recorded as doing materials design.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
PFP — common questions
What GPU do I need to run PFP?
None. PFP is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is PFP open source?
No. PFP has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does PFP have?
No parameter count has been published for PFP, which is why no memory or speed figure appears on this page.
Who created PFP?
PFP was published by Preferred Networks Inc, based in Japan, categorised as industry.
When was PFP released?
PFP was published in May 2022. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is PFP used for?
PFP works in Materials science, and is recorded as handling materials design. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.