DeepRelax
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
- National University of Singapore,Sun Yat-sen University,Peking University,China Medical University Hospital,Asia university,Guangdong L-Med Biotechnology Company
- Organisation type
- Academia,Academia,Academia,Academia,Industry
- Country
- Singapore, China, Taiwan
- Published
- 17 September 2024
- Authors
- Ziduo Yang, Yi-Ming Zhao, Xian Wang, Xiaoqing Liu, Xiuying Zhang, Yifan Li, Qiujie Lv, Calvin Yu-Chian Chen & Lei Shen
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein folding prediction
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
"By excluding compounds missing either initial or DFT-relaxed structures, we refined the dataset to 62,724 pairs. " Multiple properties for each structure are predicted in a regression approach.
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- Scalable crystal structure relaxation using an iteration-free deep generative model with uncertainty quantification
- Last updated
- 28 November 2025
What the numbers mean
Background
DeepRelax was published by National University of Singapore,Sun Yat-sen University,Peking University,China Medical University Hospital,Asia university,Guangdong L-Med Biotechnology Company, in Singapore, in September 2024. The organisation is categorised as academia,Academia,Academia,Academia,Industry.
It works in Biology, and is recorded as doing protein folding prediction.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
DeepRelax — common questions
What GPU do I need to run DeepRelax?
None. DeepRelax 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 DeepRelax open source?
The licensing for DeepRelax was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does DeepRelax have?
No parameter count has been published for DeepRelax, which is why no memory or speed figure appears on this page.
Who created DeepRelax?
DeepRelax was published by National University of Singapore,Sun Yat-sen University,Peking University,China Medical University Hospital,Asia university,Guangdong L-Med Biotechnology Company, based in Singapore, categorised as academia,Academia,Academia,Academia,Industry.
When was DeepRelax released?
DeepRelax was published in September 2024. 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 DeepRelax used for?
DeepRelax works in Biology, and is recorded as handling protein folding prediction. 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.