GenMS
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
- Google DeepMind
- Organisation type
- Industry
- Country
- United States of America
- Published
- 10 September 2024
- Authors
- Sherry Yang, Simon Batzner, Ruiqi Gao, Muratahan Aykol, Alexander L. Gaunt, Brendan McMorrow, Danilo J. Rezende, Dale Schuurmans, Igor Mordatch, Ekin D. Cubuk
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Materials science
- Task
- Crystal discovery
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
for training dissusion model (there were also language and GNN modeules trained): Batch size 512 Training steps 200000
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- Google TPU v4
- Chips used
- 64
- Power draw
- 42.9 kW
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
- Unreleased
- Training code
- Unreleased
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
- Generative Hierarchical Materials Search
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
GenMS was published by Google DeepMind, in United States of America, in September 2024. The organisation is categorised as industry.
It works in Materials science, and is recorded as doing crystal discovery.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
GenMS — common questions
Who created GenMS?
GenMS was published by Google DeepMind, based in United States of America, categorised as industry.
When was GenMS released?
GenMS 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 GenMS used for?
GenMS works in Materials science, and is recorded as handling crystal discovery. 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.
What GPU do I need to run GenMS?
None. GenMS 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 GenMS open source?
No. GenMS has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does GenMS have?
No parameter count has been published for GenMS, which is why no memory or speed figure appears on this page.
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.