GNoME for crystal discovery
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
- 29 November 2023
- Authors
- Amil Merchant, Simon Batzner, Samuel S. Schoenholz, Muratahan Aykol, Gowoon Cheon, Ekin Dogus 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.
- Parameters
- 16.2M
- Training data
- 69,000 tokens
- Epochs
- 1,000
"The pretrained potential has 16.24 million parameters." This refers to the GNoME network, which is a "Gaussian Network Model of Energy" for predicting crystal potential energy of new crystals.
"Initial models are trained on a snapshot of the Materials Project from 2018 of approximately 69,000 materials"
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
- 4
- Power draw
- 2.7 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.
- Why it is tracked
- Historical significance
- Record confidence
- Likely
Economic impacts from development of commercially and socially valuable protein designs and materials
Sources
Where this record came from and when it was last checked.
- Reference
- Scaling deep learning for materials discovery
- Last updated
- 28 November 2025
What the numbers mean
Background
GNoME for crystal discovery was published by Google DeepMind, in the country recorded as United States of America, during November 2023. The publishing organisation is categorised as industry.
It works in the domain of Materials science, and is recorded as performing the task of crystal discovery.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
It was trained on a corpus of about 69,000 tokens of text.
The reason it appears in this catalogue at all: historical significance.
Answers
GNoME for crystal discovery — common questions
GNoME for crystal discovery— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
GNoME for crystal discovery— how many parameters does it have?
It has a parameter count of 16.2M. "The pretrained potential has 16.24 million parameters." This refers to the GNoME network, which is a "Gaussian Network Model of Energy" for predicting crystal potential energy of new crystals. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
GNoME for crystal discovery— who created it?
It was published by Google DeepMind, based in United States of America, an organisation categorised as industry.
GNoME for crystal discovery— when was it released?
It was published in November 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
GNoME for crystal discovery— what is it used for?
It works in the domain of Materials science, and is recorded as handling the task of crystal discovery. These are the areas it was designed around; they describe intent rather than a hard boundary.
GNoME for crystal discovery— what GPU do I need to run it?
None. This 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.
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.