M3GNet
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
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
- University of California San Diego
- Organisation type
- Academia
- Country
- United States of America
- Published
- 11 August 2022
- Authors
- Chi Chen, Shyue Ping Ong
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Materials science
- Task
- Atomistic simulations, Molecular simulation
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
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Open source
https://github.com/materialsvirtuallab/m3gnet BSD 3-Clause
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
- A Universal Graph Deep Learning Interatomic Potential for the Periodic Table
- Last updated
- 28 November 2025
What the numbers mean
Background
M3GNet was published by University of California San Diego, in United States of America, in August 2022. It comes out of academia.
It works in Materials science, and is recorded as doing atomistic simulations, Molecular simulation.
The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.
Answers
M3GNet — common questions
What is M3GNet used for?
M3GNet works in Materials science, and is recorded as handling atomistic simulations, Molecular simulation. These are the areas it was designed around; they describe intent rather than a hard boundary.
Where can I download M3GNet?
The weights for M3GNet are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
What GPU do I need to run M3GNet?
We cannot say. M3GNet has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Is M3GNet open source?
Its weights are published, so M3GNet can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
How many parameters does M3GNet have?
No parameter count has been published for M3GNet, which is why no memory or speed figure appears on this page.
Who created M3GNet?
M3GNet was published by University of California San Diego, based in United States of America, categorised as academia.
When was M3GNet released?
M3GNet was published in August 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.
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.