MatterSim (Grpaphomer)
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
- Microsoft Research AI for Science
- Organisation type
- Industry
- Country
- United States of America
- Published
- 10 May 2024
- Authors
- Han Yang, Chenxi Hu, Yichi Zhou, Xixian Liu, Yu Shi, Jielan Li, Guanzhi Li, Zekun Chen, Shuizhou Chen, Claudio Zeni, Matthew Horton, Robert Pinsler, Andrew Fowler, Daniel Zügner, Tian Xie, Jake Smith, Lixin Sun, Qian Wang, Lingyu Kong, Chang Liu, Hongxia Hao, Ziheng Lu
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.
- Parameters
- 182M
- Training data
- tokens
"The total parameters of Graphormer is 182M."
"The model is trained for a total of 1,562,500 steps" "The batch size for training is set to 256" "the maximum number of expanded atoms is capped at 256"
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 1.1 × 10²⁰ FLOP
- How it was established
- Operation counting
Speculative confidence because I am unsure how to calculate gradient updates/tokens 6*1,562,500*256*256*182000000=1.118208e+20
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
- NVIDIA A100
- Chips used
- 64
- Power draw
- 50.6 kW
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures
- Last updated
- 11 February 2026
What the numbers mean
About this model
MatterSim (Grpaphomer) was published by Microsoft Research AI for Science, in United States of America, in May 2024. It comes out of industry.
It works in Materials science, and is recorded as doing atomistic simulations, Molecular simulation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Training it took roughly 1.1 × 10²⁰ FLOP of computation, on NVIDIA A100 — a measure of what producing the model cost, not of how fast it answers.
Answers
MatterSim (Grpaphomer) — common questions
Who created MatterSim (Grpaphomer)?
MatterSim (Grpaphomer) was published by Microsoft Research AI for Science, based in United States of America, categorised as industry.
When was MatterSim (Grpaphomer) released?
MatterSim (Grpaphomer) was published in May 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 MatterSim (Grpaphomer) used for?
MatterSim (Grpaphomer) works in Materials science, and is recorded as handling atomistic simulations, Molecular simulation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train MatterSim (Grpaphomer)?
Around 1.1 × 10²⁰ FLOP, on NVIDIA A100. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run MatterSim (Grpaphomer)?
None. MatterSim (Grpaphomer) 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 MatterSim (Grpaphomer) open source?
The licensing for MatterSim (Grpaphomer) 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 MatterSim (Grpaphomer) have?
MatterSim (Grpaphomer) has 182M parameters. "The total parameters of Graphormer is 182M.". 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.
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.