MEGNet (molecule model)
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
- University of California San Diego
- Organisation type
- Academia
- Country
- United States of America
- Published
- 10 April 2019
- Authors
- Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, 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
- Molecular property 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.
- Parameters
- 8.7K
- Training data
- 117,416 tokens
- Epochs
- 1,000
Calculations here: https://docs.google.com/document/d/1BTmyZ9KVTIwkp9z9tRRFsWM5A0QcXiufXrIhujffyso/edit?tab=t.0#heading=h.dcux1bvmijlm
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
- 4.5 × 10¹⁷ FLOP
Calculations here: https://docs.google.com/document/d/1BTmyZ9KVTIwkp9z9tRRFsWM5A0QcXiufXrIhujffyso/edit?tab=t.0#heading=h.c0h2t2icf5xr Assuming a single GPU (the model is quite small): 1.0e+3*100*1.1e+13*0.4=4.5e+17
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 GeForce GTX 1080 Ti
- Chips used
- 1
- Wall-clock time
- 28 hours
- Power draw
- 283 W
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
- Citations
- 1,301
Sources
Where this record came from and when it was last checked.
- Reference
- Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
- Last updated
- 25 May 2026
What the numbers mean
About this model
MEGNet (molecule model) was published by University of California San Diego, in United States of America, in April 2019. It comes out of academia.
It works in Materials science, and is recorded as doing molecular property prediction.
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 4.5 × 10¹⁷ FLOP of computation, on NVIDIA GeForce GTX 1080 Ti — a measure of what producing the model cost, not of how fast it answers.
Around 117,416 tokens went into training it.
Answers
MEGNet (molecule model) — common questions
What is MEGNet (molecule model) used for?
MEGNet (molecule model) works in Materials science, and is recorded as handling molecular property prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train MEGNet (molecule model)?
Around 4.5 × 10¹⁷ FLOP, on NVIDIA GeForce GTX 1080 Ti. 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 MEGNet (molecule model)?
None. MEGNet (molecule model) 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 MEGNet (molecule model) open source?
The licensing for MEGNet (molecule model) 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 MEGNet (molecule model) have?
MEGNet (molecule model) has 8.7K parameters. Calculations here: https://docs.google.com/document/d/1BTmyZ9KVTIwkp9z9tRRFsWM5A0QcXiufXrIhujffyso/edit?tab=t.0#heading=h.dcux1bvmijlm. 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.
Who created MEGNet (molecule model)?
MEGNet (molecule model) was published by University of California San Diego, based in United States of America, categorised as academia.
When was MEGNet (molecule model) released?
MEGNet (molecule model) was published in April 2019. 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.