MEGNet (crystal elasticity 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
- Numerical format
- FP32
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
- 26.1K
- Training data
- 4,664 tokens
- Epochs
- 1,000
Calculations here: https://docs.google.com/document/d/1BTmyZ9KVTIwkp9z9tRRFsWM5A0QcXiufXrIhujffyso/edit?tab=t.0#heading=h.913mrln2g0cv
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.3ria9ya1ty1o Updated calculation assuming 100s per epoch and a single GPU. 1000*100*11340000000000*0.4=453600000000000000
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
- Wall-clock time
- 28 hours
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- 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 (crystal elasticity model) was published by University of California San Diego, in the country recorded as United States of America, during April 2019. The publishing organisation is categorised as academia.
It works in the domain of Materials science, and is recorded as performing the task of molecular property prediction.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
The training run consumed about 4.5 × 10¹⁷ FLOP, on hardware recorded as NVIDIA GeForce GTX 1080 Ti. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It was trained on a corpus of about 4,664 tokens of text.
Answers
MEGNet (crystal elasticity model) — common questions
MEGNet (crystal elasticity model)— 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.
MEGNet (crystal elasticity model)— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
MEGNet (crystal elasticity model)— how many parameters does it have?
It has a parameter count of 26.1K. Calculations here: https://docs.google.com/document/d/1BTmyZ9KVTIwkp9z9tRRFsWM5A0QcXiufXrIhujffyso/edit?tab=t.0#heading=h.913mrln2g0cv. 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.
MEGNet (crystal elasticity model)— who created it?
It was published by University of California San Diego, based in United States of America, an organisation categorised as academia.
MEGNet (crystal elasticity model)— when was it released?
It 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.
MEGNet (crystal elasticity model)— what is it used for?
It works in the domain of Materials science, and is recorded as handling the task of molecular property prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
MEGNet (crystal elasticity model)— how much compute was used to train it?
Training consumed around 4.5 × 10¹⁷ FLOP, on hardware recorded as 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.
The other direction
Looking at it from the other side?
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