MEGNet (crystal band gap 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
- 26.1K
- Training data
- 36,720 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.lk6l1te7vrkv 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
- 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
- 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
What this model is
MEGNet (crystal band gap 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.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Training it took a computation budget of roughly 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.
The training set ran to roughly 36,720 tokens of text.
Answers
MEGNet (crystal band gap model) — common questions
MEGNet (crystal band gap 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 band gap 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 band gap 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 band gap 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 band gap 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. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
MEGNet (crystal band gap 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.
MEGNet (crystal band gap 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.
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.