MACE-MP-0
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 Cambridge,Federal Institute of Materials Research and Testing (BAM),NERSC, Lawrence Berkeley National Laboratory,University of British Columbia (UBC),Friedrich Schiller University Jena,University of Bayreuth,Fritz Haber Institute of the Max Planck Society,U. S. Naval Research Laboratory,Chemix,Daresbury Laboratory,BASF,University of South Carolina,University of Stuttgart,Uppsala University,Newcastle University,Technical University of Denmark,Aix-Marseille Université,University of Warwick,University of California Los Angeles (UCLA),InstaDeep,University of California (UC) Berkeley
- Organisation type
- Academia,Academia,Government,Academia,Academia,Academia,Academia,Government,Industry,Academia,Industry,Academia,Academia,Academia,Academia,Academia,Academia,Academia,Academia,Industry,Academia
- Country
- United Kingdom of Great Britain and Northern Ireland, Germany, United States of America, Canada, Sweden, Denmark, France
- Published
- 1 March 2024
- Authors
- Ilyes Batatia, Philipp Benner, Yuan Chiang, Alin M. Elena, Dávid P. Kovács, Janosh Riebesell, Xavier R. Advincula, Mark Asta, Matthew Avaylon, William J. Baldwin, Fabian Berger, Noam Bernstein, Arghya Bhowmik, Samuel M. Blau, Vlad Cărare, James P. Darby, Sandip De, Flaviano Della Pia, Volker L. Deringer, Rokas Elijošius, Zakariya El-Machachi, Fabio Falcioni, Edvin Fako, Andrea C. Ferrari, Annalena…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Materials science
- Task
- 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
- Epochs
- 200
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
- 8.8 × 10²⁰ FLOP
- How it was established
- Hardware
312000000000000*2600*3600*0.3 = 8.76096e+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
- 80
- Chip-hours
- 2,600
- Power draw
- 63.3 kW
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
MIT License https://github.com/ACEsuit/mace-mp https://github.com/ACEsuit/mace/
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- A foundation model for atomistic materials chemistry
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
MACE-MP-0 was published by University of Cambridge,Federal Institute of Materials Research and Testing (BAM),NERSC, Lawrence Berkeley National Laboratory,University of British Columbia (UBC),Friedrich Schiller University Jena,University of Bayreuth,Fritz Haber Institute of the Max Planck Society,U. S. Naval Research Laboratory,Chemix,Daresbury Laboratory,BASF,University of South Carolina,University of Stuttgart,Uppsala University,Newcastle University,Technical University of Denmark,Aix-Marseille Université,University of Warwick,University of California Los Angeles (UCLA),InstaDeep,University of California (UC) Berkeley, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during March 2024. It comes out of an organisation categorised as academia,Academia,Government,Academia,Academia,Academia,Academia,Government,Industry,Academia,Industry,Academia,Academia,Academia,Academia,Academia,Academia,Academia,Academia,Industry,Academia.
It works in the domain of Materials science, and is recorded as performing the task of molecular simulation.
The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.
What went into building it
Producing it required arithmetic totalling around 8.8 × 10²⁰ FLOP, on hardware recorded as NVIDIA A100. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
MACE-MP-0 — common questions
MACE-MP-0— when was it released?
It was published in March 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.
MACE-MP-0— what is it used for?
It works in the domain of Materials science, and is recorded as handling the task of molecular simulation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
MACE-MP-0— where can I download it?
The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
MACE-MP-0— how much compute was used to train it?
Training consumed around 8.8 × 10²⁰ FLOP, on hardware recorded as 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.
MACE-MP-0— what GPU do I need to run it?
We cannot say. It 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.
MACE-MP-0— is it open source?
Its weights are published, so it 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.
MACE-MP-0— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
MACE-MP-0— who created it?
It was published by University of Cambridge,Federal Institute of Materials Research and Testing (BAM),NERSC, Lawrence Berkeley National Laboratory,University of British Columbia (UBC),Friedrich Schiller University Jena,University of Bayreuth,Fritz Haber Institute of the Max Planck Society,U. S. Naval Research Laboratory,Chemix,Daresbury Laboratory,BASF,University of South Carolina,University of Stuttgart,Uppsala University,Newcastle University,Technical University of Denmark,Aix-Marseille Université,University of Warwick,University of California Los Angeles (UCLA),InstaDeep,University of California (UC) Berkeley, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as academia,Academia,Government,Academia,Academia,Academia,Academia,Government,Industry,Academia,Industry,Academia,Academia,Academia,Academia,Academia,Academia,Academia,Academia,Industry,Academia.
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.