UMA — Universal Models for Atoms

UMA is a family of universal machine-learning interatomic potentials developed by Meta's FAIR Chemistry team. Trained on approximately 500 million unique 3D atomic structures spanning molecules, materials, and catalysts, UMA is designed to serve as a single universal model that handles diverse chemical domains without requiring fine-tuning.

MAPLE defaults to UMA v1.2 (uma-s-1p2), which offers ~50% faster inference and ~40% improved accuracy on molecular systems compared to v1.1.

Configuration

Basic usage with all defaults (task = omol, size = uma-s-1p2):

#model=uma

Specify task and/or size explicitly:

#model=uma(task=omat, size=uma-s-1p2)

Both parameters are optional and can be used independently:

# Only change task (size stays at default uma-s-1p2)
#model=uma(task=oc20)

# Only change size (task stays at default omol)
#model=uma(size=uma-m-1p1)

Model Sizes

Three checkpoints are available, offering different tradeoffs between speed and accuracy:

Checkpoint	Version	Active Params	Total Params	Notes
`uma-s-1p2`	v1.2	6.6M	~150M	Default. Latest and fastest; ~50% faster, ~40% more accurate than v1.1
`uma-s-1p1`	v1.1	6.6M	~150M	Previous stable release; use if v1.2 compatibility issues arise
`uma-m-1p1`	v1.1	~50M	~1.4B	Higher accuracy across all benchmarks; slower and more GPU memory

Tip

For most use cases, the default uma-s-1p2 provides the best balance of speed and accuracy. Switch to uma-m-1p1 when you need the highest possible accuracy and can tolerate slower evaluation.

Task Domains

UMA is a multi-task model trained on different DFT methodologies. The task parameter selects which domain-specific head to use, matching the DFT level of theory for your system type:

Task	Domain	DFT Method	Typical Applications
`omol`	Organic Molecules	ωB97M-V / def2-TZVPD	Drug design, polymers, biochemistry
`omat`	Inorganic Materials	PBE / PBE+U	Photovoltaics, batteries, superconductors
`oc20`	Heterogeneous Catalysis	RPBE	Fuel cells, renewable energy, surface reactions
`odac`	MOFs / Direct Air Capture	PBE+D3	Carbon capture, gas adsorption, porous materials
`omc`	Molecular Crystals	PBE+D3	Organic electronics, pharmaceutical crystals
`oc22`	Oxide Catalysis	PBE+U (spin-polarized)	Oxide surfaces, electrocatalysis on oxides
`oc25`	Electrolyte Interfaces	RPBE+D3	Electrochemistry, solid-liquid interfaces

Important

The oc22 and oc25 tasks are only available with UMA v1.2 checkpoints (uma-s-1p2). Using these tasks with v1.1 checkpoints will result in an error.

Charge and Spin

UMA supports charge and spin multiplicity for the omol task. Specify them in the coordinate block:

#model=uma
#opt(method=lbfgs)

-1 2
O    0.000000    0.000000    0.000000
O    1.210000    0.000000    0.000000

The first line of the coordinate block is charge multiplicity (here: charge = −1, multiplicity = 2 for a doublet superoxide anion).

Warning

Charge and spin are only meaningful for the omol task. Other tasks (materials, catalysis) do not use these parameters.

Usage Examples

Default: molecular geometry optimization

# Default: uma-s-1p2 checkpoint, omol task
#model=uma
#device=gpu0
#opt(method=lbfgs)

0 1
C    0.000000    0.000000    0.000000
O    1.200000    0.000000    0.000000
H   -0.540000    0.940000    0.000000
H   -0.540000   -0.940000    0.000000

Inorganic material optimization (omat)

#model=uma(task=omat)
#device=gpu0
#opt(method=lbfgs)

XYZ /path/to/material.xyz

Catalysis with medium model for higher accuracy

#model=uma(task=oc20, size=uma-m-1p1)
#device=gpu0
#opt(method=lbfgs)

XYZ /path/to/catalyst_surface.xyz

Molecular crystal with v1.1 fallback

#model=uma(task=omc, size=uma-s-1p1)
#device=gpu0
#opt(method=lbfgs)

XYZ /path/to/crystal.xyz

Transition state search with UMA

#model=uma
#device=gpu0
#ts(method=neb)

XYZ /path/to/reactant.xyz

XYZ /path/to/product.xyz

With implicit solvation (GBSA)

#model=uma
#device=gpu0
#solv(method=gbsa, implicit=water)
#opt(method=lbfgs)

0 1
C    0.000000    0.000000    0.000000
O    1.200000    0.000000    0.000000
H   -0.540000    0.940000    0.000000
H   -0.540000   -0.940000    0.000000

Compatibility Notes

D4 dispersion: Not supported with UMA. The model's training data already includes dispersion-corrected DFT for relevant tasks (PBE+D3 for omc, odac, oc25).
GBSA solvation: Fully supported. QEq charges are computed automatically when implicit solvation is enabled.
All MAPLE job types are compatible with UMA: single point, optimization, transition state search, IRC, frequency, PES scan, and molecular dynamics.
Requires fairchem-core Python package (v2.9+) and a HuggingFace account with access to the facebook/UMA repository.