Models & Backends
*****************


Included (pre-parameterized) models
===================================

A **model** is the combination of a functional form with a set of
parameters. A number of pre-parameterized models can be selected in
AMS.

+-----------------------------------+-----------------------------------+-----------------------------------+
| Model                             | Primary Domain                    | Backend                           |
|===================================|===================================|===================================|
| AIMNet2-B973c                     | (Charged) Organic Molecules,      | AIMNet2                           |
|                                   | Organohalides                     |                                   |
+-----------------------------------+-----------------------------------+-----------------------------------+
| AIMNet2-wB97MD3                   | (Charged) Organic Molecules,      | AIMNet2                           |
|                                   | Organohalides                     |                                   |
+-----------------------------------+-----------------------------------+-----------------------------------+
| ANI-1ccx                          | Organic Molecules                 | TorchANI                          |
+-----------------------------------+-----------------------------------+-----------------------------------+
| ANI-1x                            | Organic Molecules                 | TorchANI                          |
+-----------------------------------+-----------------------------------+-----------------------------------+
| ANI-2x                            | Organic Molecules                 | TorchANI                          |
+-----------------------------------+-----------------------------------+-----------------------------------+
| eSEN-S-Con-OMol                   | Organic Molecules, Biomolecules,  | FAIRChem                          |
|                                   | Metal Complexes, Electrolytes     |                                   |
+-----------------------------------+-----------------------------------+-----------------------------------+
| M3GNet-UP-2022                    | Materials                         | M3GNet                            |
+-----------------------------------+-----------------------------------+-----------------------------------+
| MACE-MP-0-Large                   | Materials                         | MACE                              |
+-----------------------------------+-----------------------------------+-----------------------------------+
| MACE-MP-0-Medium                  | Materials                         | MACE                              |
+-----------------------------------+-----------------------------------+-----------------------------------+
| MACE-MP-0-Small                   | Materials                         | MACE                              |
+-----------------------------------+-----------------------------------+-----------------------------------+
| MACE-MPA-0                        | Materials                         | MACE                              |
+-----------------------------------+-----------------------------------+-----------------------------------+
| UMA-S-1.2-OC20                    | Adsorbates on Catalytic Surfaces  | FAIRChem                          |
+-----------------------------------+-----------------------------------+-----------------------------------+
| UMA-S-1.2-OC22                    | Adsorbates on Catalytic Oxide     | FAIRChem                          |
|                                   | Surfaces                          |                                   |
+-----------------------------------+-----------------------------------+-----------------------------------+
| UMA-S-1.2-OC25                    | Solvents, Ions and Electrolytes   | FAIRChem                          |
|                                   | with Catalytic Surfaces           |                                   |
+-----------------------------------+-----------------------------------+-----------------------------------+
| UMA-S-1.2-ODAC                    | CO_2/H_2O adsorbed in MOFs        | FAIRChem                          |
+-----------------------------------+-----------------------------------+-----------------------------------+
| UMA-S-1.2-OMat                    | Inorganic Materials               | FAIRChem                          |
+-----------------------------------+-----------------------------------+-----------------------------------+
| UMA-S-1.2-OMC                     | Organic Molecular Crystals        | FAIRChem                          |
+-----------------------------------+-----------------------------------+-----------------------------------+
| UMA-S-1.2-OMol                    | Organic Molecules, Biomolecules,  | FAIRChem                          |
|                                   | Metal Complexes, Electrolytes     |                                   |
+-----------------------------------+-----------------------------------+-----------------------------------+


AIMNet2 Models
--------------

AIMNet2 (Atoms In Molecules Network) is a neural network potential
designed for accurate predictions of molecular geometries and reaction
energies for both neutral and gas-phase charged organic molecules. It
incorporates explicit long-range electrostatics and dispersion
contributions and uses charge equilibration within the message passing
framework, enabling improved performance for systems where long-range
interactions are important.

Two pre-trained models are available in AMS: AIMNet2-wB97MD3 and
AIMNet2-B973c [1]. The difference between them is that AIMNet2-wB97MD3
is trained to more expensive and accurate ωB97M-D3/def2-TZVPP DFT
reference data, whereas AIMNet2-B973c uses only B97-3c reference data.


Best for
~~~~~~~~

* Fast calculations of small, drug-like molecules to routine DFT level
  accuracy

* Predictions of atomic charges and dipole moments


Limitations
~~~~~~~~~~~

* Does not support periodic systems

* Restricted to 14 elements (H, B, C, N, O, F, Si, P, S, Cl, As, Se,
  Br, I)


Training data
~~~~~~~~~~~~~

+--------------------------------+------------------------------------------------------------------------+
| Dataset                        | 20 million conformers, including charged species, distilled from an    |
|                                | initial pool of 120 million                                            |
+--------------------------------+------------------------------------------------------------------------+
| Reference methods              | B97-3c (AIMNet2-B973c), ωB97M-D3/def2-TZVPP (AIMNet2-wB97MD3)          |
+--------------------------------+------------------------------------------------------------------------+
| Included elements              | H, B, C, N, O, F, Si, P, S, Cl, As, Se, Br, I                          |
+--------------------------------+------------------------------------------------------------------------+


Notes
~~~~~

* Predictions from AIMNet2-B973c are calculated from **committees**
  (ensembles), meaning that the final prediction is an average over
  multiple independently trained neural networks.


Examples
~~~~~~~~

An example AMS input file for the geometry optimization of
chloromethane with AIMNet2-wB97MD3 is as follows:

   #!/bin/sh

   export NSCM=1

   "$AMSBIN/ams" --delete-old-results << eor
   Task GeometryOptimization

   System
       Atoms
           C -0.13367473200681762 0.0032536323351088866 0.001112839342431487
           Cl 1.6437227908091656 -0.04000788304685571 -0.013683802158888616
           H -0.5076353557849584 -0.4795638958097295 0.9272936171699165
           H -0.5242024748732897 -0.5409765935860863 -0.8832353361874874
           H -0.47821022814410086 1.0572947401075619 -0.03148731816597247
       End
   End

   Engine MLPotential
       Model AIMNet2-wB97MD3
   EndEngine
   eor


See also further examples.


ANI Models
----------

ANI (Accurate NeurAl networK engINe for Molecular Energies) is a
family of neural network potentials for efficient and accurate
prediction of molecular geometries, vibrational frequencies, and
reaction energies for gas-phase organic molecules.

Three pre-trained models are available in AMS: ANI-1x [2], ANI-1ccx
[3], and ANI-2x [4]. ANI-1x and ANI-2x are trained to ωB97X/6-31G(d)
DFT reference data, while ANI-1ccx is trained using transfer learning
to target coupled-cluster quality reference energies CCSD(T)*/CBS.


Best for
~~~~~~~~

* Very fast calculations of organic molecules (ANI-1x, ANI-1ccx), also
  including light halogens and sulfur (ANI-2x)


Limitations
~~~~~~~~~~~

* Restricted to a small set of light elements

* Does not support charged systems


Training data
~~~~~~~~~~~~~

+--------------------------------+------------------------------------------------------------------------+
| Dataset                        | 5.5 million conformers of small organic molecules (ANI-1x),            |
|                                | supplemented with additional conformers containing light elements to   |
|                                | make 8.9 million in total (ANI-2x)                                     |
+--------------------------------+------------------------------------------------------------------------+
| Reference methods              | ωB97X/6-31G(d) (ANI-1x, ANI-2x), DLPNO-CCSD(T)/CBS (ANI-1ccx)          |
+--------------------------------+------------------------------------------------------------------------+
| Included elements              | H, C, N, O (ANI-1x, ANI-1ccx), F, S, Cl (ANI-2x)                       |
+--------------------------------+------------------------------------------------------------------------+


Notes
~~~~~

* Predictions from ANI models are calculated from **committees**
  (ensembles), meaning that the final prediction is an average over
  multiple independently trained neural networks.


Examples
~~~~~~~~

An example AMS input file for the geometry optimization of methane
with ANI-1ccx is as follows:

   #!/bin/sh

   export NSCM=1

   "$AMSBIN/ams" --delete-old-results << eor
   Task GeometryOptimization

   System
       Atoms
           C 2.9166023165223268e-09 6.006194625417838e-09 7.204071579427031e-10
           H 0.5389120953335456 0.7623581289443943 -0.5992945752094279
           H 0.7312440919932264 -0.5966159238699776 0.5831823400796503
           H -0.5671285553273658 -0.6703024318238477 -0.6781076404295054
           H -0.7030276349160114 0.5045602207432393 0.6942198748388725
       End
   End

   Engine MLPotential
       Model ANI-1ccx
   EndEngine
   eor



eSEN Models
-----------

eSEN (Equivariant Smooth Energy Network) is a neural network potential
developed for highly accurate computation of energies and forces for
molecules with diverse chemistries. This includes the modeling of
charged and open-shell systems.

In AMS, the pre-trained eSEN-S-Con-OMol [5] (con=conserving) model is
provided, which is trained on the OMol25 dataset, designed to cover a
very broad range of organic and bio-relevant molecules, including
metal complexes and electrolytes.


Best for
~~~~~~~~

* Highly accurate calculations of diverse organic and bio-relevant
  molecules

* Molecular systems containing main-group elements and many heavier
  elements up to Bi

* Inclusion of system charge and spin-multiplicity


Limitations
~~~~~~~~~~~

* Not intended for calculations on periodic inorganic bulk materials


Training data
~~~~~~~~~~~~~

+--------------------------------+------------------------------------------------------------------------+
| Dataset                        | OMol25 dataset comprising over 100 million structures covering small   |
|                                | molecules, biomolecules, metal complexes, and electrolytes             |
+--------------------------------+------------------------------------------------------------------------+
| Reference methods              | wB97M-V/def2-TZVPD, including non-local dispersion                     |
+--------------------------------+------------------------------------------------------------------------+
| Included elements              | Elements from H .. Bi                                                  |
+--------------------------------+------------------------------------------------------------------------+


Notes
~~~~~

* All training data is aperiodic, so any periodic systems should be
  treated with some caution

* eSEN models are made accessible for commercial and non-commercial
  use under the permissive FAIRChem license, which applies when using
  these models


Examples
~~~~~~~~

An example AMS input file for the geometry optimization of methane
with eSEN-S-Con-OMol is as follows:

   #!/bin/sh

   export NSCM=1

   "$AMSBIN/ams" --delete-old-results << eor
   Task GeometryOptimization

   System
       Atoms
           C 2.9166023165223268e-09 6.006194625417838e-09 7.204071579427031e-10
           H 0.5389120953335456 0.7623581289443943 -0.5992945752094279
           H 0.7312440919932264 -0.5966159238699776 0.5831823400796503
           H -0.5671285553273658 -0.6703024318238477 -0.6781076404295054
           H -0.7030276349160114 0.5045602207432393 0.6942198748388725
       End
   End

   Engine MLPotential
       Model eSEN-S-Con-OMol
   EndEngine
   eor


M3GNet Models
-------------

M3GNet (Materials based on Graph Neural Networks with three-body
interactions) is an interatomic potential designed for atomistic
simulations of periodic materials.

The M3GNet-UP-2022 [6] model available in AMS is intended to be
"universal", i.e., applicable to a broad range of materials containing
elements from across the periodic table, although the training data is
primarily made up of crystal data from inorganic materials from the
Materials Project [7].


Best for
~~~~~~~~

* Fast calculations of inorganic crystalline materials

* Periodic systems (bulk solids, surfaces, interfaces)


Limitations
~~~~~~~~~~~

* Not designed for accurately modeling small organic molecules or
  biomolecules


Training data
~~~~~~~~~~~~~

+--------------------------------+------------------------------------------------------------------------+
| Dataset                        | 187k structures from 63k materials from the Materials Project          |
+--------------------------------+------------------------------------------------------------------------+
| Reference methods              | PBE, PBE+U                                                             |
+--------------------------------+------------------------------------------------------------------------+
| Included elements              | Elements from H .. Pu (except Po .. Ra)                                |
+--------------------------------+------------------------------------------------------------------------+


Notes
~~~~~

* M3GNet-UP-2022 can be fine-tuned with ParAMS


Examples
~~~~~~~~

An example AMS input file for the geometry optimization of methane
with M3GNet-UP-2022 is as follows:

   #!/bin/sh

   export NSCM=1

   "$AMSBIN/ams" --delete-old-results << eor
   Task GeometryOptimization

   System
       Atoms
           Na 0.0 0.0 0.0 
           Cl 2.815 2.815 2.815 
       End
       Lattice
           0.0 2.815 2.815
           2.815 0.0 2.815
           2.815 2.815 0.0
       End
   End

   Engine MLPotential
       Model M3GNet-UP-2022
   EndEngine
   eor

See also further examples.


MACE Models
-----------

MACE (Message Passing Atomic Cluster Expansion) [8] is a family of
equivariant neural network interatomic potentials designed for
accurate prediction of energies and forces in atomistic simulations.

MACE-MP-0 and MACE-MPA-0 models [9] available in AMS are pre-trained
foundation potentials targeting inorganic materials chemistry and are
intended for periodic systems. For MACE-MP-0, multiple model sizes are
provided (Small, Medium, Large), offering a trade-off between
computational cost and accuracy. MACE-MPA-0 is equivalent in size to
"Medium", but is trained on a larger dataset with additional crystal
structures for improved accuracy.


Best for
~~~~~~~~

* Accurate periodic calculations of inorganic materials

* Tuning speed/accuracy via Small/Medium/Large variants


Limitations
~~~~~~~~~~~

* Not designed for accurately modeling small organic molecules or
  biomolecules


Training data
~~~~~~~~~~~~~

+--------------------------------+------------------------------------------------------------------------+
| Dataset                        | Materials Project MPtraj dataset comprising 1.58 million structures    |
|                                | from 146k materials (MACE-MP-0), supplemented with the sAlex dataset   |
|                                | comprising a further 10.4 million structures from 3.23 million         |
|                                | materials (MACE-MPA)                                                   |
+--------------------------------+------------------------------------------------------------------------+
| Reference methods              | PBE+U                                                                  |
+--------------------------------+------------------------------------------------------------------------+
| Included elements              | Elements from H .. Pu (except Po .. Ra)                                |
+--------------------------------+------------------------------------------------------------------------+


Notes
~~~~~

* MACE-MP-0 and MACE-MPA-0 can be fine-tuned with ParAMS


Examples
~~~~~~~~

An example AMS input file for the geometry optimization of methane
with MACE-MPA-0 is as follows:

   #!/bin/sh

   export NSCM=1

   "$AMSBIN/ams" --delete-old-results << eor
   Task GeometryOptimization

   System
       Atoms
           Na 0.0 0.0 0.0 
           Cl 2.815 2.815 2.815 
       End
       Lattice
           0.0 2.815 2.815
           2.815 0.0 2.815
           2.815 2.815 0.0
       End
   End

   Engine MLPotential
       Model MACE-MPA-0
   EndEngine
   eor

See also further examples.


UMA Models
----------

UMA (Universal Model for Atoms) [10] is a foundation neural network
potential with models trained on large-scale atomistic datasets
spanning molecules, materials, surfaces, adsorption systems, and
molecular crystals, which are intended to provide broad
transferability across diverse chemistry.

Several pre-trained UMA variants are available in AMS. Each variant is
specialized towards a particular training domain while retaining a
shared underlying model architecture.

These include:

* UMA-S-1.2-OC20: adsorbates on catalytic surfaces (solid-gas
  interfaces)

* UMA-S-1.2-OC22: adsorbates on catalytic oxide surfaces (solid-gas
  interfaces)

* UMA-S-1.2-OC25: solvent layers, ions and electrolytes with catalytic
  surfaces (solid-liquid interfaces)

* UMA-S-1.2-ODAC: CO_2/H_2O adsorbed in MOFs

* UMA-S-1.2-OMat: inorganic materials

* UMA-S-1.2-OMC: organic molecular crystals

* UMA-S-1.2-OMol: organic molecules, biomolecules, metal complexes,
  electrolytes


Best for
~~~~~~~~

* High accuracy calculations on a broad range of systems including
  molecules (OMol) and inorganic materials (OMat) with diverse
  chemistry

* Charged / open-shell molecules including radicals (OMol)

* Adsorption and surface chemistry with solid-gas interfaces (OC20),
  oxide materials (OC22) and electrocatalysts with solid-liquid
  interfaces (OC25)

* Porous framework adsorption (ODAC)

* Organic molecular crystals (OMC)


Limitations
~~~~~~~~~~~

* Predictions are best within the dominant chemistry represented in
  the chosen UMA variant

* Relatively computationally expensive compared to other, more
  targeted models


Training data
~~~~~~~~~~~~~

+--------------------------------+------------------------------------------------------------------------+
| Model                          | UMA-S-1.2-OC20                                                         |
+--------------------------------+------------------------------------------------------------------------+
| Dataset                        | OC20 dataset comprising >100 million calculations of small molecules   |
|                                | adsorbed on catalyst surfaces formed from materials in the Materials   |
|                                | Project                                                                |
+--------------------------------+------------------------------------------------------------------------+
| Reference methods              | RPBE, no dispersion                                                    |
+--------------------------------+------------------------------------------------------------------------+
| Included elements              | Elements H .. Cl .. Bi (except other grp. 7, grp. 8 or Mg, Ba, Ln)     |
+--------------------------------+------------------------------------------------------------------------+

+--------------------------------+------------------------------------------------------------------------+
| Model                          | UMA-S-1.2-OC22                                                         |
+--------------------------------+------------------------------------------------------------------------+
| Dataset                        | OC22 dataset comprising ~10 million calculations of small molecules    |
|                                | adsorbed on catalyst surfaces formed from unary and binary oxide       |
|                                | materials in the Materials Project                                     |
+--------------------------------+------------------------------------------------------------------------+
| Reference methods              | PBE/PBE+U, no dispersion                                               |
+--------------------------------+------------------------------------------------------------------------+
| Included elements              | Elements Li, Be, O, Na, Mg, Al, Si, K .. Ce, Lu .. Bi (except grp. 7,  |
|                                | grp. 8, As, Tc, Te)                                                    |
+--------------------------------+------------------------------------------------------------------------+

+--------------------------------+------------------------------------------------------------------------+
| Model                          | UMA-S-1.2-OC25                                                         |
+--------------------------------+------------------------------------------------------------------------+
| Dataset                        | OC25 dataset comprising ~8 million calculations of solvents, ions and  |
|                                | electrolytes with catalyst surfaces formed from materials in the       |
|                                | Materials Project                                                      |
+--------------------------------+------------------------------------------------------------------------+
| Reference methods              | RPBE+D3                                                                |
+--------------------------------+------------------------------------------------------------------------+
| Included elements              | Elements H .. Pm, Hf .. Bi                                             |
+--------------------------------+------------------------------------------------------------------------+

+--------------------------------+------------------------------------------------------------------------+
| Model                          | UMA-S-1.2-ODAC                                                         |
+--------------------------------+------------------------------------------------------------------------+
| Dataset                        | ODAC23 dataset comprising >10 million calculations of CO_2/H_2O        |
|                                | molecules adsorbed in Metal Organic Frameworks sampled from various    |
|                                | open databases like CoreMOF                                            |
+--------------------------------+------------------------------------------------------------------------+
| Reference methods              | PBE+D3                                                                 |
+--------------------------------+------------------------------------------------------------------------+
| Included elements              | Elements H, Li .. Np (except grp. 8, K, Rb, Tc, In, Pm, Yb, Ta, Os,    |
|                                | Ir, Tl, Pb, Po .. Ac, Pa)                                              |
+--------------------------------+------------------------------------------------------------------------+

+--------------------------------+------------------------------------------------------------------------+
| Model                          | UMA-S-1.2-OMat                                                         |
+--------------------------------+------------------------------------------------------------------------+
| Dataset                        | OMat24 dataset comprising >100 million calculations of inorganic       |
|                                | materials collected from many open databases like Materials Project    |
|                                | and Alexandria, and randomly sampled far from equilibria               |
+--------------------------------+------------------------------------------------------------------------+
| Reference methods              | PBE/PBE+U, no dispersion                                               |
+--------------------------------+------------------------------------------------------------------------+
| Included elements              | Elements H .. Pu (except Po .. Ra)                                     |
+--------------------------------+------------------------------------------------------------------------+

+--------------------------------+------------------------------------------------------------------------+
| Model                          | UMA-S-1.2-OMC                                                          |
+--------------------------------+------------------------------------------------------------------------+
| Dataset                        | OMC25 dataset comprising ~25 million calculations of organic molecular |
|                                | crystals from random packing of OE62 structures into various 3D unit   |
|                                | cells                                                                  |
+--------------------------------+------------------------------------------------------------------------+
| Reference methods              | PBE+D3                                                                 |
+--------------------------------+------------------------------------------------------------------------+
| Included elements              | H, B, C, N, O, F, Si, P, S, Cl, Br, I                                  |
+--------------------------------+------------------------------------------------------------------------+

+--------------------------------+------------------------------------------------------------------------+
| Model                          | UMA-S-1.2-OMol                                                         |
+--------------------------------+------------------------------------------------------------------------+
| Dataset                        | OMol25 dataset comprising over 100 million structures covering small   |
|                                | molecules, biomolecules, metal complexes, and electrolytes.            |
|                                | Supplemented with OPoly26 dataset comprising ~6.35 million structures  |
|                                | of various polymeric systems including traditional polymers,           |
|                                | fluoropolymers, optical polymers, peptoids, lipids and polymer         |
|                                | electrolytes.                                                          |
+--------------------------------+------------------------------------------------------------------------+
| Reference methods              | wB97M-V/def2-TZVPD, including non-local dispersion                     |
+--------------------------------+------------------------------------------------------------------------+
| Included elements              | Elements from H .. Bi                                                  |
+--------------------------------+------------------------------------------------------------------------+


Notes
~~~~~

* A UMA variant should be selected based on the dominant system type

* Only UMA-S-1.2-OMol supports charged systems/spin multiplicity

* All UMA-S-1.2-OMol training data is aperiodic, so any periodic
  systems should be treated with some caution

* UMA models are made accessible for commercial and non-commercial use
  under the permissive FAIRChem license, which applies when using
  these models


Examples
~~~~~~~~

An example AMS input file for the geometry optimization of methane
with UMA-S-1.2-OMol is as follows:

   #!/bin/sh

   export NSCM=1

   "$AMSBIN/ams" --delete-old-results << eor
   Task GeometryOptimization

   System
       Atoms
           C 2.9166023165223268e-09 6.006194625417838e-09 7.204071579427031e-10
           H 0.5389120953335456 0.7623581289443943 -0.5992945752094279
           H 0.7312440919932264 -0.5966159238699776 0.5831823400796503
           H -0.5671285553273658 -0.6703024318238477 -0.6781076404295054
           H -0.7030276349160114 0.5045602207432393 0.6942198748388725
       End
   End

   Engine MLPotential
       Model UMA-S-1.2-OMol
   EndEngine
   eor

An example AMS input file for the geometry optimization of sodium
chloride with UMA-S-1.2-OMat is as follows:

   #!/bin/sh

   export NSCM=1

   "$AMSBIN/ams" --delete-old-results << eor
   Task GeometryOptimization

   System
       Atoms
           Na 0.0 0.0 0.0 
           Cl 2.815 2.815 2.815 
       End
       Lattice
           0.0 2.815 2.815
           2.815 0.0 2.815
           2.815 2.815 0.0
       End
   End

   Engine MLPotential
       Model UMA-S-1.2-OMat
   EndEngine
   eor

See also further examples.

"Model"
   Type:
      Multiple Choice

   Default value:
      ANI-2x

   Options:
      [Custom, AIMNet2-B973c, AIMNet2-wB97MD3, ANI-1ccx, ANI-1x, ANI-
      2x, eSEN-S-Con-OMol, M3GNet-UP-2022, MACE-MP-0-Large, MACE-
      MP-0-Medium, MACE-MP-0-Small, MACE-MPA-0, UMA-S-1.2-OC20,
      UMA-S-1.2-OC22, UMA-S-1.2-OC25, UMA-S-1.2-ODAC, UMA-S-1.2-OMat,
      UMA-S-1.2-OMC, UMA-S-1.2-OMol]

   Description:
      Select a pre-parameterized or custom model.
      AIMNet2-(wB97MD3/B973c): best for fast calculations of small,
      drug-like molecules; limited to aperiodic systems of 14 elements
      (H, B, C, N, O, F, Si, P, S, Cl, As, Se, Br, I).
      ANI-(1x/1ccx/2x): best for very fast calculations of organic
      molecules; limited to elements H, C, N, O (ANI-1x/1ccx), F, S,
      Cl (ANI-2x).                   eSEN-S-Con-OMol: best for highly
      accurate calculations of diverse organic and bio-relevant
      molecules; not intended for calculations on periodic inorganic
      bulk materials.                   M3GNet-UP-2022: best for fast
      calculations of inorganic crystalline materials; not designed
      for accurately modeling small organic molecules or biomolecules.
      MACE-MP-0-(Small/Medium/Large): best for accurate periodic
      calculations of inorganic materials; size trades speed/accuracy;
      not designed for accurately modeling small organic molecules or
      biomolecules. MACE-MPA-0 has improved accuracy vs MP-0.
      UMA-S-1.2 variants: best for high accuracy calculations on a
      broad range of systems; choose from OC20 (adsorption and surface
      chemistry), OC22 (oxide catalysis), OC25 (electrocatalysis),
      ODAC (adsorption in porous frameworks), OMat (inorganic
      materials), OMC (organic molecular crystals), OMol (molecules,
      biomolecules, metal complexes, electrolytes); can be
      computationally expensive compared to other, more targeted
      models.                   Set Custom to choose a backend and
      provide your own parameters.


Custom models (custom parameters)
=================================

Note:

  You can use Engine ASE to use any ASE calculator as the engine.

Note:

  You can use ParAMS to train your own ML potential parameters.

Set "Model" to **Custom** and specify which backend to use with the
"Backend" option. In a typical case, you would have used that backend
to train your own machine learning potential.

The backend reads the parameters, and any other necessary information
(for example neural network architecture), from either a file or a
directory. Specify the "ParameterFile" or "ParameterDir" option
accordingly, with a path to the file or directory. Read the backend's
documentation to find out which option is appropriate.

Example:

   Engine MLPotential
       Backend MACE
       Model Custom
       ParameterFile mace-custom.model
   EndEngine

"Backend"
   Type:
      Multiple Choice

   Options:
      [FAIRChem, M3GNet, MACE, NequIP, TorchANI]

   Description:
      The machine learning potential backend.

"ParameterDir"
   Type:
      String

   Default value:
   GUI name:
      Parameter directory

   Description:
      Path to a set of parameters for the backend, if it expects to
      read from a directory.

"ParameterFile"
   Type:
      String

   Default value:
   Description:
      Path to a set of parameters for the backend, if it expects to
      read from a file.


Backends
========


Backends supported by the MLPotential engine.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

+----------------------+----------------------+----------------------+----------------------+----------------------+----------------------+
|                      | FAIRChem             | M3GNet               | MACE                 | NequIP               | TorchANI             |
|======================|======================|======================|======================|======================|======================|
| Reference            | [10]                 | [6]                  | [8]                  | [11]                 | [12]                 |
+----------------------+----------------------+----------------------+----------------------+----------------------+----------------------+
| Parameters from      | ParameterFile        | ParameterDir         | ParameterFile        | ParameterFile        | ParameterFile        |
+----------------------+----------------------+----------------------+----------------------+----------------------+----------------------+
| Included models      | eSEN-S-Con-OMol,     | M3GNet-UP-2022       | MACE-MP-0, MACE-     | none                 | ANI-1x, ANI-2x, ANI- |
|                      | UMA-S-1.2-OC20,      |                      | MPA-0                |                      | 1ccx                 |
|                      | UMA-S-1.2-OC22,      |                      |                      |                      |                      |
|                      | UMA-S-1.2-OC25,      |                      |                      |                      |                      |
|                      | UMA-S-1.2-ODAC,      |                      |                      |                      |                      |
|                      | UMA-S-1.2-OMat,      |                      |                      |                      |                      |
|                      | UMA-S-1.2-OMC,       |                      |                      |                      |                      |
|                      | UMA-S-1.2-OMol       |                      |                      |                      |                      |
+----------------------+----------------------+----------------------+----------------------+----------------------+----------------------+
| ML framework         | PyTorch 2.8.0        | TensorFlow 2.9.1     | PyTorch 2.8.0        | PyTorch 2.8.0        | PyTorch 2.8.0        |
+----------------------+----------------------+----------------------+----------------------+----------------------+----------------------+

Note:

  Technically, there is also an AIMNet2 backend but it can only be
  activated through the pre-parametrized models AIMNet2-B973c and
  AIMNet2-wB97MD3.

Note:

  Starting with AMS2023, PiNN [14] is only supported as a custom
  Calculator through Engine ASE [13].Starting with AMS2026, SchNetPack
  [15] and sGDML [16] are also only supported as a custom Calculator
  through Engine ASE [13].

Note:

  If you use a custom parameter file with **TorchANI**, the model
  specified via "ParameterFile filename.pt" is loaded with
  "torch.load('filename.pt')['model']", such that a forward call
  should be accessible via
  "torch.load('filename.pt')['model']((species, coordinates))". The
  energy shifter is not read from custom parameter files, so the
  absolute predicted energies will be shifted with respect to the
  reference data, but this does not affect relative energies (e.g.,
  reaction energies).


References
==========

[1] D. M. Anstine et al., Chem Sci. 16 (2025) 10228–10244.
    https://doi.org/10.1039/D4SC08572H

[2] J. S. Smith et al., J. Chem. Phys. 148 (2018) 241733.
    https://doi.org/10.1063/1.5023802

[3] J. S. Smith et al., Nat. Commun. 10 (2019) 2903.
    https://doi.org/10.1038/s41467-019-10827-4

[4] C. Devereux et al., J. Chem. Theory Comput. 16 (2020) 4192-4202.
    https://doi.org/10.1021/acs.jctc.0c00121

[5] D. S. Levine et al., arXiv:2505.08762 (2025).
    https://arxiv.org/abs/2505.08762

[6] C. Chen, S. P. Ong. Nature Computational Science 2, 718–728
    (2022). https://doi.org/10.48550/arXiv.2202.02450

[7] A. Jain et al., APL Materials 1 (2013) 011002.
    https://doi.org/10.1063/1.4812323

[8] I. Batatia et al., Advances in Neural Information Processing
    Systems (2022). https://openreview.net/forum?id=YPpSngE-ZU

[9] I. Batatia et al., arXiv:2401.00096 (2023).
    https://arxiv.org/abs/2401.00096

[10] B. M. Wood et al., arXiv:2506.23971 (2025).
     https://arxiv.org/abs/2506.23971

[11] S. Batzner et al., Nat. Commun. 13 (2022) 2453.
     https://doi.org/10.1038/s41467-022-29939-5

[12] X. Gao et al. J. Chem. Inf. Model (2020).
     https://doi.org/10.1021/acs.jcim.0c00451

[13] https://wiki.fysik.dtu.dk/ase/index.html

[14] Y. Shao et al., J. Chem. Inf. Model. 60 (2020) 1184-1193.
     https://doi.org/10.1021/acs.jcim.9b00994

[15] K. T. Schütt et al., J. Chem. Theory Comput. 15 (2019) 448-455.
     https://doi.org/10.1021/acs.jctc.8b00908

[16] S. Chmiela et al. Comp. Phys. Commun. 240 (2019) 38-45.
     https://doi.org/10.1016/j.cpc.2019.02.007
