Enum: MachineLearnedPotentialName

Machine learned potentials.

URI: https://CCPBioSim.ac.uk/biosim-schema/MachineLearnedPotentialName

Permissible Values

Value	Meaning	Description
MACE	None	Utilizes high-body-order expansions and provides exceptional force accuracy, …
ANI	None	Neural network potentials specifically parameterized for organic and small-mo…
NequIP	None	E(3)-equivariant network that models local atomic environments as tensors
UMA	None	Large-scale framework spanning half a billion atomic structures, and generali…
AceFF	None	Specifically optimized for small molecule drug discovery

Slots

Name	Description
machine_learned_potential_name	ML force field used to describe molecules

Identifier and Mapping Information

Schema Source

• from schema: https://CCPBioSim.ac.uk/biosim-schema/

LinkML Source

name: MachineLearnedPotentialName
description: Machine learned potentials.
from_schema: https://CCPBioSim.ac.uk/biosim-schema/
rank: 1000
permissible_values:
  MACE:
    text: MACE
    description: Utilizes high-body-order expansions and provides exceptional force
      accuracy, excelling at capturing anharmonic dynamics in organic and macromolecular
      systems.
    aliases:
    - MACE
    - Multi-Atomic Cluster Expansion
  ANI:
    text: ANI
    description: Neural network potentials specifically parameterized for organic
      and small-molecule drug-like spaces (covering H, C, N, O).
    aliases:
    - ANI
    - Accurate Neural net Interaction
  NequIP:
    text: NequIP
    description: E(3)-equivariant network that models local atomic environments as
      tensors.
    aliases:
    - NequIP
    - Neural Equivariant Interatomic Potential
  UMA:
    text: UMA
    description: Large-scale framework spanning half a billion atomic structures,
      and generalizes across different chemical environments.
    aliases:
    - UMA
    - Universal Model for Atoms
  AceFF:
    text: AceFF
    description: Specifically optimized for small molecule drug discovery
    aliases:
    - AceFF