Potential

eOn supports many potentials, some vendored within the executable and libraries and others via interfaces.

Note

Some of these require compile-time flags, detailed in the installation instructions. The conda-forge package (conda install -c conda-forge eon) includes Metatomic, XTB, EXT_POT, and the vendored potentials. LAMMPS, ASE, VASP, AMS, and MPI potentials require building from source with the corresponding -Dwith_* flags.

Supported potentials

External

VASP [POT_KFurthmuller96]

Vienna Ab-Initio Simulation Program (VASP) I/O interface. source build

LAMMPS [POT_Pli95, POT_TAB+22]

Library interface, detailed documentation here. source build

EXT_POT

File-based interface to any external calculator. Detailed documentation here. conda-forge

Added in version 2.0:

AMS(-IO)

Amsterdam modeling suite [POT_teVeldeBB+01], both I/O and library. source build ASE_ORCA

Atomic simulation environment [POT_LMB+17] interface to ORCA [POT_NWBR20]. source build ASE_NWChem

Atomic simulation environment [POT_LMB+17] interface to NWChem [POT_ApraBDJ+20]. source build XTB

Extended Tight binding models via native Fortran-C interfce [POT_BCE+21]. conda-forge Metatomic

Common interface to atomistic machine learning models. conda-forge SocketNWChem

Socket oriented communicator for efficient integration with NWChem [POT_ApraBDJ+20]. conda-forge

Vendored

CuH2

Copper Hydride system

FeHe

Iron-hydrides

EAM_Al

Embedded atom method parameterized for Aluminum.

EMT

Effective medium theory, for metals.

LJ [POT_Jon24]

Lennard-Jones in reduced units, served by rgpot. Neighbor pairs via vesin; timed by ASV TimeMinimizationLJCluster (ljcluster).

LJCluster [POT_Jon24]

Lennard-Jones cluster variant, served by rgpot.

Morse_Pt

Hard sphere morse potential for Platinum, served by rgpot. Neighbor pairs via vesin; timed by ASV TimePointMorsePt / saddle / NEB Morse fixtures.

ZBL

Ziegler-Biersack-Littmark screened nuclear repulsion, served by rgpot.

Lenosky_Si [POT_LSA+00]

Lenosky potential, for silicon.

SW_SI [POT_SW85]

Stillinger-Weber potential, for silicon.

Tersoff_SI [POT_Ter88]

Tersoff pair potential with angular terms, for silicon.

EDIP [POT_JBK+98]

Environment-Dependent Interatomic Potential, for carbon.

TIP4P [POT_JCM+83]

Point charge model for water, also for water-hydrogen and water on platinum.

SPCE [POT_BGS87]

Extended simple point charge model for water

Configuration

[Potential]

pydantic model eon.schema.PotentialConfig

Show JSON schema

{
   "title": "PotentialConfig",
   "type": "object",
   "properties": {
      "mpi_poll_period": {
         "default": 0.25,
         "description": "Polling period for MPI potential.",
         "title": "Mpi Poll Period",
         "type": "number"
      },
      "lammps_logging": {
         "default": false,
         "description": "Logging LAMMPS calls.",
         "title": "Lammps Logging",
         "type": "boolean"
      },
      "lammps_threads": {
         "default": 0,
         "description": "LAMMPS threads.",
         "title": "Lammps Threads",
         "type": "integer"
      },
      "ext_pot_path": {
         "default": "ext_pot",
         "description": "Path for the external potential.",
         "title": "Ext Pot Path",
         "type": "string"
      },
      "potential": {
         "default": "lj",
         "description": "Type of potential to execute.",
         "enum": [
            "ams",
            "ams_io",
            "ase_nwcem",
            "ase_nwchem",
            "ase_orca",
            "cuh2",
            "eam_al",
            "edip",
            "emt",
            "ext_pot",
            "fehe",
            "gpr",
            "lammps",
            "lenosky_si",
            "lj",
            "ljcluster",
            "metatomic",
            "morse_pt",
            "mpi",
            "rgpot",
            "socket_nwchem",
            "spce",
            "sw_si",
            "tersoff_si",
            "tip4p",
            "tip4p_h",
            "tip4p_pt",
            "unknown",
            "vasp",
            "xtb",
            "zbl"
         ],
         "title": "Potential",
         "type": "string"
      },
      "log_potential": {
         "anyOf": [
            {
               "type": "boolean"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "If true, write timing information about each force call to client.log.",
         "title": "Log Potential"
      }
   }
}

Config:

• use_attribute_docstrings: bool = True

Fields:

• ext_pot_path (str)

• lammps_logging (bool)

• lammps_threads (int)

• log_potential (bool | None)

• mpi_poll_period (float)

• potential (Literal['ams', 'ams_io', 'ase_nwcem', 'ase_nwchem', 'ase_orca', 'cuh2', 'eam_al', 'edip', 'emt', 'ext_pot', 'fehe', 'gpr', 'lammps', 'lenosky_si', 'lj', 'ljcluster', 'metatomic', 'morse_pt', 'mpi', 'rgpot', 'socket_nwchem', 'spce', 'sw_si', 'tersoff_si', 'tip4p', 'tip4p_h', 'tip4p_pt', 'unknown', 'vasp', 'xtb', 'zbl'])

field ext_pot_path: str = 'ext_pot'

Path for the external potential.

field lammps_logging: bool = False

Logging LAMMPS calls.

field lammps_threads: int = 0

LAMMPS threads.

field log_potential: bool | None = None

If true, write timing information about each force call to client.log.

field mpi_poll_period: float = 0.25

Polling period for MPI potential.

field potential: Literal['ams', 'ams_io', 'ase_nwcem', 'ase_nwchem', 'ase_orca', 'cuh2', 'eam_al', 'edip', 'emt', 'ext_pot', 'fehe', 'gpr', 'lammps', 'lenosky_si', 'lj', 'ljcluster', 'metatomic', 'morse_pt', 'mpi', 'rgpot', 'socket_nwchem', 'spce', 'sw_si', 'tersoff_si', 'tip4p', 'tip4p_h', 'tip4p_pt', 'unknown', 'vasp', 'xtb', 'zbl'] = 'lj'

Options:

• ams: Amsterdam Modeling Suite potential.

• ams_io: Amsterdam Modeling Suite via the I/O.

• ase_nwchem: ASE interface for NWChem (ase_nwcem is an accepted alias).

• ase_orca: ASE interface for ORCA quantum chemistry package.

• cuh2: Potential for copper hydride systems.

• eam_al: Embedded atom method parameterized for aluminum.

• edip: Environment-Dependent Interatomic Potential, for carbon.

• emt: Effective medium theory, for metals.

• ext_pot: External potential with system call interface.

• fehe: Potential for iron-hydrogen systems.

• gpr: Gaussian process regression potential.

• lammps: The LAMMPS potentials.

• lenosky_si: Lenosky potential, for silicon.

• lj: Lennard-Jones potential in reduced units (served by rgpot).

• ljcluster: Lennard-Jones cluster potential (served by rgpot).

• morse_pt: Morse potential for platinum (served by rgpot).

• mpi: Communicate with an MPI process to calculate energy and forces.

• rgpot: In-process rgpot backends (NWChem / CPMD / metatomic / xTB via dlopen).

• spce: Simple Point Charge model for water.

• sw_si: Stillinger-Weber potential, for silicon.

• tersoff_si: Tersoff pair potential with angular terms, for silicon.

• tip4p: Point charge model for water.

• tip4p_h: TIP4P model for water with hydrogen.

• tip4p_pt: TIP4P model for water on platinum.

• unknown: Placeholder for unknown potential type.

• vasp: Vienna Ab-Initio Simulation Program (VASP) interface.

• xtb: Extended Tight Binding model.

• zbl: Ziegler-Biersack-Littmark screened nuclear repulsion (served by rgpot).

Type of potential to execute.

Potential configurations

Several potentials have additional configuration stanzas.

Metatomic

pydantic model eon.schema.Metatomic

Show JSON schema

{
   "title": "Metatomic",
   "type": "object",
   "properties": {
      "model_path": {
         "default": "",
         "description": "Path to the Metatomic model.",
         "title": "Model Path",
         "type": "string"
      },
      "device": {
         "default": "auto",
         "description": "Device to use for Metatomic calculations (e.g., 'cpu', 'cuda', 'auto').",
         "title": "Device",
         "type": "string"
      },
      "length_unit": {
         "default": "Angstrom",
         "description": "Length unit used by the Metatomic model.",
         "title": "Length Unit",
         "type": "string"
      },
      "extensions_directory": {
         "default": "",
         "description": "Directory for Metatomic extensions.",
         "title": "Extensions Directory",
         "type": "string"
      },
      "check_consistency": {
         "default": false,
         "description": "Whether to check consistency of the Metatomic model.",
         "title": "Check Consistency",
         "type": "boolean"
      },
      "uncertainty_threshold": {
         "default": -1,
         "description": "Threshold to report per-atom uncertainties, if positive. Also used to populate the variance.",
         "title": "Uncertainty Threshold",
         "type": "number"
      },
      "variant_base": {
         "default": "",
         "description": "Default variant suffix for energy, uncertainty, and non_conservative_force unless overridden.",
         "title": "Variant Base",
         "type": "string"
      },
      "variant_energy": {
         "default": "",
         "description": "Override variant for energy (use 'off' for the base key).",
         "title": "Variant Energy",
         "type": "string"
      },
      "variant_energy_uncertainty": {
         "default": "",
         "description": "Override variant for energy_uncertainty.",
         "title": "Variant Energy Uncertainty",
         "type": "string"
      },
      "energy_output": {
         "default": "",
         "description": "Literal model energy output key; empty uses variant resolution.",
         "title": "Energy Output",
         "type": "string"
      },
      "energy_uncertainty_output": {
         "default": "",
         "description": "Literal energy uncertainty output key; empty uses variant resolution.",
         "title": "Energy Uncertainty Output",
         "type": "string"
      },
      "force_output": {
         "default": "",
         "description": "Literal non-conservative force output key; empty uses pick_output.",
         "title": "Force Output",
         "type": "string"
      },
      "non_conservative": {
         "default": false,
         "description": "Read forces from non_conservative_force instead of energy gradients.",
         "title": "Non Conservative",
         "type": "boolean"
      },
      "random_rotation": {
         "default": false,
         "description": "Apply a random SO(3) rotation per evaluation and rotate forces back.",
         "title": "Random Rotation",
         "type": "boolean"
      },
      "n_symmetry_rotations": {
         "default": 0,
         "description": "If >0, average over this many random rotations (O(3) symmetrization).",
         "title": "N Symmetry Rotations",
         "type": "integer"
      },
      "variant_force": {
         "default": "",
         "description": "Override variant for non_conservative_force (defaults to energy variant).",
         "title": "Variant Force",
         "type": "string"
      },
      "deterministic": {
         "default": true,
         "description": "Disable JIT profiling and enable PyTorch deterministic algorithms (reproducible NEB / multi-image forces).",
         "title": "Deterministic",
         "type": "boolean"
      },
      "deterministic_strict": {
         "default": false,
         "description": "Fail on nondeterministic CUDA ops; requires CUBLAS_WORKSPACE_CONFIG (e.g. :4096:8).",
         "title": "Deterministic Strict",
         "type": "boolean"
      }
   }
}

Config:

• use_attribute_docstrings: bool = True

Fields:

• check_consistency (bool)

• deterministic (bool)

• deterministic_strict (bool)

• device (str)

• energy_output (str)

• energy_uncertainty_output (str)

• extensions_directory (str)

• force_output (str)

• length_unit (str)

• model_path (str)

• n_symmetry_rotations (int)

• non_conservative (bool)

• random_rotation (bool)

• uncertainty_threshold (float)

• variant_base (str)

• variant_energy (str)

• variant_energy_uncertainty (str)

• variant_force (str)

field check_consistency: bool = False

Whether to check consistency of the Metatomic model.

field deterministic: bool = True

Disable JIT profiling and enable PyTorch deterministic algorithms (reproducible NEB / multi-image forces).

field deterministic_strict: bool = False

Fail on nondeterministic CUDA ops; requires CUBLAS_WORKSPACE_CONFIG (e.g. :4096:8).

field device: str = 'auto'

Device to use for Metatomic calculations (e.g., ‘cpu’, ‘cuda’, ‘auto’).

field energy_output: str = ''

Literal model energy output key; empty uses variant resolution.

field energy_uncertainty_output: str = ''

Literal energy uncertainty output key; empty uses variant resolution.

field extensions_directory: str = ''

Directory for Metatomic extensions.

field force_output: str = ''

Literal non-conservative force output key; empty uses pick_output.

field length_unit: str = 'Angstrom'

Length unit used by the Metatomic model.

field model_path: str = ''

Path to the Metatomic model.

field n_symmetry_rotations: int = 0

If >0, average over this many random rotations (O(3) symmetrization).

field non_conservative: bool = False

Read forces from non_conservative_force instead of energy gradients.

field random_rotation: bool = False

Apply a random SO(3) rotation per evaluation and rotate forces back.

field uncertainty_threshold: float = -1

Threshold to report per-atom uncertainties, if positive. Also used to populate the variance.

field variant_base: str = ''

Default variant suffix for energy, uncertainty, and non_conservative_force unless overridden.

field variant_energy: str = ''

Override variant for energy (use ‘off’ for the base key).

field variant_energy_uncertainty: str = ''

Override variant for energy_uncertainty.

field variant_force: str = ''

Override variant for non_conservative_force (defaults to energy variant).

XTB

pydantic model eon.schema.XTBPot

Show JSON schema

{
   "title": "XTBPot",
   "type": "object",
   "properties": {
      "paramset": {
         "default": "GFNFF",
         "description": "Parameter set for XTB potential.",
         "enum": [
            "GFNFF",
            "GFN0xTB",
            "GFN1xTB",
            "GFN2xTB"
         ],
         "title": "Paramset",
         "type": "string"
      },
      "accuracy": {
         "default": 1.0,
         "description": "Accuracy of the XTB calculation.",
         "title": "Accuracy",
         "type": "number"
      },
      "electronic_temperature": {
         "default": 0.0,
         "description": "Electronic temperature for XTB.",
         "title": "Electronic Temperature",
         "type": "number"
      },
      "max_iterations": {
         "default": 250,
         "description": "Maximum number of XTB iterations.",
         "title": "Max Iterations",
         "type": "integer"
      },
      "uhf": {
         "default": 0,
         "description": "Multiplicity",
         "title": "Uhf",
         "type": "integer"
      },
      "charge": {
         "default": 0.0,
         "description": "Total charge",
         "title": "Charge",
         "type": "number"
      }
   }
}

Config:

• use_attribute_docstrings: bool = True

Fields:

• accuracy (float)

• charge (float)

• electronic_temperature (float)

• max_iterations (int)

• paramset (Literal['GFNFF', 'GFN0xTB', 'GFN1xTB', 'GFN2xTB'])

• uhf (int)

field accuracy: float = 1.0

Accuracy of the XTB calculation.

field charge: float = 0.0

Total charge

field electronic_temperature: float = 0.0

Electronic temperature for XTB.

field max_iterations: int = 250

Maximum number of XTB iterations.

field paramset: Literal['GFNFF', 'GFN0xTB', 'GFN1xTB', 'GFN2xTB'] = 'GFNFF'

Parameter set for XTB potential.

field uhf: int = 0

Multiplicity

ZBL

pydantic model eon.schema.ZBLPot

Show JSON schema

{
   "title": "ZBLPot",
   "type": "object",
   "properties": {
      "cut_inner": {
         "default": 2.0,
         "description": "Distance where switching function begins.",
         "title": "Cut Inner",
         "type": "number"
      },
      "cut_global": {
         "default": 2.5,
         "description": "Global cutoff for the ZBL interaction.",
         "title": "Cut Global",
         "type": "number"
      }
   }
}

Config:

• use_attribute_docstrings: bool = True

Fields:

• cut_global (float)

• cut_inner (float)

field cut_global: float = 2.5

Global cutoff for the ZBL interaction.

field cut_inner: float = 2.0

Distance where switching function begins.

NWChem

Support for nwchem works best with the socket potential structure as noted in the reproduction details of the Optimal transport Gaussian Process [POT_GJonsson25], and can lead to manyfold increases in speed compared to file or ASE interfaces [POT_Gos25].

pydantic model eon.schema.SocketNWChemPot

Show JSON schema

{
   "title": "SocketNWChemPot",
   "type": "object",
   "properties": {
      "host": {
         "default": "127.0.0.1",
         "description": "Host where the NWChem client will connect.",
         "title": "Host",
         "type": "string"
      },
      "port": {
         "default": 9999,
         "description": "Port for the NWChem client to send data to eOn.",
         "title": "Port",
         "type": "integer"
      },
      "nwchem_settings": {
         "default": "nwchem_settings.nwi",
         "description": "Path to the user-provided file containing NWChem scientific settings (basis, theory, task, etc.).",
         "title": "Nwchem Settings",
         "type": "string"
      },
      "unix_socket_path": {
         "default": "eon_nwchem",
         "description": "The basename for the UNIX socket file. The full path will be /tmp/ipi_<basename>.",
         "title": "Unix Socket Path",
         "type": "string"
      },
      "unix_socket_mode": {
         "default": false,
         "description": "If true, use a UNIX domain socket for communication instead of TCP/IP.",
         "title": "Unix Socket Mode",
         "type": "boolean"
      },
      "mem_in_gb": {
         "default": 2,
         "description": "Memory (in GB) to be allocated for the NWChem calculation.",
         "title": "Mem In Gb",
         "type": "integer"
      },
      "make_template_input": {
         "default": true,
         "description": "If false, the socket input for the right geometry is generated by the user.",
         "title": "Make Template Input",
         "type": "boolean"
      }
   }
}

Config:

• use_attribute_docstrings: bool = True

Fields:

• host (str)

• make_template_input (bool)

• mem_in_gb (int)

• nwchem_settings (str)

• port (int)

• unix_socket_mode (bool)

• unix_socket_path (str)

field host: str = '127.0.0.1'

Host where the NWChem client will connect.

field make_template_input: bool = True

If false, the socket input for the right geometry is generated by the user.

field mem_in_gb: int = 2

Memory (in GB) to be allocated for the NWChem calculation.

field nwchem_settings: str = 'nwchem_settings.nwi'

Path to the user-provided file containing NWChem scientific settings (basis, theory, task, etc.).

field port: int = 9999

Port for the NWChem client to send data to eOn.

field unix_socket_mode: bool = False

If true, use a UNIX domain socket for communication instead of TCP/IP.

field unix_socket_path: str = 'eon_nwchem'

The basename for the UNIX socket file. The full path will be /tmp/ipi_<basename>.

Warning

NWChem’s Fortran i-PI socket driver truncates UNIX socket names to approximately 30 characters. The full socket path is /tmp/ipi_<unix_socket_path>, so unix_socket_path should be kept short (under ~20 characters). For example, eon_nwchem works but eon_nwchem_test_socket is truncated, and the connection fails with no clear error message.

An older ASE interface exists as well.

ASE potentials

There are several specific ASE potentials supported,

pydantic model eon.schema.ASE_NWCHEM

Show JSON schema

{
   "title": "ASE_NWCHEM",
   "type": "object",
   "properties": {
      "nwchem_path": {
         "default": "NONE",
         "description": "Path to the NWChem executable.",
         "title": "Nwchem Path",
         "type": "string"
      },
      "nproc": {
         "anyOf": [
            {
               "type": "integer"
            },
            {
               "const": "auto",
               "type": "string"
            }
         ],
         "default": "auto",
         "description": "Number of processors to use for NWChem. Can be 'auto' or an integer string.",
         "title": "Nproc"
      },
      "mpi_launcher": {
         "default": "mpirun",
         "description": "MPI launcher for NWChem (mpirun default, or srun on Slurm).",
         "title": "Mpi Launcher",
         "type": "string"
      },
      "multiplicity": {
         "default": "1",
         "description": "Spin multiplicity for the NWChem calculation.",
         "title": "Multiplicity",
         "type": "string"
      },
      "scf_thresh": {
         "default": 1e-05,
         "description": "SCF convergence threshold for NWChem.",
         "title": "Scf Thresh",
         "type": "number"
      },
      "scf_maxiter": {
         "default": 200,
         "description": "Maximum number of SCF iterations for NWChem.",
         "title": "Scf Maxiter",
         "type": "integer"
      }
   }
}

Config:

• use_attribute_docstrings: bool = True

Fields:

• mpi_launcher (str)

• multiplicity (str)

• nproc (int | Literal['auto'])

• nwchem_path (str)

• scf_maxiter (int)

• scf_thresh (float)

field mpi_launcher: str = 'mpirun'

MPI launcher for NWChem (mpirun default, or srun on Slurm).

field multiplicity: str = '1'

Spin multiplicity for the NWChem calculation.

field nproc: int | Literal['auto'] = 'auto'

Number of processors to use for NWChem. Can be ‘auto’ or an integer string.

field nwchem_path: str = 'NONE'

Path to the NWChem executable.

field scf_maxiter: int = 200

Maximum number of SCF iterations for NWChem.

field scf_thresh: float = 1e-05

SCF convergence threshold for NWChem.

pydantic model eon.schema.ASE_ORCA

Show JSON schema

{
   "title": "ASE_ORCA",
   "type": "object",
   "properties": {
      "orca_path": {
         "default": "NONE",
         "description": "Path to the ORCA executable.",
         "title": "Orca Path",
         "type": "string"
      },
      "nproc": {
         "anyOf": [
            {
               "type": "integer"
            },
            {
               "const": "auto",
               "type": "string"
            }
         ],
         "default": "auto",
         "description": "Number of processors to use for ORCA. Can be 'auto' or an integer string.",
         "title": "Nproc"
      },
      "simpleinput": {
         "default": "ENGRAD HF-3c",
         "description": "Simple input string for ORCA, specifying method and basis set.",
         "title": "Simpleinput",
         "type": "string"
      }
   }
}

Config:

• use_attribute_docstrings: bool = True

Fields:

• nproc (int | Literal['auto'])

• orca_path (str)

• simpleinput (str)

field nproc: int | Literal['auto'] = 'auto'

Number of processors to use for ORCA. Can be ‘auto’ or an integer string.

field orca_path: str = 'NONE'

Path to the ORCA executable.

field simpleinput: str = 'ENGRAD HF-3c'

Simple input string for ORCA, specifying method and basis set.

AMS potentials

Both a direct server model and a file based integration exist.

pydantic model eon.schema.AMSConfig

Show JSON schema

{
   "title": "AMSConfig",
   "type": "object",
   "properties": {
      "engine": {
         "default": "",
         "description": "Engine for AMS calculation. One of REAXFF, MOPAC.",
         "enum": [
            "",
            "REAXFF",
            "MOPAC"
         ],
         "title": "Engine",
         "type": "string"
      },
      "forcefield": {
         "default": "",
         "description": "Force field to use (e.g., 'OPt.ff').",
         "title": "Forcefield",
         "type": "string"
      },
      "model": {
         "default": "",
         "description": "Model to use (e.g., 'PM7', 'PM3').",
         "title": "Model",
         "type": "string"
      },
      "xc": {
         "default": "",
         "description": "Exchange-correlation functional.",
         "title": "Xc",
         "type": "string"
      },
      "basis": {
         "default": "",
         "description": "Basis set to use with the exchange-correlation functional.",
         "title": "Basis",
         "type": "string"
      },
      "resources": {
         "default": "",
         "description": "Resources path for DFTB.",
         "title": "Resources",
         "type": "string"
      }
   }
}

Config:

• use_attribute_docstrings: bool = True

Fields:

• basis (str)

• engine (Literal['', 'REAXFF', 'MOPAC'])

• forcefield (str)

• model (str)

• resources (str)

• xc (str)

field basis: str = ''

Basis set to use with the exchange-correlation functional.

field engine: Literal['', 'REAXFF', 'MOPAC'] = ''

Engine for AMS calculation. One of REAXFF, MOPAC.

field forcefield: str = ''

Force field to use (e.g., ‘OPt.ff’).

field model: str = ''

Model to use (e.g., ‘PM7’, ‘PM3’).

field resources: str = ''

Resources path for DFTB.

field xc: str = ''

Exchange-correlation functional.

pydantic model eon.schema.AMSIOConfig

Show JSON schema

{
   "title": "AMSIOConfig",
   "type": "object",
   "properties": {
      "engine": {
         "default": "",
         "description": "Engine for AMS calculation. One of REAXFF, MOPAC.",
         "enum": [
            "",
            "REAXFF",
            "MOPAC"
         ],
         "title": "Engine",
         "type": "string"
      },
      "forcefield": {
         "default": "",
         "description": "Force field to use (e.g., 'OPt.ff').",
         "title": "Forcefield",
         "type": "string"
      },
      "model": {
         "default": "",
         "description": "Model to use (e.g., 'PM7', 'PM3').",
         "title": "Model",
         "type": "string"
      },
      "xc": {
         "default": "",
         "description": "Exchange-correlation functional.",
         "title": "Xc",
         "type": "string"
      }
   }
}

Config:

• use_attribute_docstrings: bool = True

Fields:

• engine (Literal['', 'REAXFF', 'MOPAC'])

• forcefield (str)

• model (str)

• xc (str)

field engine: Literal['', 'REAXFF', 'MOPAC'] = ''

Engine for AMS calculation. One of REAXFF, MOPAC.

field forcefield: str = ''

Force field to use (e.g., ‘OPt.ff’).

field model: str = ''

Model to use (e.g., ‘PM7’, ‘PM3’).

field xc: str = ''

Exchange-correlation functional.

Along with helpers to set environment variables for these calculations.

pydantic model eon.schema.AMSEnvConfig

Show JSON schema

{
   "title": "AMSEnvConfig",
   "type": "object",
   "properties": {
      "amshome": {
         "default": "",
         "description": "Path to AMS home directory (e.g., '/some/path/to/amshome/').",
         "title": "Amshome",
         "type": "string"
      },
      "scm_tmpdir": {
         "default": "",
         "description": "Temporary directory for SCM (e.g., '/tmp').",
         "title": "Scm Tmpdir",
         "type": "string"
      },
      "scm_pythondir": {
         "default": "",
         "description": "Python directory for SCM (e.g., '/.scm/python').",
         "title": "Scm Pythondir",
         "type": "string"
      },
      "amsbin": {
         "default": "",
         "description": "Path to AMS binary directory (will be appended to amshome).",
         "title": "Amsbin",
         "type": "string"
      },
      "scmlicense": {
         "default": "",
         "description": "Path to SCM license file (will be appended to amshome).",
         "title": "Scmlicense",
         "type": "string"
      },
      "amsresources": {
         "default": "",
         "description": "Path to AMS atomic data resources (will be appended to amshome).",
         "title": "Amsresources",
         "type": "string"
      }
   }
}

Config:

• use_attribute_docstrings: bool = True

Fields:

• amsbin (str)

• amshome (str)

• amsresources (str)

• scm_pythondir (str)

• scm_tmpdir (str)

• scmlicense (str)

field amsbin: str = ''

Path to AMS binary directory (will be appended to amshome).

field amshome: str = ''

Path to AMS home directory (e.g., ‘/some/path/to/amshome/’).

field amsresources: str = ''

Path to AMS atomic data resources (will be appended to amshome).

field scm_pythondir: str = ''

Python directory for SCM (e.g., ‘/.scm/python’).

field scm_tmpdir: str = ''

Temporary directory for SCM (e.g., ‘/tmp’).

field scmlicense: str = ''

Path to SCM license file (will be appended to amshome).

References

[POT_ApraBDJ+20] (1,2)

E. Aprà, E. J. Bylaska, W. A. De Jong, N. Govind, K. Kowalski, T. P. Straatsma, M. Valiev, H. J. J. Van Dam, Y. Alexeev, J. Anchell, V. Anisimov, F. W. Aquino, R. Atta-Fynn, J. Autschbach, N. P. Bauman, J. C. Becca, D. E. Bernholdt, K. Bhaskaran-Nair, S. Bogatko, P. Borowski, J. Boschen, J. Brabec, A. Bruner, E. Cauët, Y. Chen, G. N. Chuev, C. J. Cramer, J. Daily, M. J. O. Deegan, T. H. Dunning, M. Dupuis, K. G. Dyall, G. I. Fann, S. A. Fischer, A. Fonari, H. Früchtl, L. Gagliardi, J. Garza, N. Gawande, S. Ghosh, K. Glaesemann, A. W. Götz, J. Hammond, V. Helms, E. D. Hermes, K. Hirao, S. Hirata, M. Jacquelin, L. Jensen, B. G. Johnson, H. Jónsson, R. A. Kendall, M. Klemm, R. Kobayashi, V. Konkov, S. Krishnamoorthy, M. Krishnan, Z. Lin, R. D. Lins, R. J. Littlefield, A. J. Logsdail, K. Lopata, W. Ma, A. V. Marenich, J. Martin Del Campo, D. Mejia-Rodriguez, J. E. Moore, J. M. Mullin, T. Nakajima, D. R. Nascimento, J. A. Nichols, P. J. Nichols, J. Nieplocha, A. Otero-de-la-Roza, B. Palmer, A. Panyala, T. Pirojsirikul, B. Peng, R. Peverati, J. Pittner, L. Pollack, R. M. Richard, P. Sadayappan, G. C. Schatz, W. A. Shelton, D. W. Silverstein, D. M. A. Smith, T. A. Soares, D. Song, M. Swart, H. L. Taylor, G. S. Thomas, V. Tipparaju, D. G. Truhlar, K. Tsemekhman, T. Van Voorhis, Á. Vázquez-Mayagoitia, P. Verma, O. Villa, A. Vishnu, K. D. Vogiatzis, D. Wang, J. H. Weare, M. J. Williamson, T. L. Windus, K. Woliński, A. T. Wong, Q. Wu, C. Yang, Q. Yu, M. Zacharias, Z. Zhang, Y. Zhao, and R. J. Harrison. NWChem: past, present, and future. Journal of Chemical Physics, 152(18):184102, May 2020. doi:10.1063/5.0004997.

[POT_BCE+21]

Christoph Bannwarth, Eike Caldeweyher, Sebastian Ehlert, Andreas Hansen, Philipp Pracht, Jakob Seibert, Sebastian Spicher, and Stefan Grimme. Extended tight-binding quantum chemistry methods. WIREs Computational Molecular Science, 11(2):e1493, 2021. doi:10.1002/wcms.1493.

[POT_BGS87]

H. J. C. Berendsen, J. R. Grigera, and T. P. Straatsma. The missing term in effective pair potentials. The Journal of Physical Chemistry, 91(24):6269–6271, November 1987. doi:10.1021/j100308a038.

[POT_Gos25]

Rohit Goswami. Efficient exploration of chemical kinetics. October 2025. arXiv:2510.21368, doi:10.48550/arXiv.2510.21368.

[POT_GJonsson25]

Rohit Goswami and Hannes Jónsson. Adaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point Searches. ChemPhysChem, November 2025. doi:10.1002/cphc.202500730.

[POT_Jon24] (1,2)

Lennard Jones. On the determination of molecular fields. —II. From the equation of state of a gas. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 106(738):463–477, October 1924. doi:10.1098/rspa.1924.0082.

[POT_JCM+83]

William L. Jorgensen, Jayaraman Chandrasekhar, Jeffry D. Madura, Roger W. Impey, and Michael L. Klein. Comparison of simple potential functions for simulating liquid water. The Journal of Chemical Physics, 79(2):926–935, July 1983. doi:10.1063/1.445869.

[POT_JBK+98]

João F. Justo, Martin Z. Bazant, Efthimios Kaxiras, V. V. Bulatov, and Sidney Yip. Interatomic potential for silicon defects and disordered phases. Physical Review B, 58(5):2539–2550, August 1998. doi:10.1103/PhysRevB.58.2539.

[POT_KFurthmuller96]

G. Kresse and J. Furthmüller. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Physical Review B, 54(16):11169–11186, October 1996. doi:10.1103/PhysRevB.54.11169.

[POT_LMB+17] (1,2)

Ask Hjorth Larsen, Jens Jørgen Mortensen, Jakob Blomqvist, Ivano E. Castelli, Rune Christensen, Marcin Du\lak, Jesper Friis, Michael N. Groves, Bjørk Hammer, Cory Hargus, Eric D. Hermes, Paul C. Jennings, Peter Bjerre Jensen, James Kermode, John R. Kitchin, Esben Leonhard Kolsbjerg, Joseph Kubal, Kristen Kaasbjerg, Steen Lysgaard, Jón Bergmann Maronsson, Tristan Maxson, Thomas Olsen, Lars Pastewka, Andrew Peterson, Carsten Rostgaard, Jakob Schiøtz, Ole Schütt, Mikkel Strange, Kristian S. Thygesen, Tejs Vegge, Lasse Vilhelmsen, Michael Walter, Zhenhua Zeng, and Karsten W. Jacobsen. The atomic simulation environment—a Python library for working with atoms. Journal of Physics: Condensed Matter, 29(27):273002, June 2017. doi:10.1088/1361-648X/aa680e.

[POT_LSA+00]

Thomas J Lenosky, Babak Sadigh, Eduardo Alonso, Vasily V Bulatov, Tomas Diaz De La Rubia, Jeongnim Kim, Arthur F Voter, and Joel D Kress. Highly optimized empirical potential model of silicon. Modelling and Simulation in Materials Science and Engineering, 8(6):825–841, November 2000. doi:10.1088/0965-0393/8/6/305.

[POT_NWBR20]

Frank Neese, Frank Wennmohs, Ute Becker, and Christoph Riplinger. The ORCA quantum chemistry program package. The Journal of Chemical Physics, 152(22):224108, June 2020. doi:10.1063/5.0004608.

[POT_Pli95]

Steve Plimpton. Fast Parallel Algorithms for Short-Range Molecular Dynamics. Journal of Computational Physics, 117(1):1–19, March 1995. doi:10.1006/jcph.1995.1039.

[POT_SW85]

Frank H. Stillinger and Thomas A. Weber. Computer simulation of local order in condensed phases of silicon. Physical Review B, 31(8):5262–5271, April 1985. doi:10.1103/PhysRevB.31.5262.

[POT_Ter88]

J. Tersoff. Empirical interatomic potential for silicon with improved elastic properties. Physical Review B, 38(14):9902–9905, November 1988. doi:10.1103/PhysRevB.38.9902.

[POT_TAB+22]

Aidan P. Thompson, H. Metin Aktulga, Richard Berger, Dan S. Bolintineanu, W. Michael Brown, Paul S. Crozier, Pieter J. In 'T Veld, Axel Kohlmeyer, Stan G. Moore, Trung Dac Nguyen, Ray Shan, Mark J. Stevens, Julien Tranchida, Christian Trott, and Steven J. Plimpton. LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Computer Physics Communications, 271:108171, February 2022. doi:10.1016/j.cpc.2021.108171.

[POT_teVeldeBB+01]

G. te Velde, F. M. Bickelhaupt, E. J. Baerends, C. Fonseca Guerra, S. J. A. van Gisbergen, J. G. Snijders, and T. Ziegler. Chemistry with ADF. Journal of Computational Chemistry, 22(9):931–967, 2001. doi:10.1002/jcc.1056.