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---
title: MorsePotential
description: API reference for qiskit.chemistry.algorithms.pes_samplers.MorsePotential
in_page_toc_min_heading_level: 1
python_api_type: class
python_api_name: qiskit.chemistry.algorithms.pes_samplers.MorsePotential
---
# qiskit.chemistry.algorithms.pes\_samplers.MorsePotential
<Class id="qiskit.chemistry.algorithms.pes_samplers.MorsePotential" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-aqua/tree/stable/0.9/qiskit/chemistry/algorithms/pes_samplers/potentials/morse_potential.py" signature="MorsePotential(molecule)" modifiers="class">
Implements a 1D Morse potential.
Input units are Angstroms (distance between the two atoms), and output units are Hartrees (molecular energy).
Initializes the potential to the zero-function. [`fit()`](#qiskit.chemistry.algorithms.pes_samplers.MorsePotential.fit "qiskit.chemistry.algorithms.pes_samplers.MorsePotential.fit") should be used afterwards to fit the potential to computed molecular energies.
**Parameters**
**molecule** (`Molecule`) – the underlying molecule.
**Raises**
**ValueError** – Only implemented for diatomic molecules
### \_\_init\_\_
<Function id="qiskit.chemistry.algorithms.pes_samplers.MorsePotential.__init__" signature="__init__(molecule)">
Initializes the potential to the zero-function. [`fit()`](#qiskit.chemistry.algorithms.pes_samplers.MorsePotential.fit "qiskit.chemistry.algorithms.pes_samplers.MorsePotential.fit") should be used afterwards to fit the potential to computed molecular energies.
**Parameters**
**molecule** (`Molecule`) – the underlying molecule.
**Raises**
**ValueError** – Only implemented for diatomic molecules
</Function>
## Methods
| | |
| -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------- |
| [`__init__`](#qiskit.chemistry.algorithms.pes_samplers.MorsePotential.__init__ "qiskit.chemistry.algorithms.pes_samplers.MorsePotential.__init__")(molecule) | Initializes the potential to the zero-function. |
| [`dissociation_energy`](#qiskit.chemistry.algorithms.pes_samplers.MorsePotential.dissociation_energy "qiskit.chemistry.algorithms.pes_samplers.MorsePotential.dissociation_energy")(\[scaling]) | Returns the calculated dissociation energy for the current fit. |
| [`eval`](#qiskit.chemistry.algorithms.pes_samplers.MorsePotential.eval "qiskit.chemistry.algorithms.pes_samplers.MorsePotential.eval")(x) | After fitting the data to the fit function, predict the energy at a point x. |
| [`fit`](#qiskit.chemistry.algorithms.pes_samplers.MorsePotential.fit "qiskit.chemistry.algorithms.pes_samplers.MorsePotential.fit")(xdata, ydata\[, initial\_vals, bounds\_list]) | Fits a potential to computed molecular energies. |
| [`fit_function`](#qiskit.chemistry.algorithms.pes_samplers.MorsePotential.fit_function "qiskit.chemistry.algorithms.pes_samplers.MorsePotential.fit_function")(x, d\_e, alpha, r\_0, m\_shift) | Functional form of the potential. |
| [`fundamental_frequency`](#qiskit.chemistry.algorithms.pes_samplers.MorsePotential.fundamental_frequency "qiskit.chemistry.algorithms.pes_samplers.MorsePotential.fundamental_frequency")() | Returns the fundamental frequency for the current fit (in s^-1). |
| [`get_equilibrium_geometry`](#qiskit.chemistry.algorithms.pes_samplers.MorsePotential.get_equilibrium_geometry "qiskit.chemistry.algorithms.pes_samplers.MorsePotential.get_equilibrium_geometry")(\[scaling]) | Returns the interatomic distance corresponding to minimal energy. |
| [`get_maximum_trusted_level`](#qiskit.chemistry.algorithms.pes_samplers.MorsePotential.get_maximum_trusted_level "qiskit.chemistry.algorithms.pes_samplers.MorsePotential.get_maximum_trusted_level")(\[n]) | Returns the maximum energy level for which the particular implementation still provides a good approximation of reality. |
| [`get_minimal_energy`](#qiskit.chemistry.algorithms.pes_samplers.MorsePotential.get_minimal_energy "qiskit.chemistry.algorithms.pes_samplers.MorsePotential.get_minimal_energy")(\[scaling]) | Returns the smallest molecular energy for the current fit. |
| [`get_num_modes`](#qiskit.chemistry.algorithms.pes_samplers.MorsePotential.get_num_modes "qiskit.chemistry.algorithms.pes_samplers.MorsePotential.get_num_modes")() | This (1D) potential represents a single vibrational mode |
| [`get_trust_region`](#qiskit.chemistry.algorithms.pes_samplers.MorsePotential.get_trust_region "qiskit.chemistry.algorithms.pes_samplers.MorsePotential.get_trust_region")() | The potential will usually be well-defined (even if not useful) for arbitrary x so we return a fairly large interval here. |
| [`update_molecule`](#qiskit.chemistry.algorithms.pes_samplers.MorsePotential.update_molecule "qiskit.chemistry.algorithms.pes_samplers.MorsePotential.update_molecule")(molecule) | Updates the underlying molecule. |
| [`vibrational_energy_level`](#qiskit.chemistry.algorithms.pes_samplers.MorsePotential.vibrational_energy_level "qiskit.chemistry.algorithms.pes_samplers.MorsePotential.vibrational_energy_level")(n) | Returns the n-th vibrational energy level for the current fit (in Hartrees). |
| [`wave_number`](#qiskit.chemistry.algorithms.pes_samplers.MorsePotential.wave_number "qiskit.chemistry.algorithms.pes_samplers.MorsePotential.wave_number")() | Returns the wave number for the current fit (in cm^-1). |
### dissociation\_energy
<Function id="qiskit.chemistry.algorithms.pes_samplers.MorsePotential.dissociation_energy" signature="dissociation_energy(scaling=1.0)">
Returns the calculated dissociation energy for the current fit.
**Parameters**
**scaling** (`float`) – Scaling to change units. (Default is 1.0 for Hartrees)
**Return type**
`float`
**Returns**
calculated dissociation energy for the current fit
</Function>
### eval
<Function id="qiskit.chemistry.algorithms.pes_samplers.MorsePotential.eval" signature="eval(x)">
After fitting the data to the fit function, predict the energy at a point x.
**Parameters**
**x** (`float`) – value to evaluate surface in
**Return type**
`float`
**Returns**
value of surface in point x
</Function>
### fit
<Function id="qiskit.chemistry.algorithms.pes_samplers.MorsePotential.fit" signature="fit(xdata, ydata, initial_vals=None, bounds_list=None)">
Fits a potential to computed molecular energies.
**Parameters**
* **xdata** (`List`\[`float`]) – interatomic distance points (Angstroms)
* **ydata** (`List`\[`float`]) – molecular energies (Hartrees)
* **initial\_vals** (`Optional`\[`List`\[`float`]]) – Initial values for fit parameters. None for default. Order of parameters is d\_e, alpha, r\_0 and m\_shift (see fit\_function implementation)
* **bounds\_list** (`Optional`\[`Tuple`\[`List`\[`float`], `List`\[`float`]]]) – Bounds for the fit parameters. None for default. Order of parameters is d\_e, alpha, r\_0 and m\_shift (see fit\_function implementation)
**Return type**
`None`
</Function>
### fit\_function
<Function id="qiskit.chemistry.algorithms.pes_samplers.MorsePotential.fit_function" signature="fit_function(x, d_e, alpha, r_0, m_shift)" modifiers="static">
Functional form of the potential.
**Parameters**
* **x** (`float`) – x parameter of morse potential
* **d\_e** (`float`) – d\_e parameter of morse potential
* **alpha** (`float`) – alpha parameter of morse potential
* **r\_0** (`float`) – r\_0 parameter of morse potential
* **m\_shift** (`float`) – m\_shift parameter of morse potential
**Return type**
`float`
**Returns**
potential functional form
</Function>
### fundamental\_frequency
<Function id="qiskit.chemistry.algorithms.pes_samplers.MorsePotential.fundamental_frequency" signature="fundamental_frequency()">
Returns the fundamental frequency for the current fit (in s^-1).
**Return type**
`float`
**Returns**
fundamental frequency for the current fit
</Function>
### get\_equilibrium\_geometry
<Function id="qiskit.chemistry.algorithms.pes_samplers.MorsePotential.get_equilibrium_geometry" signature="get_equilibrium_geometry(scaling=1.0)">
Returns the interatomic distance corresponding to minimal energy.
**Parameters**
**scaling** (`float`) – Scaling to change units. (Default is 1.0 for Angstroms)
**Return type**
`float`
**Returns**
interatomic distance corresponding to minimal energy
</Function>
### get\_maximum\_trusted\_level
<Function id="qiskit.chemistry.algorithms.pes_samplers.MorsePotential.get_maximum_trusted_level" signature="get_maximum_trusted_level(n=0)">
Returns the maximum energy level for which the particular implementation still provides a good approximation of reality.
**Parameters**
**n** (`int`) – vibronic mode
**Return type**
`float`
**Returns**
maximum\_trusted\_level estimated
</Function>
### get\_minimal\_energy
<Function id="qiskit.chemistry.algorithms.pes_samplers.MorsePotential.get_minimal_energy" signature="get_minimal_energy(scaling=1.0)">
Returns the smallest molecular energy for the current fit.
**Parameters**
**scaling** (`float`) – Scaling to change units. (Default is 1.0 for Hartrees)
**Return type**
`float`
**Returns**
smallest molecular energy for the current fit
</Function>
### get\_num\_modes
<Function id="qiskit.chemistry.algorithms.pes_samplers.MorsePotential.get_num_modes" signature="get_num_modes()">
This (1D) potential represents a single vibrational mode
**Return type**
`int`
</Function>
### get\_trust\_region
<Function id="qiskit.chemistry.algorithms.pes_samplers.MorsePotential.get_trust_region" signature="get_trust_region()">
The potential will usually be well-defined (even if not useful) for arbitrary x so we return a fairly large interval here. Redefine in derived classes if needed.
**Return type**
`Tuple`\[`float`, `float`]
</Function>
### update\_molecule
<Function id="qiskit.chemistry.algorithms.pes_samplers.MorsePotential.update_molecule" signature="update_molecule(molecule)">
Updates the underlying molecule.
**Parameters**
**molecule** (`Molecule`) – chemistry molecule
**Raises**
**ValueError** – Only implemented for diatomic molecules
**Return type**
`None`
</Function>
### vibrational\_energy\_level
<Function id="qiskit.chemistry.algorithms.pes_samplers.MorsePotential.vibrational_energy_level" signature="vibrational_energy_level(n)">
Returns the n-th vibrational energy level for the current fit (in Hartrees).
**Parameters**
**n** (`int`) – vibrational mode
**Return type**
`float`
**Returns**
vibrational energy level for the current fit
</Function>
### wave\_number
<Function id="qiskit.chemistry.algorithms.pes_samplers.MorsePotential.wave_number" signature="wave_number()">
Returns the wave number for the current fit (in cm^-1).
**Return type**
`float`
**Returns**
wave number for the current fit
</Function>
</Class>
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