Molecular Dynamics Simulation¶

The MD workflow provides a general-purpose interface for running NVT/NPT molecular dynamics simulations. It is used for equilibration, production runs, and as a building block for property calculations.

Usage¶

`md_simulation(structure, potential, ...)`¶

from amorphouspy import md_simulation

result = md_simulation(
    structure=glass_structure,
    potential=potential,
    temperature_sim=300.0,    # K (start temperature)
    production_steps=10_000_000,
    timestep=1.0,             # fs
    n_print=1000,             # Write output every N steps
)

final_structure = result["structure"]
thermo = result["result"]

Parameters:

Parameter	Type	Default	Description
`structure`	`Atoms`	—	Input atomic structure
`potential`	`DataFrame`	—	Potential configuration
`temperature_sim`	`float`	`5000.0`	Start temperature in K (constant if `temperature_end` is None)
`production_steps`	`int`	`10_000_000`	Number of MD steps
`timestep`	`float`	`1.0`	MD timestep in femtoseconds
`n_print`	`int`	`1000`	Output frequency in steps
`temperature_end`	`float \\| None`	`None`	End temperature for a linear ramp; None = constant temperature
`pressure`	`float \\| None`	`None`	Start pressure in GPa; None = NVT, float value = NPT
`pressure_end`	`float \\| None`	`None`	End pressure in GPa for a linear ramp; requires `pressure` to be set
`langevin`	`bool`	`False`	Use Langevin dynamics instead of Nosé-Hoover
`seed`	`int`	`12345`	Random seed for velocity initialization
`server_kwargs`	`dict \\| None`	`None`	Additional arguments passed to the LAMMPS server
`tmp_working_directory`	`str \\| Path \\| None`	`None`	Directory for temporary simulation files

Returns: dict with keys:

Key	Type	Description
`"structure"`	`Atoms`	Final structure after MD
`"result"`	`dict`	Thermodynamic data (step, temp, pressure, energy, volume)

Ensembles¶

Ensemble selection is controlled by the pressure parameter:

NVT (constant volume): leave pressure=None (the default)
NPT (constant pressure): set pressure to a float value in GPa, e.g. pressure=0.0

NVT example¶

result = md_simulation(
    structure=glass_structure,
    potential=potential,
    temperature_sim=300.0,
    production_steps=50_000,
)

Use NVT for equilibration at a fixed density, structural analysis, or property calculations that require constant volume.

NPT example¶

result = md_simulation(
    structure=glass_structure,
    potential=potential,
    temperature_sim=300.0,
    production_steps=100_000,
    pressure=0.0,    # GPa — ambient pressure
)

Use NPT for density relaxation, pressure equilibration after quenching, or CTE calculations.

Temperature and pressure ramps¶

Linear ramps are supported for both temperature and pressure:

# Cool from 3000 K → 300 K while ramping pressure from 5 GPa → 0 GPa
result = md_simulation(
    structure=glass_structure,
    potential=potential,
    temperature_sim=3000.0,
    temperature_end=300.0,
    production_steps=500_000,
    pressure=5.0,
    pressure_end=0.0,
)

pressure_end requires pressure to be set; omitting it holds pressure constant.

Timestep Selection¶

System type	Recommended dt	Notes
Oxide glass (standard)	1.0 fs	Safe for most potentials
SHIK potential (initial)	0.25 fs	Steep \(r^{-24}\) requires smaller dt
High-temperature melt	0.5–1.0 fs	Fast dynamics benefit from smaller dt
Production at 300 K	1.0–2.0 fs	Slower dynamics allow larger dt

Warning: Using a timestep that is too large will cause energy drift and eventually simulation instability. If you see the total energy drifting upward, reduce the timestep.

Output Analysis¶

Thermodynamic data¶

import plotly.graph_objects as go

thermo = result["result"]

fig = go.Figure()
fig.add_trace(go.Scatter(x=thermo["step"], y=thermo["temp"], name="Temperature"))
fig.update_layout(xaxis_title="Step", yaxis_title="T (K)")
fig.show()

Saving the final structure¶

from ase.io import write

write("final_structure.xyz", result["structure"])