Hi everyone,
I am currently using the on-the-fly Machine Learning Force Field (MLFF) engine (ML_MODE = TRAIN) to generate a training dataset for a metallic system. My goal is to sample the phase space across several different temperatures using NVT and NPT AIMD runs - which require proper choice of ISMEAR and SIGMA values.
In previous literature for this specific reference metal, static structural relaxations were performed using Methfessel-Paxton smearing (ISMEAR = 1 or 2) with SIGMA = 0.010 eV.
However, I am facing a dilemma regarding the best practice for smearing during MLFF dataset generation:
Methfessel-Paxton is generally unsuitable for Molecular Dynamics due to the risk of negative occupancies causing unstable forces and SCF convergence failure.
To ensure the Machine Learning Interatomic Potential maps to a single, mathematically consistent Potential Energy Surface (PES), the electronic smearing (SIGMA) must remain strictly fixed across all training data, regardless of the differing ionic temperatures (e.g., 300 K vs. 1000 K) of the MD runs.
To maintain a consistent PES for the MLFF while ensuring stable MD forces, I am considering using Gaussian smearing (ISMEAR = 0) with a fixed width of SIGMA = 0.020 eV for all calculations—including the initial static geometry relaxations and all subsequent AIMD/MLFF training runs at various temperatures.
Is using a strictly fixed Gaussian smearing (ISMEAR = 0, SIGMA = 0.020 eV) a valid and physically acceptable compromise for training a metallic MLFF across different thermodynamic temperatures?
Any guidance on the standard best practices for this workflow would be greatly appreciated.
Thanks
Dominic