Applying foundation model on metal oxide surface gets irrational results

[certejlhj2223]

Dear all:

I am using MACE foundation model to run a MD on CeO2 (100) surface.

The trajectory shows formation of oxygen gas (O2), which should not happen in reality.

The foundation models I tried are MACE-MH-1(head: oc20 and matpes_r2scan) and MACE-MP-0b3-medium

I wonder if it is the problem of the foundation models that not suitable for my system and whether fine-tuning are possible to fix this.

I used ASE to run MD simulation with script as follows:

---

warnings.filterwarnings("ignore")

from ase import units
from ase.io import read, write
from ase.md import Langevin
from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
from ase.io.trajectory import Trajectory

import numpy as np
import time

from mace.calculators import MACECalculator

calculator = MACECalculator(model_paths='/HOME/cityu_hlxue/cityu_hlxue_3/HDD_POOL/models/mace-mh-1.model',device='cuda',head='matpes_r2scan')
atoms = read(f"quarter.in", format='espresso-in')
atoms.calc = calculator

traj = Trajectory('quarter-default.traj', 'w', atoms)

T_init = 300 # Initial temperature in K
MaxwellBoltzmannDistribution(atoms, T_init * units.kB)

dyn = Langevin(atoms, 1 * units.fs, T_init * units.kB, 0.001)
n_steps = 20000 # Number of steps to run

dyn.attach(traj.write, interval=20)

def printenergy(a=atoms):
"""Function to print the potential, kinetic and total energy"""
epot = a.get_potential_energy() / len(a)
ekin = a.get_kinetic_energy() / len(a)
print(
f'Energy per atom: Epot ={epot:6.3f}eV Ekin = {ekin:.3f}eV '
f'(T={ekin / (1.5 * units.kB):3.0f}K) Etot = {epot + ekin:.3f}eV'
)

dyn.attach(printenergy, interval=500)
printenergy()

dyn.run(n_steps)

---

[gabor1]

Well, I wouldn't call it irrational, in reality it depends on the temperature, oxygen partial pressure etc. But I'm sure that fine-tuning with a dozen configurations (take them from various stages of losing the oxygen) would help nail the DFT prediction right (and you have to be content with that, ML potentials can only speed up what you would see with DFT).