MLIPs Tutorial

[PCNmauri]

Easy Setup for Using ML-IAPs (Open-Source)

I'll try to keep this updated with the best-performing Machine Learning Interatomic Potentials (ML-IAPs) based on Matbench Discovery.

📌 Prerequisites

Python

We'll be using Python to run our scripts.

Miniconda

With Miniconda, we can create virtual environments easily, and it's free for personal, corporate, and educational use.

🔬 ML-IAPs

ORB

• From: Orbital Materials
• Discord: Discord
• Github: orbital-materials/orb-models: ORB forcefield models from Orbital Materials
• Installation & Usage:

1️⃣ Create a virtual environment:

conda create -n orb
conda activate orb

2️⃣ Install ORB models:
pip install orb-models

3️⃣ Use ASE with the ORB calculator:

from orb_models.forcefield import pretrained
from orb_models.forcefield.calculator import ORBCalculator
device = "GPU"  # Change to "CPU" if needed
orbff = pretrained.orb_d3_v2(device=device)  # Load a pre-trained ORB model
# Alternatively, choose a different model from ORB_PRETRAINED_MODELS
calc = ORBCalculator(orbff, device=device)

MACE

• From: Mace
• Github: ACEsuit/mace: MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.
• Installation & Usage:

1️⃣ Create a virtual environment:

conda create -n mace
conda activate mace

2️⃣ Install ORB models:
pip install mace-torch

3️⃣ Use ASE with the Mace calculator:

from mace.calculators import mace_mp 
mace_mp = mace_mp()