mlip-guide

name	mlip-guide
description	Machine Learning Interatomic Potentials (MLIPs) (4 sub-skills: mace-advanced, mlip-validation, torchsim-batch, universal-mlip)

Machine Learning Interatomic Potentials (MLIPs)

Universal and specialized machine learning interatomic potentials for rapid atomistic simulation without DFT. Covers model selection, usage patterns, validation strategies, and known limitations.

Sub-Skills

Sub-Skill	Directory	Description
Universal MLIPs	universal-mlip/	MACE-MP-0, CHGNet, M3GNet/MatGL, SevenNet-0: setup, usage, benchmarking, and validation against DFT
TorchSim Batch GPU	torchsim-batch/	GPU-accelerated batch MD and optimization with TorchSim: 10-100x speedup, auto-batching, parallel relaxation and screening

Method Decision Guide

What do you need MLIPs for?

Quick geometry relaxation / screening many structures?
  --> universal-mlip/  (MACE-MP-0 medium is fastest, CHGNet is a good alternative)
  --> torchsim-batch/  (GPU available? 10-100x faster batch relaxation with TorchSim)

Molecular dynamics (phonons, thermal, diffusion)?
  --> universal-mlip/  (MACE-MP-0 large for best accuracy)
  --> torchsim-batch/  (GPU available? TorchSim for 10-100x faster MD on GPU)

Elastic constants / equation of state?
  --> universal-mlip/  (any universal MLIP; validate against DFT for novel systems)

Band gaps / electronic properties / magnetic ordering?
  --> MLIPs CANNOT predict these. Use Quantum ESPRESSO DFT instead.

System contains rare elements / extreme conditions?
  --> Validate MLIP against DFT first; MLIPs may extrapolate poorly.

Pre-installed vs. Installable

MLIP	Status	Install Command
MACE-MP-0	Pre-installed	--
CHGNet	pip install	pip install chgnet
M3GNet (MatGL)	pip install	pip install matgl
SevenNet	pip install	pip install sevenn
ORB Models	pip install	pip install orb-models
TorchSim	pip install	pip install torch-sim (requires PyTorch + CUDA for GPU)