mlip-guide

name	mlip-guide
description	Machine Learning Interatomic Potentials (MLIPs) (4 sub-skills: mace-advanced, mlip-validation, torchsim-batch, universal-mlip) Use when this capability is needed.

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)

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Source: bjzgcai/MatClaw — distributed by TomeVault.