ML Performance and Extrapolation Guide (reference deployment https://ml-peg.stfc.ac.uk)
Tools for machine learnt interatomic potentials
machine learning interatomic potentials aiida plugin
A free and open platform for interactive benchmarking and simulations using machine learning interatomic potentials
Python package designed to run atomistic Monte Carlo simulations.
ASE with Rust hot paths — 12× faster neighbor lists, 6× faster VASP IO, 3.5× faster extxyz. Drop-in: pip install ase-fast
Production MLIP molecular dynamics on consumer GPUs — a measured MACE + cuEquivariance deployment study on phosphorene (break-even maps, precision error budget, Nsight, RTX 3080 Ti).
Phonon band unfolding from supercells to primitive cells, for relaxed, defective, and moiré structures
A benchmark and fine-tuning study of MACE foundation models on CsPbI₃ phase stability, using zero-shot inference, phonon analysis, LoRA fine-tuning, and finite-temperature MD.
Personal reading notes on PhaseForge and MaterialsFramework, connecting their research direction with my current atomistic background and PhD learning goals.
Silicon phonons with a MACE foundation model, benchmarked across four machines — phono3py displacement force sets as the textbook CUDA-graph workload (capture once, replay 111x; x12 single, x28 batched). Sister project to phosbench.
Quantum-chemistry interfaces for machine-learning interatomic potentials
Machine learning simulations to study the molecular structure and dynamics of sodium-ion battery electrolytes; Sodium-Ion Battery Electrolyte Dataset for MLIP Benchmarking
Hands-on guides for materials science simulations and the surrounding dev environment.
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