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.
Phonon band unfolding from supercells to primitive cells, for relaxed, defective, and moiré structures
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).
High-throughput autonomous framework coupling GPAW (PBE-D3) and equivariant MACE foundation potentials: combinatorial Pt-NxCy SAC design, ab initio sampling, CI-NEB kinetics, and on-the-fly active-learning MD validation.
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.
Autonomous, restartable control plane for computational-materials workflows on HPC systems — SLURM-aware orchestration for VASP, LAMMPS, DeepMD, and MACE
FPBench: application-oriented error decomposition for foundation potentials. Force prediction, phase stability and ordering, ion migration by NEB.
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.
Hands-on guides for materials science simulations and the surrounding dev environment.
Machine learning simulations to study the molecular structure and dynamics of sodium-ion battery electrolytes; Sodium-Ion Battery Electrolyte Dataset for MLIP Benchmarking
Quantum-chemistry interfaces for machine-learning interatomic potentials
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