dogusariturk / PhaseForgeLinks
PhaseForge is a framework for high-throughput alloy phase diagram prediction using machine learning interatomic potentials (MLIPs) integrated with ATAT and CALPHAD modeling. Includes tools for structure relaxation, molecular dynamics, and TDB generation.
☆48Updated last month
Alternatives and similar repositories for PhaseForge
Users that are interested in PhaseForge are comparing it to the libraries listed below
Sorting:
- Quick Uncertainty and Entropy via STructural Similarity☆50Updated last month
- ☆27Updated 2 months ago
- OVITO Python modifier to compute the Warren-Cowley parameters.☆34Updated 6 months ago
- Heat-conductivity benchmark test for foundational machine-learning potentials☆28Updated 2 months ago
- OVITO Python modifier to generate bulk crystal structures with target Warren-Cowley parameters.☆27Updated 9 months ago
- Active Learning for Machine Learning Potentials☆59Updated 2 months ago
- Graph neural network prediction of electronic Hamiltonians in atomic orbital representation with many body messages☆24Updated 2 weeks ago
- A molecular simulation package integrating MLFFs in MOFs for DAC☆37Updated 2 weeks ago
- Some tutorial-style examples for validating machine-learned interatomic potentials☆34Updated last year
- Cross-platform Optimizer for ML Interatomic Potentials☆20Updated last month
- `quansino` is a modular package based on the Atomic Simulation Environment (ASE) for quickly building custom Monte Carlo algorithms☆28Updated 3 weeks ago
- Python package to interact with high-dimensional representations of the chemical elements☆46Updated last week
- GRACE models and gracemaker (as implemented in TensorPotential package)☆74Updated this week
- DistMLIP: A Distributed Inference Library for Fast, Large Scale Atomistic Simulation☆86Updated last month
- Tutorial exercises for the OPTIMADE API☆16Updated 2 years ago
- Phonons from ML force fields☆23Updated 3 months ago
- ☆21Updated last year
- Alchemical machine learning interatomic potentials☆32Updated 11 months ago
- Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics https://arxiv.org/abs/2509.…☆77Updated last week
- Benchmarking foundation Machine Learning Potentials with Lattice Thermal Conductivity from Anharmonic Phonons☆16Updated last year
- ML potentials via transfer learning☆20Updated 2 months ago
- ☆27Updated 3 months ago
- An algorithm to match crystal structures atom-to-atom☆53Updated 2 years ago
- Machine-Learned Interatomic Potential eXploration (mlipx) is designed at BASF for evaluating machine-learned interatomic potentials (MLIP…☆96Updated 2 weeks ago
- ☆13Updated 3 weeks ago
- Code repository for a tutorial based on the "Direct prediction of phonon density of states with Euclidean neural networks"☆30Updated last year
- Collection of tutorials to use the MACE machine learning force field.☆48Updated last year
- Global Optimizer for Clusters, Interfaces, and Adsorbates☆28Updated 7 months ago
- A collection of files related to machine learning force fields☆21Updated 2 years ago
- MACE_Osaka24 models☆20Updated 10 months ago