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.
☆55Updated 2 months ago
Alternatives and similar repositories for PhaseForge
Users that are interested in PhaseForge are comparing it to the libraries listed below
Sorting:
- ☆40Updated last month
- Quick Uncertainty and Entropy via STructural Similarity☆55Updated last week
- Active Learning for Machine Learning Potentials☆63Updated last month
- 🌟 [NeurIPS '25 Spotlight] Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics …☆87Updated 3 weeks ago
- Python package to interact with high-dimensional representations of the chemical elements☆47Updated last week
- MACE_Osaka24 models☆25Updated last year
- A RL framework for Crystal Structure Generation using GRPO☆37Updated last month
- DistMLIP: A Distributed Inference Library for Fast, Large Scale Atomistic Simulation☆91Updated 3 months ago
- A unified platform for fine-tuning atomistic foundation models in chemistry and materials science☆71Updated last month
- OVITO Python modifier to compute the Warren-Cowley parameters.☆39Updated 9 months ago
- GRACE models and gracemaker (as implemented in TensorPotential package)☆80Updated last month
- A molecular simulation package integrating MLFFs in MOFs for DAC☆41Updated 3 months ago
- Particle-mesh based calculations of long-range interactions in PyTorch☆67Updated last month
- Heat-conductivity benchmark test for foundational machine-learning potentials☆29Updated 5 months ago
- Alchemical machine learning interatomic potentials☆32Updated last year
- MatTen: Equivariant Graph Neural Nets for Tensorial Properties of Materials☆44Updated last month
- Some tutorial-style examples for validating machine-learned interatomic potentials☆34Updated 2 years ago
- Collection of tutorials to use the MACE machine learning force field.☆50Updated last year
- Cross-platform Optimizer for ML Interatomic Potentials☆23Updated 4 months ago
- Graph neural network prediction of electronic Hamiltonians in atomic orbital representation with many body messages☆27Updated 3 months ago
- DARA: Data-driven Automated Rietveld Analysis for powder XRD phase search and refinement☆32Updated 2 weeks ago
- ML potentials via transfer learning☆24Updated 3 weeks ago
- `quansino` is a modular package based on the Atomic Simulation Environment (ASE) for quickly building custom Monte Carlo algorithms☆29Updated last week
- Benchmarking foundation Machine Learning Potentials with Lattice Thermal Conductivity from Anharmonic Phonons☆16Updated last year
- ☆17Updated 2 weeks ago
- ☆16Updated 3 months ago
- A foundational potential energy dataset for materials☆49Updated 2 weeks ago
- Generative materials benchmarking metrics, inspired by guacamol and CDVAE.☆41Updated last year
- Phonons from ML force fields☆23Updated 6 months ago
- Tutorial exercises for the OPTIMADE API☆17Updated 2 years ago