An ASE-friendly implementation of the amorphous-to-crystalline (a2c) workflow.
☆18Oct 19, 2025Updated 4 months ago
Alternatives and similar repositories for a2c_ase
Users that are interested in a2c_ase are comparing it to the libraries listed below
Sorting:
- ☆43Updated this week
- JAX implementation of the NequIP neural network interatomic potential☆16Feb 24, 2026Updated last week
- Alchemical machine learning interatomic potentials☆34Nov 8, 2024Updated last year
- ☆31Updated this week
- dataset augmentation for atomistic machine learning☆23Nov 21, 2025Updated 3 months ago
- A molecular simulation package integrating MLFFs in MOFs for DAC☆42Oct 17, 2025Updated 4 months ago
- Reproduction of CGCNN for predicting material properties☆23Mar 2, 2026Updated last week
- ☆22May 7, 2025Updated 10 months ago
- Compute neighbor lists for atomistic systems☆74Feb 27, 2026Updated last week
- Equitrain: A Unified Framework for Training and Fine-tuning Machine Learning Interatomic Potentials☆11Jan 28, 2026Updated last month
- Collection of tutorials to use the MACE machine learning force field.☆54Jan 22, 2026Updated last month
- Torch-native C++/CUDA library to accelerate tensor-product layers in MLIPs☆55Nov 26, 2025Updated 3 months ago
- Heat-conductivity benchmark test for foundational machine-learning potentials☆30Jan 29, 2026Updated last month
- Local Environment-based Atomic Features☆13Dec 19, 2024Updated last year
- Adds Orb Model functionality to LAMMPS via Python wrapping☆15Apr 1, 2025Updated 11 months ago
- RAGSkeleton: A foundational, modular framework for building customizable Retrieval-Augmented Generation (RAG) systems across any domain.☆14Jun 24, 2025Updated 8 months ago
- A collection of simulation recipes for the atomic-scale modeling of materials and molecules☆39Updated this week
- Graph neural network prediction of electronic Hamiltonians in atomic orbital representation with many body messages☆27Feb 18, 2026Updated 2 weeks ago
- DiffSyn: A Generative Diffusion Approach to Materials Synthesis Planning (Nature Computational Science, 2026)☆31Feb 10, 2026Updated 3 weeks ago
- AMLP integrates dataset creation, input/output handling, and analysis for machine learning interatomic potentials. It supports Gaussian, …☆31Updated this week
- GRACE models and gracemaker (as implemented in TensorPotential package)☆86Updated this week
- [NeurIPS'25 AI4Mat] Nequix: Training a foundation model for materials on a budget and [arXiv'26] Phonon fine-tuning (PFT)☆69Updated this week
- MLP training for molecular systems☆57Feb 24, 2026Updated 2 weeks ago
- train and use graph-based ML models of potential energy surfaces☆122Updated this week
- Flow matching for accelerated simulation of atomic transport☆63Oct 17, 2025Updated 4 months ago
- [NeurIPS 2024] Official implementation of the Efficiently Scaled Attention Interatomic Potential☆60Sep 26, 2025Updated 5 months ago
- Phonons from ML force fields☆23Jul 7, 2025Updated 8 months ago
- CUDA implementations of MACE models☆23Aug 19, 2025Updated 6 months ago
- Library for efficient training and application of Machine Learning Interatomic Potentials (MLIP)☆82Jan 7, 2026Updated 2 months ago
- MESS: Modern Electronic Structure Simulations☆43Sep 26, 2025Updated 5 months ago
- Generate symmetrized force constants☆26Mar 3, 2026Updated last week
- ☆36Feb 22, 2026Updated 2 weeks ago
- Atomistic machine learning models you can use everywhere for everything☆36Updated this week
- ☆24Nov 1, 2024Updated last year
- More efficient and faster version of pyscal☆28Mar 2, 2026Updated last week
- A unified platform for fine-tuning atomistic foundation models in chemistry and materials science☆72Feb 5, 2026Updated last month
- ☆20May 7, 2024Updated last year
- Cross-platform Optimizer for ML Interatomic Potentials☆23Aug 31, 2025Updated 6 months ago
- ☆13Dec 14, 2024Updated last year