Ramprasad-Group / ML-DFT
Predict the electronic structure and atomic properties (potential energy, forces, and stress tensor) of polymers containing N and/or O.
☆17Updated 5 months ago
Alternatives and similar repositories for ML-DFT:
Users that are interested in ML-DFT are comparing it to the libraries listed below
- ☆24Updated 10 months ago
- Quick Uncertainty and Entropy via STructural Similarity☆33Updated last month
- Official repository for the paper "Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials".☆15Updated 3 months ago
- Symmetry-Adapted Learning of Three-dimensional Electron Densities (and their electrostatic response)☆31Updated this week
- Wyckoff Inorganic Crystal Generator Framework☆20Updated last year
- Official implementation of DeepDFT model☆69Updated last year
- A molecular simulation package integrating MLFFs in MOFs for DAC☆24Updated last month
- add the influence of external field to REANN model☆23Updated 4 months ago
- python workflow toolkit☆37Updated this week
- Collection of Tutorials on Machine Learning Interatomic Potentials☆17Updated 6 months ago
- Benchmarking foundation Machine Learning Potentials with Lattice Thermal Conductivity from Anharmonic Phonons☆15Updated 3 months ago
- Alchemical machine learning interatomic potentials☆14Updated 3 months ago
- Active learning workflow developed as a part of the upcoming article "Machine Learning Inter-Atomic Potentials Generation Driven by Activ…☆27Updated 3 years ago
- A unified platform for fine-tuning atomistic foundation models in chemistry and materials science☆17Updated last week
- This repo contains the datasets used for the paper The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials.☆18Updated 2 years ago
- A flexible workflow for on-the-fly learning of interatomic potential models.☆25Updated 9 months ago
- Particle-mesh based calculations of long-range interactions in PyTorch☆32Updated this week
- Public repository for symmetry-adapted Gaussian Process Regression (SA-GPR)☆20Updated last week
- Collection of tutorials to use the MACE machine learning force field.☆43Updated 5 months ago
- Some tutorial-style examples for validating machine-learned interatomic potentials☆31Updated last year
- Charge equilibration method for crystal structures☆11Updated 2 years ago
- GRACE models and gracemaker (as implemented in TensorPotential package)☆38Updated last week
- Metadynamics code on the G-space.☆14Updated 2 years ago
- Generative materials benchmarking metrics, inspired by guacamol and CDVAE.☆35Updated 8 months ago
- ☆58Updated this week
- ☆44Updated 8 months ago
- ☆9Updated 3 months ago
- ☆11Updated last year
- A framework for performing active learning for training machine-learned interatomic potentials.☆31Updated 3 months ago
- Force-field-enhanced Neural Networks optimized library☆28Updated this week