ilyes319 / mace-tutorials-cscLinks
☆11Updated last year
Alternatives and similar repositories for mace-tutorials-csc
Users that are interested in mace-tutorials-csc are comparing it to the libraries listed below
Sorting:
- ☆26Updated last month
- Benchmarking foundation Machine Learning Potentials with Lattice Thermal Conductivity from Anharmonic Phonons☆16Updated 11 months ago
- Cross-platform Optimizer for ML Interatomic Potentials☆19Updated last month
- MACE_Osaka24 models☆18Updated 9 months ago
- Quick Uncertainty and Entropy via STructural Similarity☆49Updated this week
- Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics https://openreview.net/foru…☆68Updated 2 weeks ago
- Collection of Tutorials on Machine Learning Interatomic Potentials☆22Updated last year
- Tools for machine learnt interatomic potentials☆38Updated this week
- dataset augmentation for atomistic machine learning☆20Updated 3 months ago
- This repository contains the source code for Bayesian Learned Interatomic Potentials (BLIP)☆28Updated last month
- Compute neighbor lists for atomistic systems☆60Updated this week
- Alchemical machine learning interatomic potentials☆32Updated 10 months ago
- python workflow toolkit☆43Updated 3 weeks ago
- ☆21Updated last year
- Official repository for the paper "Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials".☆21Updated 11 months ago
- ☆28Updated 2 months ago
- Modulated automation of cluster expansion based on atomate2 and Jobflow☆12Updated last week
- Calculation of vibrational spectra with quantum nuclear motion☆12Updated last year
- ☆12Updated 2 weeks ago
- ⚛ download and manipulate atomistic datasets☆47Updated 9 months ago
- Training and evaluating machine learning models for atomistic systems.☆43Updated this week
- A collection of files related to machine learning force fields☆21Updated last year
- Some tutorial-style examples for validating machine-learned interatomic potentials☆35Updated last year
- A collection of simulation recipes for the atomic-scale modeling of materials and molecules☆30Updated last month
- PhaseForge is a framework for high-throughput alloy phase diagram prediction using machine learning interatomic potentials (MLIPs) integr…☆46Updated 3 weeks ago
- Adds Orb Model functionality to LAMMPS via Python wrapping☆15Updated 6 months ago
- OVITO Python modifier to compute the Warren-Cowley parameters.☆33Updated 6 months ago
- Heat-conductivity benchmark test for foundational machine-learning potentials☆27Updated 2 months ago
- Collection of tutorials to use the MACE machine learning force field.☆48Updated last year
- `quansino` is a modular package based on the Atomic Simulation Environment (ASE) for quickly building custom Monte Carlo algorithms☆28Updated this week