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:
- Cross-platform Optimizer for ML Interatomic Potentials☆23Updated 5 months ago
- Benchmarking foundation Machine Learning Potentials with Lattice Thermal Conductivity from Anharmonic Phonons☆16Updated last year
- dataset augmentation for atomistic machine learning☆23Updated 2 months ago
- ☆41Updated last week
- This repository contains the source code for Bayesian Learned Interatomic Potentials (BLIP)☆30Updated 5 months ago
- Tools for machine learnt interatomic potentials☆42Updated this week
- Official repository for the paper "Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials".☆21Updated last year
- Quick Uncertainty and Entropy via STructural Similarity☆56Updated 3 weeks ago
- Alchemical machine learning interatomic potentials☆33Updated last year
- ☆22Updated last year
- python workflow toolkit☆43Updated last month
- Collection of Tutorials on Machine Learning Interatomic Potentials☆25Updated last year
- ☆33Updated last week
- Calculation of vibrational spectra with quantum nuclear motion☆12Updated last year
- MACE_Osaka24 models☆25Updated last year
- A RL framework for Crystal Structure Generation using GRPO☆38Updated last week
- Compute neighbor lists for atomistic systems☆73Updated this week
- Some tutorial-style examples for validating machine-learned interatomic potentials☆34Updated 2 years ago
- Modulated automation of cluster expansion based on atomate2 and Jobflow☆12Updated last week
- PhaseForge is a framework for high-throughput alloy phase diagram prediction using machine learning interatomic potentials (MLIPs) integr…☆58Updated 2 months ago
- ⚛ download and manipulate atomistic datasets☆48Updated 2 months ago
- Train, fine-tune, and manipulate machine learning models for atomistic systems☆54Updated last week
- A collection of files related to machine learning force fields☆22Updated 2 years ago
- Adds Orb Model functionality to LAMMPS via Python wrapping☆15Updated 10 months ago
- 🌟 [NeurIPS '25 Spotlight] Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics …☆88Updated last week
- Heat-conductivity benchmark test for foundational machine-learning potentials☆29Updated last week
- `quansino` is a modular package based on the Atomic Simulation Environment (ASE) for quickly building custom Monte Carlo algorithms☆29Updated last week
- ☆12Updated 3 weeks ago
- ☆30Updated 6 months ago
- A flexible and performant framework for training machine learning potentials.☆32Updated last week