Some tutorial-style examples for validating machine-learned interatomic potentials
☆34Dec 4, 2023Updated 2 years ago
Alternatives and similar repositories for how-to-validate-potentials
Users that are interested in how-to-validate-potentials are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- train and use graph-based ML models of potential energy surfaces☆123May 7, 2026Updated 2 weeks ago
- `quansino` is a modular package based on the Atomic Simulation Environment (ASE) for quickly building custom Monte Carlo algorithms☆29Updated this week
- ⚛ download and manipulate atomistic datasets☆49Nov 25, 2025Updated 6 months ago
- A Benchmarking Framework for Crystal GNNs☆21Jan 3, 2024Updated 2 years ago
- Machine Learning Interatomic Potentials with the Atomic Cluster Expansion☆73Apr 22, 2026Updated last month
- Deploy to Railway using AI coding agents - Free Credits Offer • AdUse Claude Code, Codex, OpenCode, and more. Autonomous software development now has the infrastructure to match with Railway.
- Python bindings for the `buildcell` program for Ab Initio Random Structure Searching (AIRSS)☆19May 16, 2026Updated last week
- A python library for calculating materials properties from the PES☆142Updated this week
- Alchemical machine learning interatomic potentials☆34Nov 8, 2024Updated last year
- Heat-conductivity benchmark test for foundational machine-learning potentials☆30Jan 29, 2026Updated 3 months ago
- Generate symmetrized force constants☆27May 19, 2026Updated last week
- NequIP extension package that adapts the Allegro equivariant GNN architecture to predict the electric response of materials☆38Mar 19, 2026Updated 2 months ago
- ASE framework for Monte Carlo simulations with machine learned interatomic potentials☆23May 6, 2026Updated 3 weeks ago
- 🌟 [NeurIPS '25 Spotlight] Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics …☆100Apr 16, 2026Updated last month
- MLP training for molecular systems☆59May 1, 2026Updated 3 weeks ago
- Deploy to Railway using AI coding agents - Free Credits Offer • AdUse Claude Code, Codex, OpenCode, and more. Autonomous software development now has the infrastructure to match with Railway.
- Code for automated fitting of machine learned interatomic potentials.☆148May 14, 2026Updated last week
- Reproduction of CGCNN for predicting material properties☆27May 7, 2026Updated 2 weeks ago
- Collective atomic modulation analysis with irreducible space-group representation☆18May 18, 2026Updated last week
- A collection of files related to machine learning force fields☆23Oct 25, 2023Updated 2 years ago
- A 22.9 million carbon atom dataset☆16Mar 7, 2023Updated 3 years ago
- Allen-Feldman thermal conductivity compatible to GULP implementation☆22Apr 1, 2025Updated last year
- JAX implementation of the NequIP neural network interatomic potential☆17Feb 24, 2026Updated 3 months ago
- Band structure unfolding made easy!☆67Apr 28, 2026Updated 3 weeks ago
- Fully validating pure-python CP2K input file tools including preprocessing capabilities☆59May 18, 2026Updated last week
- Deploy to Railway using AI coding agents - Free Credits Offer • AdUse Claude Code, Codex, OpenCode, and more. Autonomous software development now has the infrastructure to match with Railway.
- CatBench - Benchmark Framework of Machine Learning Interatomic Potentials in Adsorption Energy Predictions☆55Apr 19, 2026Updated last month
- ☆16Oct 1, 2023Updated 2 years ago
- Particle-mesh based calculations of long-range interactions in PyTorch☆79May 3, 2026Updated 3 weeks ago
- Universal interatomic potentials for advanced materials modeling☆207Updated this week
- Julia implementation of algorithm for counting primitive rings in an atomistic structure. Useful for materials simulations