Collection of Tutorials on Machine Learning Interatomic Potentials
☆29Jul 26, 2024Updated last year
Alternatives and similar repositories for Tutorials
Users that are interested in Tutorials are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- ☆11Sep 16, 2024Updated last year
- Tools for geometric learning☆12Sep 26, 2025Updated 9 months ago
- Collection of tutorials to use the MACE machine learning force field.☆68Jul 13, 2026Updated last week
- 🌟 [NeurIPS '25 Spotlight] Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics …☆104Jul 2, 2026Updated 2 weeks ago
- CUDA implementations of MACE models☆23Aug 19, 2025Updated 11 months ago
- Simple, predictable pricing with DigitalOcean hosting • AdAlways know what you'll pay with monthly caps and flat pricing. Enterprise-grade infrastructure trusted by 600k+ customers.
- ☆16Oct 1, 2023Updated 2 years ago
- Benchmarking foundation Machine Learning Potentials with Lattice Thermal Conductivity from Anharmonic Phonons☆16Oct 30, 2024Updated last year
- ☆35Jun 17, 2026Updated last month
- Machine Learning Interatomic Potentials with the Atomic Cluster Expansion☆77Jun 29, 2026Updated 3 weeks ago
- Compute neighbor lists for atomistic systems☆83Jul 9, 2026Updated last week
- Crystal Host-Guided Generation☆19Apr 24, 2025Updated last year
- ☆137May 3, 2026Updated 2 months ago
- Calculation of vibrational spectra with quantum nuclear motion☆12Sep 18, 2024Updated last year
- MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.☆1,281Updated this week
- Virtual machines for every use case on DigitalOcean • AdGet dependable uptime with 99.99% SLA, simple security tools, and predictable monthly pricing with DigitalOcean's virtual machines, called Droplets.
- A collection of files related to machine learning force fields☆25Oct 25, 2023Updated 2 years ago
- REICO-unbiased random sampling to generate diverse datasets encompassing a wide range of atomic configurations and bonding scenarios. EML…☆30Feb 14, 2025Updated last year
- Enhanced sampling in the representation space of atomistic neural networks.☆15Jul 13, 2026Updated last week
- ☆57Jun 16, 2026Updated last month
- A Python package for the creation of input files for CP2K, MACE-torch, MatterSim, SevenNet, ORB and Grace as well as the post-processing …☆26May 5, 2026Updated 2 months ago
- MESS: Modern Electronic Structure Simulations☆21Sep 24, 2024Updated last year
- DeePMD-kit plugin for various graph neural network models☆55Jul 13, 2026Updated last week
- MACE foundation models (MP, OMAT, mh-1)☆280Jul 12, 2026Updated last week
- Graph neural network prediction of electronic Hamiltonians in atomic orbital representation with many body messages☆40Jun 2, 2026Updated last month
- GPUs on demand by Runpod - Special Offer Available • AdRun AI, ML, and HPC workloads on powerful cloud GPUs—without limits or wasted spend. Deploy GPUs in under a minute and pay by the second.
- Official repository for the paper "Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials".☆21Oct 29, 2024Updated last year
- ☆12May 10, 2024Updated 2 years ago
- AMLP integrates dataset creation, input/output handling, and analysis for machine learning interatomic potentials. It supports Gaussian, …☆40Mar 9, 2026Updated 4 months ago
- Lab materials for MIT 3.320 and Harvard AP 275 courses on atomistic modeling☆15Apr 20, 2021Updated 5 years ago
- pytorch implementation of dftd2 & dftd3 (not actively maintained)☆107Mar 11, 2026Updated 4 months ago
- Tools for machine learnt interatomic potentials☆49Updated this week
- More efficient and faster version of pyscal☆31May 1, 2026Updated 2 months ago
- ☆18Oct 29, 2025Updated 8 months ago
- ☆24Updated this week
- 1-Click AI Models by DigitalOcean Gradient • AdDeploy popular AI models on DigitalOcean Gradient GPU virtual machines with just a single click. Zero configuration with optimized deployments.
- IANN (InterAtomic Neural Network Framework) is an equivariant interatomic neural network potential framework package☆18Updated this week
- Alchemical machine learning interatomic potentials☆36Nov 8, 2024Updated last year
- Zero Shot Molecular Generation via Similarity Kernels☆29Aug 27, 2025Updated 10 months ago
- ☆13May 28, 2026Updated last month
- A collection of simulation recipes for the atomic-scale modeling of materials and molecules☆54Updated this week
- Gold-Standard Chemical Database 138 (GSCDB138)☆39Jul 12, 2026Updated last week
- 《能源环境材料计算模拟方法》学习笔记，包括linux基础命令、pbs作业管理系统、vasp、materials studio等。☆24Updated this week