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Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ https://www.youtube.com/@dr_k_choudhary
| Date | Stars |
|---|---|
| 2026-07-31 | 329 |
| 2026-08-05 | 328 |
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| 2026-08-13 | 329 |
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| 2026-08-28 | 330 |
| 2026-08-31 | 330 |
| 2026-09-08 | 331 |
| 2026-09-19 | 332 |
| 2026-09-20 | 332 |
Today
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This week
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Momentum
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growth rate 0.30%/day
 [](https://codecov.io/gh/usnistgov/alignn) [](https://badge.fury.io/py/alignn)    [](https://pepy.tech/project/alignn) <!-- [](https://paperswithcode.com/sota/formation-energy-on-materials-project?p=atomistic-line-graph-neural-network-for) [](https://paperswithcode.com/sota/band-gap-on-materials-project?p=atomistic-line-graph-neural-network-for) [](https://paperswithcode.com/sota/formation-energy-on-qm9?p=atomistic-line-graph-neural-network-for) [](https://paperswithcode.com/sota/formation-energy-on-jarvis-dft-formation?p=atomistic-line-graph-neural-network-for) [](https://paperswithcode.com/sota/band-gap-on-jarvis-dft?p=atomistic-line-graph-neural-network-for) --> # Table of Contents * [Introduction](#intro) * [Installation](#install) * [Examples](#example) * [Pre-trained models](#pretrained) * [JARVIS-ALIGNN webapp](#webapp) * [ALIGNN-FF & ASE Calculator](#alignnff) * [Peformances on a few datasets](#performances) * [Useful notes](#notes) * [References](#refs) * [How to contribute](#contrib) * [Correspondence](#corres) * [Funding support](#fund) <a name="intro"></a> # ALIGNN & ALIGNN-FF (Introduction) The Atomistic Line Graph Neural Network (https://www.nature.com/articles/s41524-021-00650-1) introduces a new graph convolution layer that explicitly models both two and three body interactions in atomistic systems. This is achieved by composing two edge-gated graph convolution layers, the first applied to the atomistic line graph *L(g)* (representing triplet interactions) and the second applied to the atomistic bond graph *g* (representing pair interactions). Atomisitic line graph neural network-based FF (ALIGNN-FF) (https://pubs.rsc.org/en/content/articlehtml/2023/dd/d2dd00096b ) can be used to model both structurally and chemically diverse systems with any combination of 89 elements from the periodic table, specially for structural optimization. To train the ALIGNN-FF model, we have used the JARVIS-DFT dataset which contains around 75000 materials and 4 million energy-force entries, out of which 307113 are used in the training. These models can be further finetuned, or new models can be developed from scratch on a new dataset.  <a name="install"></a> Installation ------------------------- First create a conda environment: Install miniconda environment from https://conda.io/miniconda.html Based on your system requirements, you'll get a file something like 'Miniconda3-latest-XYZ'. Now, ``` bash Miniconda3-latest-Linux-x86_64.sh (for linux) bash Miniconda3-latest-MacOSX-x86_64.sh (for Mac) ``` Download 32/64 bit python 3.10 miniconda exe
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d45ff02e8a325af7, llm:description: 'Atomistic Line Graph Neural Network' (graph neural network for atomistic/materials modeling)
matched fp:d45ff02e8a325af7, llm:description: 'Atomistic Line Graph Neural Network' (graph neural network for atomistic/materials modeling)