A user needs at least the following info to train a model: 1) id_prop.csv with name of the file and corresponding value, 2) config_example.json a config file with training and hyperparameters. The main script to train model is train_folder.py. You can install a development version of alignn by cloning the repository and installing in place with pip: git clone Īs an alternate method, ALIGNN can also be installed using pip command as follows: pip install alignn Some of our models may not be stable with the latest DGL release (v1.1.0) so you may wish to install v1.0.2 instead: conda install -c dglteam/label/cu118 dgl=1.0.2.cu118 Then install the matching DGL version conda install -c dglteam/label/cu118 dgl To on linux with cudatoolkit 11.8 run conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia If you need CUDA support, it's best to install PyTorch and DGL before installing alignn to ensure that you get a CUDA-enabled version of DGL. Now, let's make a conda environment, say "version", choose other name as you like:: conda create -name version python=3.10 Now, bash Miniconda3-latest-Linux-x86_64.sh (for linux)īash Miniconda3-latest-MacOSX-x86_64.sh (for Mac)ĭownload 32/64 bit python 3.10 miniconda exe and install (for windows) The line graph convolution updates the triplet representations and the pair representations the direct graph convolution further updates the pair representations and the atom representations.īased on your system requirements, you'll get a file something like 'Miniconda3-latest-XYZ'. The atomistic line graph L(g) represents relationships between atom triplets: it has nodes corresponding to bonds (sharing representations e ij with those in g) and edges corresponding to bond angles (with angle/triplet representations t ijk). The atomistic graph g consists of a node for each atom i (with atom/node representations h i), and one edge for each atom pair within a cutoff radius (with bond/pair representations e ij). 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). The Atomistic Line Graph Neural Network ( ) introduces a new graph convolution layer that explicitly models both two and three body interactions in atomistic systems.
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