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SchNetPack is a toolbox for the development and application of deep neural networks to the prediction of potential energy surfaces and other quantum-chemical properties of molecules and materials. It contains basic building blocks of atomistic neural networks, manages their training and provides…
Official Repository for the Uni-Mol Series Methods
MatGL (Materials Graph Library) is a graph deep learning library for materials science. Mathematical graphs are a natural representation for a collection of atoms. Graph deep learning models have been shown to consistently deliver exceptional performance as surrogate models for the prediction of…
The main features of materialyzeai/matgl are: Atomistic Machine Learning.
Open-source alternatives to materialyzeai/matgl include: atomistic-machine-learning/schnetpack — SchNetPack is a toolbox for the development and application of deep neural networks to the prediction of potential… awslabs/dgl-lifesci — Documentation | Discussion Forum. deepmodeling/uni-mol — Official Repository for the Uni-Mol Series Methods. lamm-mit/graph-aware-transformers — We present an approach to enhancing Transformer architectures by integrating graph-aware relational reasoning into… lanl/hippynn — The hippynn python package - a modular library for atomistic machine learning with pytorch. atomistic-machine-learning/dtnn — Deep Tensor Neural Network.
