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…
The main features of atomistic-machine-learning/schnetpack are: Atomistic Machine Learning.
Open-source alternatives to atomistic-machine-learning/schnetpack include: 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. learningmatter-mit/uvvisml — UVVisML [//]: # (Badges). atomistic-machine-learning/dtnn — Deep Tensor Neural Network.
Official Repository for the Uni-Mol Series Methods
We present an approach to enhancing Transformer architectures by integrating graph-aware relational reasoning into their attention mechanisms. Building on the inherent connection between attention and graph theory, we reformulate the Transformer’s attention mechanism as a graph operation and…
