How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
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
Rethinking the role of frames for SE(3)-invariant crystal structure modeling Yusei Ito, Tatsunori Taniai, Ryo Igarashi, Yoshitaka Ushiku, and Kanta Ono In The Thirteenth International Conference on Learning Representations (ICLR 2025)
The main features of omron-sinicx/crystalframer are: Atomistic Machine Learning.
Open-source alternatives to omron-sinicx/crystalframer 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.
