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
Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding Tatsunori Taniai, Ryo Igarashi, Yuta Suzuki, Naoya Chiba, Kotaro Saito, Yoshitaka Ushiku, and Kanta Ono In The Twelfth International Conference on Learning Representations (ICLR 2024)
The main features of omron-sinicx/crystalformer are: Atomistic Machine Learning.
Open-source alternatives to omron-sinicx/crystalformer 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.
