Understanding Equivariant N Centered Representations For Atomistic Machine Learning
Let's dive into the details surrounding Equivariant N Centered Representations For Atomistic Machine Learning. Lennard-Jones Centre discussion group seminar by Jigyasa Nigam from the Swiss Federal Institute of Technology Lausanne ...
Key Takeaways about Equivariant N Centered Representations For Atomistic Machine Learning
- Join the
- Episode 6: In this episode, we explore ML models that have
- Papers / Resources ▭▭▭ Fabian Fuchs Equivariance: https://fabianfuchsml.github.io/equivariance1of2/ Deep
- Speaker: Robin Walters, Northeastern University Title:
- Abstract: Applications such as climate science and transportation require
Detailed Analysis of Equivariant N Centered Representations For Atomistic Machine Learning
Speaker: Jigyasa NIGAM (EPFL, Switzerland) Young Researchers' Workshop on Try datamol.io - the open source toolkit that simplifies molecular processing and featurization workflows for Soledad Villar (Johns Hopkins) https://simons.berkeley.edu/talks/
Lecture 1: Regular group convolutional neural networks.
That wraps up our extensive overview of Equivariant N Centered Representations For Atomistic Machine Learning.
