Differentiable Simulations for Enhanced Sampling of Rare Events
Offered By: Valence Labs via YouTube
Course Description
Overview
Explore cutting-edge applications of deep learning and auto-differentiation techniques in molecular simulations through this comprehensive talk. Delve into active learning of machine learning potentials, deep neural network generative models for coarse-grained atomic system representations, and differentiable simulations for reaction path finding. Examine the challenges of overfitting and generalizability in AI for science, discussing the scalability of active learning in molecular simulations and the sensitivity of deep learning solutions to molecular problems. Gain insights into screening photoswitchable drugs, differentiable uncertainty, and the issues and tools needed for advancing this field.
Syllabus
- Intro
- Virtuous Cycle for Design
- Autodiff, Uncertainty, and ML Potentials
- Using Neural Network Potentials for Molecules
- Screening Photoswitchable Drugs
- Differentiable Uncertainty
- Beyond Forces
- Differentiable Simulations
- Issues and Tools Needed
- Q+A
Taught by
Valence Labs
Related Courses
Visual Recognition & UnderstandingUniversity at Buffalo via Coursera Deep Learning for Computer Vision
IIT Hyderabad via Swayam Deep Learning in Life Sciences - Spring 2021
Massachusetts Institute of Technology via YouTube Advanced Deep Learning Methods for Healthcare
University of Illinois at Urbana-Champaign via Coursera Generative Models
Serrano.Academy via YouTube