Participate in a hands-on workshop demonstrating the use of Schrödinger's AutoQSAR tool for machine learning in predicting ionic conductivity of ionic liquids. Learn how to create and implement machine learning models using Schrödinger's MS Maestro graphical user interface within nanoHUB. Explore the integration of physics-based methods with machine learning in materials science, applicable to various fields including energy materials and organic electronics. Follow along with a step-by-step demonstration of the AutoQSAR tool, understanding its application in materials science and digital materials discovery. Gain insights into atomistic simulation across diverse systems, methods capabilities, and workflows. Discover how to access and utilize nanoHUB resources for this workshop and future projects.
Machine Learning Models for Ionic Conductivity with Schrödinger's AutoQSAR - Hands-On Workshop
Overview
Syllabus
Hands-On Workshop in nanoHUB: Machine Learning Models for Ionic Conductivity with Schrödinger's AutoQSAR
The Schrödinger Platform: An integrated solution for digital materials discovery and analysis
Atomistic Simulation Across Diverse Systems and Applications
Methods Power Capabilities
From Engines to Workflows
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Today's Example
Today's Example
AutoQSAR
nanoHUB Access
Live Demo
Next Steps
Questions? Thank you!
Taught by
nanohubtechtalks
