We are in the midst of an inflection point in the utilization and impact of molecular modeling in materials science, particularly for industrial applications. This inflection is driven by significant advancements in compute power, methods development, and the integration of physics-based methods with machine learning.

In this workshop, we will demonstrate the hands-on use of Schrödinger's MS Maestro graphical user interface within nanoHUB to perform machine learning model creation and implementation.

In particular, we will walk participants through a hands-on demonstration of Schrödinger’s AutoQSAR tool for predicting experimental ionic conductivity of ionic liquids. Note that while the example demonstrated here will be tailored towards energy materials, the same workflow can be applied for a variety of materials science applications, ranging from organic electronics to complex formulations.

In order to participate in the hands-on portion of the workshop, please be sure to request membership to the Schrödinger Materials Science nanoHUB group (https://nanohub.org/groups/schrodinger) prior to the seminar. Otherwise, feel free to simply watch the demonstration.

Dr. Michael Rauch is a Principal Scientist I at Schrödinger specializing in materials science and education. Michael earned his Ph.D. from Columbia University in synthetic organometallic chemistry as an NSF Graduate Research Fellow before pursuing a postdoctoral role in organic chemistry at the Weizmann Institute of Science as a Zuckerman Postdoctoral Scholar. Michael is particularly interested in green, sustainable chemistry and transforming the way that synthetic chemists utilize molecular modeling via practical education.

Schrödinger Inc., Network for Computational Nanotechnology

Researchers should cite this work as follows:

• Michael Rauch (2023), "Hands-On Workshop in nanoHUB: Machine Learning Models for Ionic Conductivity with Schrödinger's AutoQSAR," https://nanohub.org/resources/38198.

  BibTex | EndNote

1. artificial intelligence (AI)
2. conductance
3. ionic conductivity
4. machine learning
5. materials science
6. physics-based modeling
7. Schrödinger AutoQSAR