Click the button below to register
June 24, 2021, 3 p.m. CET

Join Now

Global challenges such as a clean energy future or a circular economy have increased the demand for new materials. Typically, the performance of materials depends on a variety of parameters that make traditional, experimental approaches to research slow, inefficient, and prohibitively expensive. Data-driven approaches can significantly accelerate the discovery process and shorten the time from idea to market.

In this presentation we will demonstrate how the integration of Schrödinger’s machine learning technologies into physics-based modeling can be used to predict the properties of new materials. Use cases from key materials science areas such as polymers and optoelectronic materials will illustrate how data generated from experimental and physics-based models can be used to build machine learning models to predict physical properties and even suggest new compounds. Finally, we show how integrating machine learning approaches into collaborative design schemes can maximize their usability and accessibility for non-expert users.

In this webinar you will:

  • Learn more about the integration of Schrödinger’s machine learning technologies into physics-based modeling, which can be used to predict the properties of new materials
  • Learn about material science use cases and how generated data can be used to build machine learning models
  • Learn how integrating machine learning approaches into collaborative design schemas can maximize their usability and accessibility

Laura Scarbath-Evers, Senior Scientist at Schrödinger

Speaker: Laura Scarbath-Evers, Senior Scientist at Schrödinger

Before Laura Katharina Scarbath-Evers came to Schrödinger as an application scientist for materials science, she studied chemistry at the Eberhard-Karls-Universität Tübingen and at the University of Leipzig and did her doctorate in computational chemistry at the Martin-Luther-Universität Halle Wittenberg.

At Schrödinger, she supports customers in Europe from various material science areas in the successful application of molecular models to advance their research.

Schrödinger Materials logo

Schrödinger is a leading provider of advanced molecular simulations as well as enterprise software solutions and services for its customers in the field of materials science research. Schrödinger also builds close partnerships and collaborations with companies in such areas: asetrochemicals, semiconductors, aerospace and specialty chemicals. Schrödinger was founded in 1990 and has almost 500 employees and branches in the USA, Europe, Japan and India as well as business partners in China and Korea.

Click the button below to register
June 24, 2021, 3 p.m. CET

Join Now

LEAVE A REPLY

Please enter your comment!
Please enter your name here