Speaker: Dr. Leonie Koch, Schrödinger

Topic: Materials Science Suite for Polymer and Battery Applications

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Abstract: Cost and time consuming trial and error procedures can significantly slow down innovation, which becomes detrimental to our ability to comply with novel environmental legislation and to develop sustainable supply chains while facing steadily increasing material consumption. Physics-based modeling and machine learning can significantly accelerate the discovery and improvement of sustainable materials using virtual screening of the vast chemical space such that only the most promising candidates will enter the experimental design cycle. Equally, computer simulations substantially contribute to the decoding of structure-property relationships, which help to understand and reformulate existing solutions.

In this presentation, we will show how Schrödinger’s digital technology can catalyze selection processes and even open up larger chemical search spaces by predicting key properties of battery and polymer materials. This includes the generation of structural models using advanced building techniques as well as a multiscale simulation approach, ranging from molecular quantum chemistry to coarse grained models for classical molecular dynamics simulations and complemented by machine learning. Using this approach enables us to understand, amongst others, degradation thermodynamics, transport mechanisms, or thermophysical and mechanical properties.

Bio: Dr. Leonie Koch is a Materials Science Application Scientist at Schrödinger GmbH. Prior to joining Schrödinger in 2021, she received her PhD in Materials Science from the Technical University in Darmstadt with a strong focus on solid state physics applications. At Schrödinger, she applies physics-based modeling and machine learning techniques to various research projects in materials science.