Machine learning (ML) has revolutionized materials science and chemistry with the help of deep learning innovations and the availability of larger and larger datasets. Many industrial scientists want to adopt a data-driven and AI-based design approach, but they face challenges with limited datasets and complex materials that need customized feature engineering. Furthermore, typical ML methods often struggle with interpretability and generalization to new chemical domains. In this webinar, we show how Schrödinger’s tools can address these common issues by using a combination of physics-based simulation data, enterprise informatics, and chemistry-aware ML. We illustrate how this synergistic approach can transform materials innovation across a broad range of technology fields. Specifically, we will present case studies in the following areas:

• Using molecular dynamics simulations to generate features that improve the accuracy of ML models for viscosity predictions
• Building interpretable ML models to predict the ionic conductivity of Li-ion battery electrolytes
• Enhancing the performance of ML models for predicting properties such as absorption and emission wavelengths, fluorescence lifetime, and extinction coefficients of organic electronics using features derived from density functional theory

This integrated approach represents a new frontier in materials science and chemistry, combining the strengths of ML and physics-based methods.

Anand Chandrasekaran joined Schrodinger in 2019 and he is currently the product manager of MS-Informatics. He graduated from the group of professor Nicola Marzari in the Swiss Federal Institute of Technology, Lausanne, with a PhD in materials science. Before joining Schrödinger, Anand worked in the group of professor Rampi Ramprasad on a number of topics such as polymer informatics, machine-learning force-fields, and machine-learning for electronic structure calculations.

Schrödinger Inc., Network for Computational Nanotechnology

Researchers should cite this work as follows:

• Anand Chandrasekaran (2023), "Data-Driven Materials Innovation: where Machine Learning Meets Physics," https://nanohub.org/resources/38194.

  BibTex | EndNote

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