Webinar

Leveraging atomistic simulation, machine learning, and cloud-based collaborative ideation for display materials discovery

CalendarDate & Time
  • August 7th, 2024
  • 10:00 AM PT / 1:00 PM ET / 6:00 PM BST / 7:00 PM CEST
LocationLocation
  • Virtual
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The rapid evolution of display technology requires the use of cutting-edge research methods to maintain progress. This webinar will explore the union of physics-based simulations, machine learning (ML), and cloud-native collaboration and informatics tools in revolutionizing R&D innovation for display materials.

We will delve into how physics-based simulations provide a robust foundation for understanding and predicting material behaviors, while ML modeling accelerates the discovery and optimization of new materials through data-driven insights. Furthermore, we will introduce Schrödinger’s LiveDesign, a cutting-edge web-based collaboration platform, designed to facilitate R&D in a modern, digital working environment. LiveDesign supports comprehensive functionalities, including modeling, data processing, data storage, and collaborative ideation, empowering teams to work seamlessly across diverse geographical locations.

Join us to gain a deeper understanding of:

  • The principles and benefits of combining physics-based simulation and machine learning models
  • Strategies for seamless integration of computational approaches in your R&D workflow
  • Real-world examples illustrating the application and impact of integrated models in developing superior display materials
  • How to leverage LiveDesign for collaborative ideation, advanced modeling, and project management

Our Speaker

Hadi Abroshan

Principal Scientist I, Schrödinger

Hadi Abroshan is the Product Manager for Organic Electronics at Schrödinger, Inc. He holds a Ph.D. from Carnegie Mellon University and has conducted research at Stanford University and Georgia Tech. Hadi specializes in multiscale simulations, leading projects to design cost-effective multifunctional materials for optoelectronics. His expertise lies in developing computational strategies that bridge atomistic structures to multilayered device scales, using a blend of physics-based methodologies and machine learning techniques. His work has led to the discovery of novel, environmentally friendly materials and processes with superior efficiencies.

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