Artificial intelligence (AI) and machine learning (ML) are transforming materials science, unlocking new possibilities for innovation by enabling data-driven design and optimization across a wide range of applications. From accelerating the discovery of novel materials to optimizing formulations for specific performance criteria, AI/ML allows researchers to explore complex chemical spaces with unprecedented speed and precision. These approaches reduce reliance on trial-and-error experimentation, even in data-limited environments; empowering scientists to tackle challenges across diverse technology domains, including electronics, energy storage, polymers, and catalysis. Schrödinger’s integrated platform, which combines chemistry-informed ML with physics-based simulations, enhances predictability, scalability, and overall innovation in materials design.

In this webinar, we will explore how AI/ML is driving impactful advancements in materials innovation, highlighting case studies that illustrate cutting-edge ML techniques in diverse applications. Additionally, we will introduce the Schrödinger platform, illustrating how it empowers researchers to efficiently design and optimize materials. Specifically, we will highlight:

• Design of novel OLED devices with physics-augmented machine learning
• Optimization of materials properties for consumer packaged goods, battery electrolytes, polymers, and catalysis
• Utilization of machine learning force fields (MLFF) for enhanced throughput and precision of atomistic simulations