AI-driven materials discovery is no longer experimental, it is the new national standard. With the recent launch of the Genesis Mission, the United States is declaring a national commitment to accelerating materials discovery through AI, high-performance computing, and integrated scientific data infrastructure. For teams at the forefront of materials innovation, now is the ideal opportunity to integrate computational workflows into your R&D pipeline.

Many R&D teams are hindered from adopting AI/ML due to the complexity of software tools, steep learning curves, and limited data science support. Schrödinger’s Materials Science Suite is designed to address these challenges by providing a unified and easy-to-use AI/ML platform, powered by state-of-the-art ML technology and backed by a dedicated scientific support team.

Join our upcoming webinar to learn how your R&D organization can remove adoption barriers, accelerate discovery cycles, and align with national AI initiatives. In this webinar, we will demonstrate how MS Informatics, Formulation ML, and Formulation Optimization make advanced property prediction, model building, and ML-driven design of experiments simple, fast, and accessible – even for non-experts. We will showcase how easy it is to apply these tools using experimental datasets across broad MS applications, including formulations, consumer goods, batteries, pharmaceuticals, and beyond.

Join us and see live demos on:

• Training accurate viscosity ML models for binary liquids that can be applied to a variety of material applications
• Scaling up to complex shampoo formulations, where ML models can be predictive of complicated multicomponent systems and provide suggestions of next best experiments

Who should attend:

• R&D leaders
• Innovation managers
• Digitization managers
• Synthetic chemists
• Materials scientists
• Formulation scientists
• Computational materials scientists