Demand for sustainable, eco-friendly cosmetics is growing, but meeting customer demand requires finding formulations that perform at least as well as the existing synthetic alternatives. In this webinar, we explore the challenges chemists face, and how new approaches can help find solutions quicker.
Harnessing advances in machine learning, particularly active learning combined with molecular simulation, holds immense potential for efficient formulation development in the eco-friendly cosmetics industry. However, the limited availability of relevant data poses challenges. Active learning bridges this gap by integrating diverse datasets, enabling the construction of robust machine learning models that cover the extensive design space. Molecular simulation complements this process by predicting physical properties of various formulations.
In this hour-long webinar, you will hear about the efficacy of this approach using rhamnolipid biosurfactants as an eco-friendly formulation example.
By watching the recording of this live webinar you will learn:
• New digital approaches to sustainable cosmetic formulation development
• How machine learning methods and molecular simulations reduce formulation development cycle time
• To identify key areas in your R&D where machine learning and molecular simulations can provide value
19 сен 2024
