Exploring the formulations of personal care products using a digital chemistry strategy
Speaker
Jeffrey Sanders
Product Manager of Consumer Packaged Goods
Abstract
The demand for new and innovative personal care products is increasing due to changes in consumer trends and sustainability goals for CPG companies. Customers are becoming more discerning – choosing products based on understanding the ingredients and whether a product is made with natural or petroleum-based materials. These concerns highlight challenges for consumer goods research and development. To meet these challenges and retain their position in the consumer marketplace, new product formulations need to be developed and match the existing formulation’s key properties. Understanding how ingredients behave in products and “in action” will be necessary to drive not only new development but also end-to-end product tracing. To streamline this process, multi-scale physics simulations can be utilized to cut down product development timeline and cost, as well as optimize large-scale production by simulating digital twins. Molecular simulation provides a unique opportunity to predict how individual ingredients will behave in formulations. Atomistic simulations can help researchers and engineers understand product morphology, solubility, and other physical properties if the components are known. Unlike process simulations, only the chemistry and composition are required to build molecular models of up to millions of atoms and predict properties. Beyond physics-based modeling, chemical information can be used to build machine-learned models with existing experimental or sensory data. In this talk, we will show you molecular modeling in action and explore how digital chemistry strategies are driving innovation in personal care product formulations.
Learning Objectives:
- How to gain insight of individual ingredient behavior and key properties of components in formulation using multi-scale physics simulations
- How to predict key properties of formulations with advanced machine learning
- How digital chemistry can accelerate your research and development in personal care product