Alix Schmidt is a senior data scientist in Dow's Core R&D Information Research team in Midland, Michigan. Alix earned a BS in chemical engineering at the University of Illinois Urbana-Champaign in 2009 and then joined Dow Corning initially as a process research engineer. Since then, Alix has held a variety of roles at Dow Corning and Dow and completed an MS in data science at Northwestern University. Alix has experience with polymer process research, high-throughput research, machine learning for manufacturing troubleshooting, and data-driven product development. Her interest and experience in materials informatics allow her to lead technical data science strategy at Dow, and she has presented and chaired at the AIChE spring meeting on this topic.

Kristin Wallace earned a BS in chemical engineering (2006) and an MS in applied science (optimization focus) (2008) at McMaster University. She has worked on a variety of analytics projects since joining ProSensus Inc. in 2018 as a project engineer in Burlington, Ontario. Her particular interest in product formulation using projection to latent structures (PLS) has led her to be involved with related consulting projects, contributing to the development of FormuSense (commercial software), authoring blogs and magazine articles, as well as presenting and chairing at several AIChE spring meetings. Prior to working at ProSensus, she spent five years designing and troubleshooting non-ferrous electric arc furnaces.

Section 1: Getting Started. 1. The Digital Transformation of R&D Labs. 2. Product Formulation Fundamentals. 3. Defining a Successful Predictive Formulation Project. Section 2: Preparing Your Data. 4. Challenges with Formulation Datasets. 5. Feature Engineering: Enhancing Your Data with Descriptors. 6. Machine Learning for Analysis of Structural Characterization. Section 3: Predictive Modeling. 7. Machine Learning Techniques for Predicting Properties of Formulations. 8. Modeling of Product Formulations Using a Latent Variable Approach. 9. Gaining Trust in Your Model. Section 4: Optimization and Inverse Design. 10. Introduction to Formulation Optimization. 11. Adaptive Experimental Design. 12. Inverse Design via PLS Model Inversion. Section 5: Case Studies and Special Topics. 13. Case Studies. 14. Special Topics. 15. Conclusion.