The use case will dive into the use of APEO-free surfactants in paints and coatings, enabling the formulator to meet sustainability goals and produce safer paints. Reformulation of paints away from alkyl phenol ethoxylate (APEO)-containing surfactants can be a drawn-out process that can negatively impact the performance properties and stability of paints.
This case study will showcase the use of AI to rapidly predict and formulate stable paints using newly introduced APEO-free surfactants. Iterative improvement in formulation performance using sequential learning will be demonstrated, along with suggestions for R&D workflow incorporation and human-in-the-loop modeling best practices.
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Will Erwin is a data scientist with Citrine Informatics and is passionate about leveraging materials data and artificial intelligence to develop better processes and products across the materials and coatings industry. After earning his Ph.D. in chemical engineering from Vanderbilt University, Will worked as a research scientist, later transitioning to data science upon recognizing the impact that democratized AI will have across the materials industry. Will enjoys distilling complex topics at the intersection of machine learning and materials science into actionable insights used by industry leaders.
Tyler Bell is an account executive with Citrine Informatics with an extensive background in paint and coatings. Tyler earned a BS/MS in Chemistry and Polymer Chemistry from California Polytechnic State University, San Luis Obispo and started as a bench chemist for Valspar Paint Company developing new architectural resin systems. He joined Citrine Informatics with the vision of helping companies change the way they develop products by using AI/ML guided experimentation.