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Accelerating Formulation Design via Machine Learning: Generating a High-throughput Shampoo Formulations Dataset
Aniket Chitre1,2,3, Robert C M Querimit3,4, Simon D Rihm1,2
1Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.
Scientific Data
|July 3, 2024
Summary
This study introduces a large, open dataset for designing liquid formulations, accelerating product development using machine learning (ML) models trained on diverse ingredients and providing uncertainty measurements for improved predictions.
Area of Science:
- Formulation Science
- Materials Science
- Chemical Engineering
Background:
- Liquid formulations development is complex and time-consuming due to intricate ingredient interactions.
- Current machine learning (ML) models for formulation design are limited by small datasets and lack of structural information, hindering predictive accuracy.
- Accelerating the design of liquid formulations requires comprehensive datasets and advanced ML approaches.
Purpose of the Study:
- To create a high-dimensional, open experimental dataset for training ML models in rinse-off formulations.
- To expand the ingredient chemical space and design dimensionality for robust ML model development.
- To generate high-fidelity data including phase stability, turbidity, and rheology with sample-specific uncertainty.
Main Methods:
- Curated a dataset of 812 liquid formulations using eighteen diverse ingredients.
- Employed a semi-automated, ML-driven workflow for data generation.
- Measured phase stability, turbidity, and rheological properties for all formulations.
- Incorporated sample-specific uncertainty measurements for advanced model training.
Main Results:
- Developed a dataset with an over 50-fold increase in design space dimensionality compared to previous work.
- Included 294 stable formulations covering the entire design space.
- Generated comprehensive physical property data with associated uncertainty estimates.
- Demonstrated the utility of the dataset for training predictive surrogate models.
Conclusions:
- The presented dataset significantly advances ML-driven liquid formulations design by providing unprecedented scale and detail.
- The inclusion of uncertainty measurements enables the development of more reliable and accurate predictive models.
- This resource accelerates the development cycle for rinse-off formulations, enabling better tuning to target properties.
