Knowledge-guided Machine Learning for Sensor-based High-Performance Autonomous Material Characterization

Knowledge-guided machine learning enables sensor-based high-performance material characterization that drives accelerated materials discovery and manufacturing. Traditional materials discovery workflows are driven by low-throughput characterization processes that involve several manual sample preparation steps and require relatively large amounts of material. While automated material dispensing processes now provide the ability to automate the synthesis of materials, the characterization of material composition, structure, and properties remains challenging due to the lack of reliable high-throughput characterization methods. Commercial benchtop characterization instruments are gold standards for characterizing the composition, structure, and properties of materials but lack synergy with state-of-the-art accelerated materials discovery workflows, which are based on miniaturized transducers for material testing (e.g., sensors), automation, and low-volume test formats. Due to the time- and resource-intensive nature of experimentation and the limited budget imposed on autonomous experimentation workflows in practical applications, the data generated from accelerated material discovery workflows are usually sparse and imbalanced, challenging the construction and training of machine learning models. In this dissertation, we create knowledge-guided machine learning models to support sensor-based high-performance autonomous material characterization. Several different types of knowledge-guided machine learning models were established for high-performance sensor-based characterization of material composition and phase. Specifically, three new methodologies are proposed and developed:

1. A new rapid and autonomous high-performance characterization method for accelerated engineering of soft functional materials is proposed to overcome the challenge of low-throughput characterization and manual data analysis. The proposed method is compatible with state-of-the-art material synthesis platforms combining automated sensing and sensor physics-guided machine learning that reduces the characterization cycle time and improves the material phase classification accuracy. Utilizing domain knowledge of measurement processes that generate data (e.g., sensor physics) and thermodynamics that govern material phase for feature engineering improved model and process performance.
2. To help mitigate the challenge of low measurement confidence associated with material composition measurement using biosensors, a novel knowledge-guided machine learning approach that integrates domain knowledge in sensor chemistry and physics is proposed. The proposed method implements data augmentation techniques to address sparsity and imbalance of biosensor data and identified new features in biosensor time-series data that are predictive of target analyte concentration and probability of false positive and negative responses.
3. A novel deep learning model with knowledge-guided cost function supervision is proposed to improve biosensor performance, specifically to improve the classification of false responses and reduce biosensor time delay. This new methodology combines regression- and classification-based data analyses, significantly improving biosensor accuracy and speed. The method fuses theory that governs dynamic sensor response (i.e., data generation) with machine learning models to guide regression and classification tasks, providing improved model interpretability and explainability. With the advancement of knowledge-guided machine learning and sensing technologies, the performance of experimental tools and processes for accelerated materials discovery and manufacturing applications can continue to be improved, particularly with respect to speed and reliability, which are critical performance attributes for future industrial adoption.