Applications of Machine Learning in Drop Impact Dynamics and Drug Discovery
Creators: Au-Yeung, Lap Q
Publisher: name: University of Alberta Library
Year: 2024

<p>The use of artificial intelligence (AI) not only improves the quality of life but, more importantly, saves a tremendous amount of time, as time is money. It is common to notice the increasing presence of AI in research across different fields. As such, this thesis leverages the power of AI, or machine learning (ML) in particular, to model and predict droplet impact problems in fluid mechanics and oral drug discovery in the pharmaceutical industry.Droplet impact plays a crucial role in many industrial applications and processes, particularly when impacting a heated, nanostructured surface. While many studies have explored the impact of droplets on various types of surfaces, comprehensive phase diagrams—encom-passing all droplet impact events at different surface temperatures ($T<em>s$) and Weber numbers ($We$)—for water droplets impacting heated, nanostructured surfaces (controlled by packing fraction, $\phi$, and surface roughness, $r$) are very limited. Therefore, in the first part of this thesis, multiple data-driven ML techniques are applied to model the droplet impact outcomes (i.e., classification) and the post-impact spreading dynamics (i.e., regression), employing existing experimental data. In particular, the performance of random forest (RF), support vector machines (SVMs), and artificial neural networks (ANNs) in both classification and regression tasks is compared. The testing score using 15\% of the data indicates that the ANN algorithm, with 95.06\% accuracy, outperforms the others when classifying the impact outcomes. Consistent with previous experimental observations, our results illustrate that nanostructures not only introduce distinct impact behaviors, such as central jetting, but also influence the boundaries between the deposition, rebound, and splashing regimes within the phase diagram. An increase in $\phi$ at a constant $r$ promotes deposition and spreading events, while increasing $r$ at a constant $\phi$ results in enhanced heat transfer to promote the Leidenfrost effect for the rebound regime and greater disturbance on the liquid lamella to trigger splashing. Meanwhile, the regression results predicted by the SVM algorithm reveal the existence of a $We$-number threshold governed by the nanostructure parameters. Beyond this threshold, the maximum spreading factor ($\beta</em>{max}$) of a spreading droplet gradually becomes independent of $T<em>s$ as $We$ increases, eventually reaching a universal constant (i.e., independent of surface properties), $\beta</em>{max} \approx 3.84$, at extreme $We$ ($\gtrsim 2000$).The ability to visualize the dissolution profile of drugs dissolving in bodily fluids is an important aspect of drug discovery. However, current research in the field is limited to estimating the solubility or the dissolved fraction at certain time steps based on the drugs&#39; compound structure. Hence, in the second part of this thesis, we incorporate a novel two-stages process to model the complete dissolution profile (i.e., the dissolved mass fraction over time) of oral drugs in water with varying surfactant concentrations of Sodium Lauryl Sulfate (SLS) from the compound structure of the drugs. According to the Noyes-Whitney first-order kinetics equation, the profile of a drug is governed by two main dissolution properties, the dissolution rate constant ($k$) and the solubility dissolved mass fraction ($\phi_s$). The first stage involves using the physics-informed neural network (PINN) algorithm to extract those properties from literature (experiment) data, which will serve as the outputs for the data-driven ML algorithm in the second stage. The dissolution profiles obtained from the PINN algorithm are likely more generalizable than the traditional method of non-linear regression, which is purely statistical. In the second stage of predicting the dissolution properties, four different algorithms—RF, SVMs, deep neural networks (DNNs), and extreme gradient boosting (XGB)—are used. The results indicate that the average accuracy, measured by satisfying both the f1 and f2 scores outlined by the U.S. Food and Drug Administration (FDA), of the dissolution profiles predicted by DNN (61.7\%) is slightly higher (within 10\%) than other algorithms. However, the statistics also demonstrate that most algorithms are sensitive to the ratio and the sampling randomness of the test data splitting. This suggests that the lack of quality data, due to the absence of standardized experimental procedures and an organized database to cover the large variety of drug compounds, contributed to the lower accuracy (than the generally accepted range of 70-80\%) of our data-driven ML algorithms.</p>