title: Automated Sampling and Labelling of High-Throughput Nanomaterials Data for Machine Learning and Inference
Creators: name: Ting, Jonathan
Publisher: name: The Australian National University
Year: 2025

description: The advent of high-throughput experimental and computational techniques has generated unprecedented volumes of data across many scientific disciplines, enabling a paradigm shift toward data-intensive scientific discovery. The key bottlenecks for this approach of extracting insights from data lie within issues related to the sampling, labelling, and causal understanding of the data, necessitating the proposal of advanced computational methods to efficiently sample, label, and analyse the data to unlock its full potential. This thesis addresses these challenges by developing methodologies for automated sampling and labelling of high-throughput data to facilitate the application of machine learning and inference techniques, using metal nanoparticles for catalysis as a testbed. In the context of this discipline, the specific complexities that are dealt with include the selection bias in sampling, redundancy in collected data, lack of relevant yet affordable labels, and inefficient methodologies for causal understanding of the data. The thesis starts with the generation of sufficiently comprehensive simulated data sets with minimised selection bias and redundancy. Diverse sets of bimetallic nanoparticles data sets are collected by sampling spatial coordinates from molecular dynamics simulation pipeline designed to cover a wide coverage of the combinatorial space, and extraction of structural features from the coordinates. To mitigate redundancy in the collected data, the thesis develops an iterative approach to accelerate the archetypal analysis algorithm. Along with typical feature selection algorithms, the methodology allows reduction in both the feature and instance spaces of the collected data, culminating in comprehensive data sets with minimised selection bias and redundancy, which serve as a foundation for downstream analyses and machine learning applications. The quest for relevant and affordable labels for the simulated data is tackled in two chapters. Firstly, the thesis developed methodologies to compute the box-counting dimension of the surfaces of atomistic objects as a measure of surface roughness, by representing the surfaces as either voxelised point clouds or mathematically precise objects. This allows the quantification of the surface roughness of the simulated atomistic objects in a precise, scalable, and automated manner. Correlations between the dimensionality of metal nanoparticles with their structural features and catalytic activity are subsequently investigated using the developed methods. The second approach of labelling took a data-driven route, employing the iterative label spreading clustering algorithm to identify atomistic surface patterns, allowing pattern recognition on the surface of nanomaterials to be automated. Domain-relevant metrics are also proposed to evaluate the clustering results, enabling better understanding of the catalytic relevance of identified surface features. The final component of the thesis delves into the automation of the current approach of causal path extraction via structure learning of Bayesian networks, by introducing tables of domain rules prior to the interactive learning process. In conclusion, this thesis presents a suite of automated techniques for sampling, labelling, and causal understanding of high-throughput nanomaterials data. All methods developed are applied to simulated nanoparticle data sets with their impact on the field of nanoscience presented. The findings have broad implications, paving the way for future studies and innovations in automated data analysis for scientific discovery.