UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
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Item TOWARDS FULLY AUTOMATED ENHANCED SAMPLING OF NUCLEATION WITH MACHINE-LEARNING METHODS(2024) Zou, Ziyue; Tiwary, Pratyush; Chemistry; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Molecular dynamics (MD) simulation has become a powerful tool to model complex molecular dynamics in physics, materials science, biology, and many other fields of study as it is advantageous in providing temporal and spatial resolutions. However, phenomena of common research interest are often considered rare events, such as nucleation, protein conformational changes, and ligand binding, which occur on timescales far beyond what brute-force all-atom MD simulations can achieve within practical computer time. This makes MD simulation difficult for studying the thermodynamics and kinetics of rare events. Therefore, it is a common practice to employ enhanced sampling techniques to accelerate the sampling of rare events. Many of these methods require performing dimensionality reduction from atomic coordinates to a low-dimensional representation that captures the key information needed to describe such transitions. To better understand the current challenges in studying crystal nucleation with computer simulations, the goal is to first apply developed dimensionality reduction methods to such systems. Here, I will present two studies on applying different machine learning (ML) methods to the study of crystal nucleation under different conditions, i.e., in vacuum and in solution. I investigated how such meaningful low-dimensional representations, termed reaction coordinates (RCs), were constructed as linear or non-linear combinations of features. Using these representations along with enhanced sampling methods, I achieved robust state-to-state back-and-forth transitions. In particular, I focused on the case of urea molecules, a small molecule composed of 8 atoms, which can be easily sampled and is commonly used in daily practice as fertilizer in agriculture and as a nitrogen source in organic synthesis. I then analyzed my samples and benchmarked them against other experimental and computational studies. Given the challenges in studying crystal nucleation using molecular dynamics simulations, I aim to introduce new methods to facilitate research in this field. In the second half of the dissertation, I focused on presenting novel methods to learn low-dimensional representations directly from atomic coordinates without the aid of a priori known features, utilizing advanced machine learning techniques. To test my methods, I applied them to several representative model systems, including Lennard Jones 7 clusters, alanine dipeptide, and alanine tetrapeptide. The first system is known for its well-documented dynamics in colloidal rearrangements relevant to materials science studies, while the latter two systems represent problems related to conformational changes in biophysical studies. Beyond model systems, I also applied my methods to more complex physical systems in the field of materials science, specifically iron atoms and glycine molecules. Notably, the enhanced sampling method integrated with my approaches successfully sampled robust state-to-state transitions between allotropes of iron and polymorphs of glycine.Item Probing Kinetic Processes of Metallic Nanocrystal Formation with Liquid Phase Transmission Electron Microscopy(2021) Wang, Mei; Woehl, Taylor J.; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Metallic nanocrystals are pervasive in many applications, but rational control over their synthesis for desired applications is still difficult due to a lack of understanding of their complex formation mechanisms. The advent of liquid phase transmission electron microscopy (LP-TEM) enables direct imaging of nanocrystal formation in liquids at nanometer scale spatial resolutions in real time. In LP-TEM, the electron beam serves both as the imaging tool and the reducing agent of metal precursor solutions to form nanocrystals. The TEM beam and use of radical scavengers were investigated to establish a reproducible environment for in situ nanocrystal synthesis more representative of wet chemistry synthesis. Through systematic LP-TEM experiments, we have uncovered the effects of electron beam chemistry on the nucleation and growth kinetics of nanocrystals through scaling and reaction kinetic models. These experiments provided a direct calibration of LP-TEM electron beam chemistry and relate the microscope parameters to the chemical reactions involved in nanocrystal synthesis. LP-TEM imaging of silver nanocrystal nucleation showed domains of preferential heterogeneous nucleation on a topographically uniform but chemically patchy water-silicon nitride interface. Next, we demonstrated the self-assembly of 3D platinum supraparticles and uncovered the effects of electron beam and solution chemistry on the structures of nanocrystals. Through quantitative image analysis, we have discovered the self-assembly of nanocrystals was a diffusion-controlled particle attachment process and was mediated by a balance between van der Waals attraction and steric repulsion. Finally, we demonstrated synthesis of bimetallic alloy nanocrystals containing gold and copper with LP-TEM from an electron beam sensitive metal thiolate precursor complex. A range of electron beam synthesis conditions were successfully established that formed alloyed nanocrystals with similar composition and structure as nanocrystals synthesized by wet chemistry. The results of the study demonstrated the important role of capping ligands in facilitating alloy formation through the formation of prenucleation cluster intermediates that prevent inter-metal electron transfer by binding with metal salts to form metal ligand complexes.Item Direct Observation of Amyloid Nucleation under Nanomechanical Stretching(2013) Varongchayakul, Nitinun; Seog, Joonil; Material Science and Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Self-assembly of amyloid nanofiber is associated with functional and pathological processes such as in neurodegenerative diseases. Despite intensive studies, stochastic nature of the process has made it difficult to elucidate molecular mechanisms for the key amyloid nucleation. Here, we investigated the amyloid nucleation of silk-elastin-like peptide (SELP) using time-lapse lateral force microscopy (LFM). By repeated scanning a single line on a SELP-coated mica surface, we observed sudden stepwise height increases, corresponds to nucleation of an amyloid fiber. The lateral force profiles followed either a worm-like chain model or an exponential function, suggesting that the atomic force microscopy (AFM) tip stretches a single or multiple SELP molecules along the scanning direction, serves as the template for further self-assembly perpendicular to the scan direction. Such mechanically induced nucleation of amyloid fibrils allows positional and directional control of amyloid assembly in vitro, which we demonstrate by generating single nanofibers at predetermined nucleation sites.