Physics
Permanent URI for this communityhttp://hdl.handle.net/1903/2269
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Item Analyzing and Enhancing Molecular Dynamics Through the Synergy of Physics and Artificial Intelligence(2024) Wang, Dedi; Tiwary, Pratyush; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Rapid advances in computational power have made all-atom molecular dynamics (MD) a powerful tool for studying systems in biophysics, chemical physics and beyond. By solving Newton's equations of motion in silico, MD simulations allow us to track the time evolution of complex molecular systems in an all-atom, femtosecond resolution, enabling the evaluation of both their thermodynamic and kinetic properties. Though MD simulations are powerful, their effectiveness is often hampered by the large amount of data they produce. For instance, a standard microsecond-long simulation of a protein can easily generate hundreds of gigabytes of data, which can be difficult to analyze. Moreover, the time required to conduct these simulations can be prohibitively long. Microsecond-long simulations often take weeks to complete, whereas the processes of interest may occur on the timescale of milliseconds or even hundreds of seconds. These factors collectively pose significant challenges in leveraging MD simulations for comprehensive analysis and exploration of chemical and biological systems. In this thesis, I address these challenges by leveraging physics-inspired insights to learn unique, useful, and also meaningful low-dimensional representations of complex molecular systems. These representations enable effective analysis and interpretation of the vast amount of data generated from experiments and simulations. These representations have proven to be valuable in providing mechanistic insights into some fundamental problems within theoretical chemistry and biophysics, such as understanding the interplay between long-range and short-range forces in ion pair dissociation and the transformation of proteins from unstable random coils to structured forms. Furthermore, these physics-informed representations play a crucial role in enhancing MD simulations. They facilitate the construction of simplified kinetic models, enabling the generation of dynamical trajectories spanning significantly longer time scales than those accessible by conventional MD simulations. Additionally, they can serve as blueprints to guide the sampling process in combination with existing enhanced sampling methods. Through this thesis, I showcase how the synergy between physics and AI can advance our understanding of molecular systems and facilitate more efficient and insightful analysis in the fields of computational chemistry and biophysics.Item ON THEORETICAL ANALYSES OF QUANTUM SYSTEMS: PHYSICS AND MACHINE LEARNING(2022) Guo, Shangjie; Spielman, Ian B; Taylor, Jacob M; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Engineered quantum systems can help us learn more about fundamental physics topics and quantum technologies with real-world applications. However, building them could involve several challenging tasks, such as designing more noise-resistant quantum components in confined space, manipulating continuously-measured quantum systems without destroying coherence, and extracting information about quantum phenomena using machine learning (ML) tools. In this dissertation, we present three examples from the three aspects of studying the dynamics and characteristics of various quantum systems. First, we examine a circuit quantum acoustodynamic system consisting of a superconducting qubit, an acoustical waveguide, and a transducer that nonlocally couples both. As the sound signals travel $10^5$ times slower than the light and the coupler dimension extends beyond a few phonon emission wavelengths, we can model the system as a non-Markovian giant atom. With an explicit result, we show that a giant atom can exhibit suppressed relaxation within a free space and an effective vacuum coupling emerges between the qubit excitation and a confined acoustical wave packet. Second, we study closed-loop controls for open quantum systems using weakly-monitored Bose-Einstein condensates (BECs) as a platform. We formulate an analytical model to describe the dynamics of backaction-limited weak measurements and temporal-spatially resolved feedback imprinting. Furthermore, we design a backaction-heating-prevention feedback protocol that stabilizes the system in quasi-equilibrium. With these results, we introduce closed-loop control as a powerful instrument for engineering open quantum systems. At last, we establish an automated framework consisting of ML and physics-informed models for solitonic feature identification from experimental BEC image data. We develop classification and object detection algorithms based on convolutional neural networks. Our framework eliminates human inspections and enables studying soliton dynamics from numerous images. Moreover, we publish a labeled dataset of soliton images and an open-source Python package for implementing our framework.Item SCALABLE MODELING APPROACHES IN SYSTEMS IMMUNOLOGY(2020) Park, Kyemyung; Levy, Doron; Tsang, John S; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Systems biology seeks to build quantitative predictive models of biological system behavior. Biological systems, such as the mammalian immune system, operate across multiple spatiotemporal scales with a myriad of molecular and cellular players. Thus, mechanistic, predictive models describing such systems need to address this multiscale nature. A general outstanding problem is to cope with the high-dimensional parameter space arising when building reasonably detailed models. Another challenge is to devise integrated frameworks incorporating behavioral characteristics manifested at various organizational levels seamlessly. In this dissertation, I present two research projects addressing problems in immunological, or biological systems in general, using quantitative mechanistic models and machine learning, touching on the aforementioned challenges in scalable modeling. First, I aimed to understand how cell-to-cell heterogeneities are regulated through gene expression variations and their propagation at the single-cell level. To better understand detailed gene regulatory circuit models with many parameters without analytical solutions, I developed a framework called MAchine learning of Parameter-Phenotype Analysis (MAPPA). MAPPA combines machine learning approaches and stochastic simulation methods to dissect the mapping between high- dimensional parameters and phenotypes. MAPPA elucidated regulatory features of stochastic gene-gene correlation phenotypes. Next, I sought to quantitatively dissect immune homeostasis conferring tolerance to self-antigens and responsiveness to foreign antigens. Towards this goal, I built a series of models spanning from intracellular to organismal levels to describe the recurrent reciprocal relationships between self-reactive T cells and regulatory T cells in collaboration with an experimentalist. This effort elucidated critical immune parameters regulating the circuitry enabling the robust suppression of self-reactive T cells, followed by experimental validation. Moreover, by bridging these models across organizational scales, I derived a framework describing immune homeostasis as a dynamical equilibrium between self-activated T cells and regulatory T cells, typically operating well below thresholds that could result in clonal expansion and subsequent autoimmune diseases. I start with an introduction with a perspective linking seemingly contradictory behaviors of the immune system at different scales: microscopic “noise” and macroscopic deterministic outcomes. By connecting these aspects in the adaptive immune system analogously with an ansatz from statistical physics, I introduced a view on how robust immune homeostasis ensues.