Advancing the capability of high energy Yb:YAG lasers: multilayer coatings, pulse shaping and post compression

2022 Summer.Includes bibliographical references.Recently, cryogenically cooled Yb-doped amplifiers have been successfully scaled both in output energy and in repetition rate. The performance of such laser systems and their applications can be expanded by advancements in the development of optical coatings, that allow for scaling to higher pulse energies; as well as improvements in pulse shaping that include shorter pulse durations and the generation of programmable sequences of ultrashort pulses. This dissertation focuses on realizing the improvements mentioned above for cryogenic Yb:YAG amplifiers. First it reports the evaluation of ion beam sputtering (IBS) dielectric coatings for Yb:YAG at the environmental conditions in which cryogenic amplifiers are operated. The IBS coatings showed consistent performance in ambient, vacuum and cryogenic conditions, with damage threshold measured 20.4±0.6 J/cm2 for anti-reflection (AR) coating, and 27.4±1.3 J/cm2 for high reflector (HR) coating with 280 ps pulse duration at 77 K under the ISO:21254 standard. Second, a method for synthesizing trains of high energy compressed pulses was demonstrated and used to pump an 18.9 nm Ni-like Mo plasma-based soft x-ray laser more efficiently. The synthesized pulse increased the conversion efficiency of this spatially coherent soft x-ray source by 40%. Finally, femtosecond pulses were generated by post compression using a gas filled hollow core fiber (HCF), in which spectral broadening was achieved by self-phase modulation with an additional contribution from stimulated Raman scattering. Utilizing nitrogen gas as the non-liner medium, 300 mJ, 8 ps pulses were broadened to 3.7 nm and re-compressed to 460 fs by a grating compressor. The propagation and spectral broadening of high energy picosecond pulses in gas-filled HCFs were modeled and the results of simulations were compared with experiments

Designing Machine Learning Models for Graph Analytics

University of Technology Sydney. Faculty of Engineering and Information Technology.With growing popularity of the machine learning methods, there have been a great number of machine learning methods proposed for graph analytics. In this thesis, we design three machine learning based models for the popular graph analysis tasks such as node classification, graph interaction prediction and subgraph matching. Firstly, we design a binarized graph neural network to efficiently obtain the vector representations for vertices and graphs. Recently, there have been some breakthroughs in graph analysis by applying the Graph Neural Networks (GNNs). However, the parameters of the network and the embedding of nodes are represented in real-valued matrices in existing GNN-based approaches which may limit the efficiency and scalability of these models. This motivates us to develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters following the GNN-based paradigm. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches to binarize the model parameters and learn the compact embedding. Secondly, we design a graph of graphs neural network for entity interaction prediction, and then extend the model to support the graph classification task with more expressive representations. Entity interaction prediction is essential in many important applications, which can be quite challenging when there are two types of graphs are involved: local graphs for structured entities and a global graph for the interactions between structured entities. We observe that existing works cannot properly exploit the unique graph of graphs structure. In this thesis, we propose a Graph of Graphs Neural Network, namely GoGNN, which extracts the features of the given graph in a hierarchical way. Based on GoGNN, we further propose a Powerful Graph Of graphs neural Network, namely PGON, which has 3-Weisfeiler-Lehman expressive power and can be used to handle the graph classification task. Thirdly, we design a reinforcement learning based query vertex ordering model for subgraph matching. Subgraph matching is a fundamental problem in graph analytics. Instead generating the matching order with heuristics, our model could capture and make full use of the graph information, and thus determine the query vertex order with the adaptive learning-based rule that could significantly reduce the number of redundant enumerations. With the help of the reinforcement learning framework, our model could consider the long-term benefits during order generation. Extensive experiments on real-life datasets indicate the efficiency and effectiveness of our proposed models in the corresponding graph analytic tasks

Clustering on Magnesium Surfaces – Formation and Diffusion Energies

The formation and diffusion energies of atomic clusters on Mg surfaces determine the surface roughness and formation of faulted structure, which in turn affect the mechanical deformation of Mg. This paper reports first principles density function theory (DFT) based quantum mechanics calculation results of atomic clustering on the low energy surfaces {0001} and {1011}. In parallel, molecular statics calculations serve to test the validity of two interatomic potentials and to extend the scope of the DFT studies. On a {0001} surface, a compact cluster consisting of few than three atoms energetically prefers a facecentered- cubic stacking, to serve as a nucleus of stacking fault. On a {1011}, clusters of any size always prefer hexagonal-close-packed stacking. Adatom diffusion on surface {1011} is high anisotropic while isotropic on surface (0001). Three-dimensional Ehrlich–Schwoebel barriers converge as the step height is three atomic layers or thicker. Adatom diffusion along steps is via hopping mechanism, and that down steps is via exchange mechanism

Micro-seismic Elastic Reflection Full Waveform Inversion with An Equivalent Source

In micro-seismic event measurements, pinpointing the passive source's exact spatial and temporal location is paramount. This research advocates for the combined use of both P- and S-wave data, captured by geophone monitoring systems, to improve source inversion accuracy. Drawing inspiration from the secondary source concept in Elastic Reflection Full Waveform Inversion (ERFWI), we introduce an equivalent source term. This term combines source functions and source images. Our optimization strategy iteratively refines the spatial locations of the source, its temporal functions, and associated velocities using a full waveform inversion framework. Under the premise of an isotropic medium with consistent density, the source is defined by two spatial and three temporal components. This offers a nuanced source representation in contrast to the conventional seismic moment tensor. To address gradient computation, we employ the adjoint-state method. However, we encountered pronounced non-linearity in waveform inversion of micro-seismic events, primarily due to the unknown source origin time, resulting in cycle skipping challenges. To counteract this, we devised an objective function that is decoupled from the source origin time. This function is formulated by convolving reference traces with both observed and predicted data. Through the concurrent inversion of the source image, source time function, and velocity model, our method offers precise estimations of these parameters, as validated by a synthetic 2D example based on a modified Marmousi model. This nested inversion approach promises enhanced accuracy in determining the source image, time function, and velocity model

Anomaly of Film Porosity Dependence on Deposition Rate

This Letter reports an anomaly of film porosity dependence on deposition rate during physical vapor deposition - the porosity increases as deposition rate decreases. Using glancing angle deposition of Cu on SiO2 substrate, the authors show that the Cu film consists of well separated nanorods when the deposition rate is 1 nm/second, and that the Cu films consists of a more uniform (or lower porosity) film when the deposition rate is 6 nm/second; all other deposition conditions remain the same. This anomaly is the result of interplay among substrate non-wetting, density of Cu nuclei on the substrate, and the minimum diameter of nanorods

GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions

Entity interaction prediction is essential in many important applications such as chemistry, biology, material science, and medical science. The problem becomes quite challenging when each entity is represented by a complex structure, namely structured entity, because two types of graphs are involved: local graphs for structured entities and a global graph to capture the interactions between structured entities. We observe that existing works on structured entity interaction prediction cannot properly exploit the unique graph of graphs model. In this paper, we propose a Graph of Graphs Neural Network, namely GoGNN, which extracts the features in both structured entity graphs and the entity interaction graph in a hierarchical way. We also propose the dual-attention mechanism that enables the model to preserve the neighbor importance in both levels of graphs. Extensive experiments on real-world datasets show that GoGNN outperforms the state-of-the-art methods on two representative structured entity interaction prediction tasks: chemical-chemical interaction prediction and drug-drug interaction prediction. Our code is available at Github.Comment: Accepted by IJCAI 202

\textsc{DeFault}: Deep-learning-based Fault Delineation

The carbon capture, utilization, and storage (CCUS) framework is an essential component in reducing greenhouse gas emissions, with its success hinging on the comprehensive knowledge of subsurface geology and geomechanics. Passive seismic event relocation and fault detection serve as indispensable tools, offering vital insights into subsurface structures and fluid migration pathways. Accurate identification and localization of seismic events, however, face significant challenges, including the necessity for high-quality seismic data and advanced computational methods. To address these challenges, we introduce a novel deep learning method, DeFault, specifically designed for passive seismic source relocation and fault delineating for passive seismic monitoring projects. By leveraging data domain-adaptation, DeFault allows us to train a neural network with labeled synthetic data and apply it directly to field data. Using DeFault, the passive seismic sources are automatically clustered based on their recording time and spatial locations, and subsequently, faults and fractures are delineated accordingly. We demonstrate the efficacy of DeFault on a field case study involving CO2 injection related microseismic data from the Decatur, Illinois area. Our approach accurately and efficiently relocated passive seismic events, identified faults and aided in the prevention of potential geological hazards. Our results highlight the potential of DeFault as a valuable tool for passive seismic monitoring, emphasizing its role in ensuring CCUS project safety. This research bolsters the understanding of subsurface characterization in CCUS, illustrating machine learning's capacity to refine these methods. Ultimately, our work bear significant implications for CCUS technology deployment, an essential strategy in combating climate change

Evaluating Self-Supervised Learning for Molecular Graph Embeddings

Graph Self-Supervised Learning (GSSL) provides a robust pathway for acquiring embeddings without expert labelling, a capability that carries profound implications for molecular graphs due to the staggering number of potential molecules and the high cost of obtaining labels. However, GSSL methods are designed not for optimisation within a specific domain but rather for transferability across a variety of downstream tasks. This broad applicability complicates their evaluation. Addressing this challenge, we present "Molecular Graph Representation Evaluation" (MOLGRAPHEVAL), generating detailed profiles of molecular graph embeddings with interpretable and diversified attributes. MOLGRAPHEVAL offers a suite of probing tasks grouped into three categories: (i) generic graph, (ii) molecular substructure, and (iii) embedding space properties. By leveraging MOLGRAPHEVAL to benchmark existing GSSL methods against both current downstream datasets and our suite of tasks, we uncover significant inconsistencies between inferences drawn solely from existing datasets and those derived from more nuanced probing. These findings suggest that current evaluation methodologies fail to capture the entirety of the landscape.Comment: update result