Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 59 for “"property prediction"”.
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Raman Chemometric Method for Fuel Property Prediction
… that spans the functional-group landscape and property ranges of standard jet fuels. The set includes neat hydrocarbons, laboratory mixtures, Army Research Laboratory CN surrogates, actual jet fuels, and fuel blends. Raman spectra were preprocessed with Savitzky-Golay second-derivative …
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Message passing neural networks for molecular property prediction
… learning models are optimal for molecular property prediction. In this thesis, I apply the Direct Message Passing Neural Network (D-MPNN) from [47, 48] to 19 publicly available property prediction datasets, and I demonstrate that it consistently outperforms prior machine learning models. …
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Genomic Language Models for Protein Function and Property Prediction
… model (pLM) performance on protein function and property prediction. We show that gLMs are competitive and even outperform their pLMs counterparts on some tasks and that they perform best using the curated true coding sequences over alternative codon sampling strategies. We perform a series of …
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Development of Property Prediction Methods for Sodium-ion Battery Electrolytes
In this project, we model the interaction between the sodium ion and the organic solvents, which are widely used in sodium ion batteries (SIBs). The research mainly focuses on how a sodium cation coordinates with the solvent molecules and how its coordination affects the resulting complex …
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Learning the Language of Antibody Hypervariability Through Biological Property Prediction
… in a variety of structure and function-prediction contexts. However, foundational PLMs (those trained on the corpus of all proteins) rely on evolutionary co-conservation of protein sub-sequences, but this distributional hypothesis does not hold for antibody hypervariable regions. …
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Thermodynamic Property Prediction for Solid Organic Compounds Based on Molecular Structure
… for the design of all process units. Reliable property prediction methods are essential because reliable experimental data are often not available due to concerns about measurement difficulty, cost, scarcity, safety, or environment. In particular, there is a lack of prediction methods for solid …
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Geometric representation learning for chemical property prediction, structure elucidation, and molecular design
… to create new opportunities in chemical property prediction, structure elucidation, and molecular design. This thesis begins by highlighting surprising failure modes of graph neural networks when predicting properties dependent on chirality and conformational isomerism. A new …
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Data Analytics and Machine Learning Applications in Fermentation Processes and Molecular Property Prediction
… a crucial role in bioprocesses and molecular property prediction. Our study encompasses three main aspects: 1) using data analytics to analyze the occurrence of foaming in batch fermentation processes using multiway partial least square (MPLS) approaches; 2) using hyperparameter optimization …
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Optical Property Prediction and Molecular Discovery through Multi-Fidelity Deep Learning and Computational Chemistry
… and biological imaging. The accurate prediction of these properties has been the subject of decades of work in both physics-based approaches and statistical modeling. Recently, large datasets of both computed and experimental optical properties have become available, along with the …
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Advancing Chemical Hazard and Risk Assessments: Insights From Chemical Property Prediction and Multimedia Mass-Balance Modeling
Evaluating the hazards and risks associated with the vast array of chemical substances on the market is essential for protecting human health and environmental integrity. Chemical properties serve as critical determinants of a chemical's potential for hazard, exposure, and risk. By evaluating both …
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Deep Learning and Phase Retrieval for Chemical Holographic Imaging: System Inverse Modeling and Sample Property Prediction
… by CHIS through the significantly improved prediction accuracy. The hardware implementation of CHIS, as well as different modules of the implementation, are individually illustrated in detail. The reconstruction process of CHIS is verified using a forward model to demonstrate CHIS's …
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A Systematic Framework for Feature Analysis, Selection, and Property Prediction of Chemical Materials Using Machine Learning
… informatics. Its utility is demonstrated via the prediction of various material properties across different research domains, including solid-state physics, condensed matter physics, materials science and engineering, as well as spectroscopic characterisation. The overarching objective is to …
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Implementation of Multivariate Artificial Neural Networks Coupled with Genetic Algorithms for the Multi-Objective Property Prediction and Optimization of Emulsion Polymers
… These trends can then be used to make predictions on new data and explore entirely new design spaces. Methods vary from simple linear regression to highly complex neural networks, but the end goal is similar. The application of these methods to material property prediction and new …
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New Computational Methodologies for Microstructure Quantification
… and data-driven methods for material property prediction for metallic microstructures while indicating the context and benefit for microstructure- sensitive design. From this, the use of shape moment invariants is offered as solution to quantifying microstructure topology numerically …
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AI for materials design: Generative AI with multi-fidelity strategies
… and the high cost of obtaining high-fidelity property labels through quantum or physics-based simulations. This dissertation introduces a unified framework that combines generative artificial intelligence (AI), hierarchical transfer learning, multi-fidelity modeling, and graph-driven …
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Machine Learning for Structural Characterization and Generation: Applications to Small-Angle Scattering and Electron Microscopy
… resources present opportunities for functional-property prediction to reveal novel uses for existing structures and for deep generative models to design new structures for a wide range of applications. This thesis is concerned with the development of machine learning (ML) algorithms for the …
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Investigations into Message Passing Neural Networks and Polymer Fouling
… investigations of high-fidelity molecular property prediction with Message Passing Neural Networks (MPNNs) and nanoscale polymer fouling experimentation with Quartz Crystal Microbalances (QCMs). Message Passing Neural Networks are promising deep learning architectures for chemical property …
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Generating rationale for molecular prediction using reinforcement learning
… studies generation of rationale for neural prediction problems using reinforcement learning. In particular, we focus on neural predictions in chemical property prediction tasks. We design a reinforcement learning agent that learns to incrementally extract the important regions of molecular …
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Machine Learning Methods for Discovering Metabolite Structures from Mass Spectra
… deep learning including (A) molecular formula prediction, (B) spectrum-to-molecule property prediction, (C) molecule-to-spectrum prediction, and (D) de novo generation of molecular candidates. To address these various tasks, I first introduce the Molecular Formula Transformer to predict …
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