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Full-Text Articles in Physical Sciences and Mathematics
A Surrogate Model Of Molecular Dynamics Simulations For Polar Fluids: Supervised Learning Methods For Molecular Polarization And Unsupervised Methods For Phase Classification, Zackerie W. Hjorth
A Surrogate Model Of Molecular Dynamics Simulations For Polar Fluids: Supervised Learning Methods For Molecular Polarization And Unsupervised Methods For Phase Classification, Zackerie W. Hjorth
Dissertations, Master's Theses and Master's Reports
Molecular Dynamic (MD) simulation is a standard computational tool in soft matter physics. While very powerful, it is computationally expensive, leading to some simulations taking days or even weeks to complete depending on the size of your computer cluster. Finding computationally cheap surrogate models which can learn the output features of MD simulation is therefore highly motivated. In this report I explore the use of deep neural network ensembles as well as support vector machine regressors as surrogate models for MD simulation. From the output of the surrogate models, we can then employ unsupervised learning methods to get insight into …
Machine Learning-Driven Surrogate Models For Electrolytes, Tong Gao
Machine Learning-Driven Surrogate Models For Electrolytes, Tong Gao
Dissertations, Master's Theses and Master's Reports
We have developed a lattice Monte Carlo (MC) simulation based on the diffusion-limited aggregation model that accounts for the effect of the physical properties of ionic liquids (ILs) on lithium dendrite growth. Our simulations show that the size asymmetry between the cation and anion, the dielectric constant, and the volume fraction of ILs are critical factors to significantly suppress the dendrite growth, primarily due to substantial changes in electric-field screening. Specifically, the volume fraction of ILs has the optimal value for dendrite suppression. The present simulation method indicates potential challenges for the model extension to macroscopic systems. Therefore, we also …
Fine Scale Mapping Of Laurentian Mixed Forest Natural Habitat Communities Using Multispectral Naip And Uav Datasets Combined With Machine Learning Methods, Parth P. Bhatt
Dissertations, Master's Theses and Master's Reports
Natural habitat communities are an important element of any forest ecosystem. Mapping and monitoring Laurentian Mixed Forest natural communities using high spatial resolution imagery is vital for management and conservation purposes. This study developed integrated spatial, spectral and Machine Learning (ML) approaches for mapping complex vegetation communities. The study utilized ultra-high and high spatial resolution National Agriculture Imagery Program (NAIP) and Unmanned Aerial Vehicle (UAV) datasets, and Digital Elevation Model (DEM). Complex natural vegetation community habitats in the Laurentian Mixed Forest of the Upper Midwest. A detailed workflow is presented to effectively process UAV imageries in a dense forest environment …