2026-09-22T00:45:09Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/121398 2023-12-13 col_2142_5131 col_2142_8859 com_2142_5130 com_2142_8858 com_2142_234

Wagner, Lucas K. Ceperley, David Mahmood, Fahad Lorenz, Virginia 2023-08 application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms The student, Alexander Munoz, accepted the attached license on 2023-05-11 at 13:29. The student, Alexander Munoz, submitted this Dissertation for approval on 2023-05-11 at 13:38. This Dissertation was approved for publication on 2023-05-19 at 13:03. DSpace SAF Submission Ingestion Package generated from Vireo submission #19373 on 2023-12-04 at 16:59:38 Data-driven approaches from ab initio methods in condensed matter to climate science Munoz, Alexander Reed 2023-05-19 Physics Condensed Matter Machine Learning Climate Science Paleoclimate First–principles calculations represent a collection of methods for predicting the properties of quantum mechanical systems with many particles. First–principles approaches to condensed matter are embodied by the development of computational techniques driven by physical knowledge. As such, this dissertation covers ab initio computation, the use of machine learning techniques in climate science, and the use of machine learning for the extension of ab initio computation. Each chapter demonstrates the utility of computation in solving physical problems, and how physical feedback informs the design of computational protocols. The direct application of ab initio techniques to many–body systems is demonstrated by my work on the prediction of elastic neutron scattering experiments. I designed a machine learning workflow for climate science that creates a global isotopic dataset, identifies regions with distinct oceanographic and atmospheric processes, and measures the importance of the processes that determine the isotope’s relationship to water salinity. My ongoing work has been engineering data sets and designing equivariant neural network architectures for the application of machine learning to ab initio electron densities. My work has been focused on the use of physical insights to improve computational models for condensed matter and climate science. The physically informed approach to machine learning accelerates the solution of physical problems and can offer insights into the interpretation of physical systems. Text eng https://hdl.handle.net/2142/121398 Copyright 2023 Alexander Munoz Ph.D. Dissertation Physics University of Illinois at Urbana-Champaign Physics