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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Machine Learning To Discover And Optimize Materials, Conrad Waldhar Rosenbrock
Machine Learning To Discover And Optimize Materials, Conrad Waldhar Rosenbrock
Theses and Dissertations
For centuries, scientists have dreamed of creating materials by design. Rather than discovery by accident, bespoke materials could be tailored to fulfill specific technological needs. Quantum theory and computational methods are essentially equal to the task, and computational power is the new bottleneck. Machine learning has the potential to solve that problem by approximating material behavior at multiple length scales. A full end-to-end solution must allow us to approximate the quantum mechanics, microstructure and engineering tasks well enough to be predictive in the real world. In this dissertation, I present algorithms and methodology to address some of these problems at …
Machine Learning With Scattering Transforms, Jacob Hansen, Gus Hart
Machine Learning With Scattering Transforms, Jacob Hansen, Gus Hart
Journal of Undergraduate Research
Our goal was to implement scattering transforms as a mathematical representation of materials. The intention of this project was to build intuition on this technique using model data in one and two dimensions. The tools created here will be used as templates in further projects on real materials data. The intuition built during this project is crucial to the machine learning framework for materials design that we hope to build in the near future.
The Ogcleaner: Detecting False-Positive Sequence Homology, Masaki Stanley Fujimoto
The Ogcleaner: Detecting False-Positive Sequence Homology, Masaki Stanley Fujimoto
Theses and Dissertations
Within bioinformatics, phylogenetics is the study of the evolutionary relationships between different species and organisms. The genetic revolution has caused an explosion in the amount of raw genomic information that is available to scientists for study. While there has been an explosion in available data, analysis methods have lagged behind. A key task in phylogenetics is identifying homology clusters. Current methods rely on using heuristics based on pairwise sequence comparison to identify homology clusters. We propose the Orthology Group Cleaner (the OGCleaner) as a method to evaluate cluster level verification of putative homology clusters in order to create higher quality …