A5025493416- Metallic Glasses and Amorphous Alloys
- Electron and X-Ray Spectroscopy Techniques
- Computational Physics and Python Applications
- Advanced Electron Microscopy Techniques and Applications
- Pigment Synthesis and Properties
- Phase-change materials and chalcogenides
- Cultural Heritage Materials Analysis
- X-ray Diffraction in Crystallography
- Metallurgical Processes and Thermodynamics
- Magnetic properties of thin films
- Glass properties and applications
- Ion-surface interactions and analysis
- Electronic and Structural Properties of Oxides
- Crystallography and molecular interactions
- Force Microscopy Techniques and Applications
- Crystal Structures and Properties
- Chemical and Physical Properties of Materials
- Surface and Thin Film Phenomena
- Spectroscopy and Quantum Chemical Studies
- X-ray Spectroscopy and Fluorescence Analysis
- Crystallography and Radiation Phenomena
- Material Dynamics and Properties
- Theoretical and Computational Physics
- Crystallization and Solubility Studies
- Additive Manufacturing and 3D Printing Technologies
University of Wisconsin–Madison
2020-2024
Iowa State University
2019
We use a random forest (RF) model to predict the critical cooling rate (RC) for glass formation of various alloys from features their constituent elements. The RF was trained on database that integrates multiple sources direct and indirect RC data metallic glasses expand directly measured less than 100 values training set over 2000 values. error 5-fold cross-validation (CV) is 0.66 orders magnitude in K/s. leave-out-one-group CV alloy system groups 0.59 log units K/s when target constituents...
${\mathrm{Zr}}_{65}{\mathrm{Cu}}_{27.5}{\mathrm{Al}}_{7.5}$ metallic glass thin films with widely varying kinetic stability as a function of deposition rate were synthesized by single-target direct current magnetron sputtering. Fluctuation electron microscopy and angular correlations in coherent nanodiffraction show that glasses increased have nanoscale structural order, particularly icosahedral character. The most kinetically stable film's reduced modulus was 22% higher than the same...
Journal Article Infrastructure for Analysis of Large Microscopy and Microanalysis Data Sets Get access Jingrui Wei, Wei Department Materials Science Engineering, University Wisconsin-Madison, Madison, WI, United States Search other works by this author on: Oxford Academic Google Scholar Carter Francis, Francis Dane Morgan, Morgan KJ Schmidt, Schmidt Chicago, Globus / Argonne National Laboratory – Learning Division Aristana Scourtas, Scourtas Ian Foster, Foster Ben Blaiszik, Blaiszik Paul M...
Most electron microscopists have seen the characteristic fuzzy rings of large-area diffraction from an amorphous material at one time or another, if only as a result failure sample preparation for crystalline sample.This kind scattering can be normalized and Fourier inverted to yield pair distribution function, g(r), that measures mean number atoms sit distance r away average atom in material.g(r) is foundational data studying structure materials, but it not reminiscent symmetry-based...
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Journal Article pyxem: A Scalable Mature Python Package for Analyzing 4-D STEM Data Get access Carter Francis, Francis University of Wisconsin Madison, Materials Science and Engineering, WI, United States Search other works by this author on: Oxford Academic Google Scholar Paul M Voyles Corresponding author: paul.voyles@wisc.edu Microscopy Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Pages 685–686, https://doi.org/10.1093/micmic/ozad067.338 Published: 22 July 2023
Journal Article Segmentation of “Important” Features in High Dimensional Nanodiffraction Datasets Get access Carter Francis, Francis Department Materials Science and Engineering, University Wisconsin-Madison, Madison, Wi, US Search for other works by this author on: Oxford Academic Google Scholar Paul Voyles Corresponding author: paul.voyles@wisc.edu Microscopy Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 410–412, https://doi.org/10.1017/S1431927622002355 Published: 01 2022
An abstract is not available for this content so a preview has been provided. As you have access to content, full PDF via the ‘Save PDF’ action button.
Various combinations of characteristic temperatures, such as the glass transition temperature, liquidus and crystallization have been proposed predictions forming ability metal alloys. We used statistical approaches from machine learning to systematically explore a wide range possible temperature functions for predicting in form critical casting diameter, $D_{max}$. Both linear non-linear models were learn on largest database $D_{max}$ values date consisting 747 compositions. find that no...