Benjamin Afflerbach, “An Introduction to Machine Learning for Materials Science: A Basic Workflow for Predicting Materials Properties“, nanoHUB online presentation on the workshop “Hands-on Data Science and Machine Learning Training Series


Dane Morgan, “What Should I Learn During My PhD?“, MSE 900 Virtual Seminar due to COVID-19, Madison, WI, USA, April 28, 2020.


Dane Morgan, “Some uses of machine learning in materials science“, Computing in Engineering Forum 2018, Machine Ground Interaction Consortium (MaGIC) 2018, Madison, WI, USA, December 4, 2018.

Morgan uw maGIV v1.3 dist from ddm314


Dane Morgan, Henry Wu, Tam Mayeshiba, “High-throughput computing for materials databases and materials design“, Open Science Grid User School, Univ. of Wisconsin – Madison, WI, USA, July 29, 2016.

Ryan Jacobs, John Booske, Dane Morgan, “Doped strontium vanadate computational design of a stable low work function material“, IEEE-IVEC meeting, Monterey, California, 2016.

Doped strontium vanadate: Computational design of a stable, low work function material from Ryan Jacobs


Dane Morgan, Opportunities with the Materials Informatics Skunkworks“. This talk describes opportunities for students, companies and academics to work in a Materials Informatics Skunkworks, presently located at the University of Wisconsin – Madison. For more information please contact Dane Morgan at

Mat informatics skunkworks overview 2015 10-31 1.0
from ddm314

D. Morgan, L. Barnard, N. Cunningham, G.R. Odetter, S. Choudhury, B. Uberuaga, “Structure and Thermokinetics of Y-Ti-O Precipitaties in Nanostructured Ferritic Alloys“, TMS conference, Orlando, Florida, 2015.


D. Morgan, T. Mayeshiba, M. Gadre, Anh Ngo, “Strain Effects on Defects and Diffusion in Perovskites“, MMM, Berkeley, California, 2014.