2017-09-08 skunkworks q&a information session v1.0 distr
Transcript of 2017-09-08 skunkworks q&a information session v1.0 distr
Q&A Information Session
Dane Morgan
University of Wisconsin, Madison
[email protected], W: 608-265-5879, C: 608-234-2906
UW Madison
ECB 1025, September 8, 2017 1
To Join: Send me email at [email protected] with your name, email, major (intended if not set), and any relevant facts/interests (e.g., have project already, strong machine learning skills, know python, want only solar energy, …)
What do These Have in Common?
• Chess• Jeopardy• Go• Language translation• MRI based diagnosis• Driving • “most likely cause of WW3” (Elon Musk, Twitter,
9/4/17)• “leader in this sphere will become the ruler of the
world” (Vladimir Putin, "science lesson" to start off the Russian school year, 9/4/17)
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Artificial
Intelligence
Science, Engineering,
+ Technology
Perhaps the
greatest tool in
human history
Perhaps the most
important activity in
human history
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What is the Informatics Skunkworks?
The “Informatics Skunkworks” is a group dedicated to realizing the potential of
informatics for science and engineering.
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Vision: Transform science and
engineering with informatics
Why Form the InformationsSkunkworks?
Incredible opportunity for young creative researchers
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Massive Data New FieldsTransformative Tools
How the Informatics SkunkworksWorks – Big Picture
• You talk to me if you are interested.
• We find you a project with a mentor (me, another faculty, industry representative) –you can bring a project.
• You work on the project for either credit (most common) or pay (if available) and get cool results.
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How the Informatics SkunkworksWorks – Details
• Typical commitment is ~10h/wkduring the year (3 credits), possibly full time over summer if adequate funds and interest.
• Participants should plan to spend 2-3h/wk in lab at designated “gathering” times.
• Participants should plan to meet and present progress to a mentor at least every 2 weeks.
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Why Join the Skunkworks vs. Just Work Separately?
• Community building: You can find a like-minded community of colleagues from which to learn and form a network for a lifetime.
• Technical resources: Have people to ask questions and have access to our computational (codes and computers) resources.
• Presentation opportunities: Utilize frequent opportunities to present work on web page, as posters and/or talks, potentially publish papers.
• Learn teamwork: We tend to work in teams to help build critical teamwork skills for future employment.
• Snack food: Our lab is well stocked
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Some Stuff the Skunkworks Has/Does
• Large lab with lot’s of snacks (thanks to Profs Rebecca Willet and Robert Nowak) – EH 3546
• Excellent web page to highlight our accomplishments (skunkworks.wisc.edu)– Always looking for people to help
develop this
• Experienced members who know powerful informatics tools (python, matlab, SciKitLearn, tensorflow, Citrine/Lolo, MASTML, etc.)
• Neat data sets you can explore (mostly in materials)
• Many opportunities for posters, talks, papers, etc.
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Some Recent Skunkworks Accomplishments
• Highlighted as one of the 32 accomplishments of the first five years of the $500m Materials Genome Initiative
• Finalist in the 2017 Wisconsin Innovation Awards
• First paper - H. Wu, et al., Computational Materials Science, 2017
• High-profile fellowships (Vanessa Meschke won Citrine, LLC NextGenfellowship and Best Capstone Project award – free powerbook!)
• Dozens of presentations at conferences
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Example: Machine Learning for Impurity Diffusion
• Machine learning provides enormous opportunities for materials, including generating new data from pattern, finding correlations, ’reading’ papers, …
• Machine-learning models trained on with high-throughput calculated data can extend it by orders of magnitude
• We have extended our diffusion data by ~5x with machine learning model, saving years and ~$1m
http://diffusiondata.materialshub.org/
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Dif
fusi
vit
y a
t 100
0K
[cm
2 /
s]
Sc YLa
Ti ZrHf
V NbTa
Cr MoW
Mn TcRe
Fe RuOs
Co RhIr
Ni PdPt
Cu AgAu
Zn CdHg
Ga InTl
Ge SnPb
As SbBi
1.0
1.5
2.0
2.5
3.0
Dif
fusi
on
Bar
rier
[eV
]
Y Zr Nb Mo Tc Ru Rh Pd Ag Cd In Sn Sb
Actual BarriersLRDTANNGKRR
Al
Al
0.8
1.2
1.6
2.0
Solu
te D
iffu
sio
n B
arri
er [
eV]
Sc YLa
Ti ZrHf
V NbTa
Cr MoW
Mn TcRe
Fe RuOs
Co RhIr
Ni PdPt
Cu AgAu
Zn CdHg
Ga InTl
Ge SnPb
As SbBi
Ca SrBa
K RbCs
Pb - GKRR
H. Wu, et al., Comp. Mat. Sci ’17