Q&A Information Session

Dane Morgan

University of Wisconsin, Madison

ddmorgan@wisc.edu, W: 608-265-5879, C: 608-234-2906

UW Madison

ECB 1025, September 8, 2017 1

To Join: Send me email at ddmorgan@wisc.edu 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)

2

3

Artificial

Intelligence

Science, Engineering,

+ Technology

Perhaps the

greatest tool in

human history

Perhaps the most

important activity in

human history

+

What is the Informatics Skunkworks?

The “Informatics Skunkworks” is a group dedicated to realizing the potential of

informatics for science and engineering.

4

Vision: Transform science and

engineering with informatics

Why Form the InformationsSkunkworks?

Incredible opportunity for young creative researchers

5

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.

6

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.

7

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

8

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.

9

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

10

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/

10-18

10-16

10-14

10-12

10-10

10-8

10-6

10-4

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

Conclusions

Informatics is a transformative technology for nearly everything – come join us!

Some experienced skunkworkers to talk to

12

Benjamic Afflerbach Vanessa Meschke