Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Opportunities with the UW
Materials Informatics Skunkworks
Information about who we are, what we do, how to work
with us, ...
What is Materials Informatics?
Materials informatics is a field of study that
applies the tools and principles of informat...
Materials Informatics is Not New!
Mendeleev 1871
Ashby map 3
Turning Point for Materials Informatics
Data availabilityData Production Informatics Tools
-6
-5.5
-5
-4.5
-4
-3.5
-3
0 10...
The Undergraduate “Materials
Informatics Skunkworks”
We are establishing ~10-20 undergraduates working together
to provide...
Focus Area: Machine Learning for
Knowledge Discovery in Large Data Sets
Use machine learning techniques to
• Organize your...
Example
• Organize: Build a database of all the relevant factors (impurity
concentrations, processing conditions, testing ...
Example: Predicting Impurity Diffusion in
FCC Alloys
• 15 FCC hosts x 100
impurities = 1500
systems, ~15m core-
hours (~$5...
Materials Informatics Approach –
Regression and Prediction
• Assume Activation energy = F(elemental properties)
• Elementa...
Model Predictive Ability
• Leave one out
cross validation
• Predictive RMS =
0.24 eV – predicts
diffusion of new
impurity ...
Who Did the Work?
Undergraduate Informatics Team!
Benjamin Anderson
Liam Witteman
Team guru and postdoc
Henry Wu
Aren Lore...
For Researchers Interested in Working
with the Materials Informatics
Skunkworks
12
What the Informatics Skunkworks
Might Provide You
WORKFORCE
A team of talented students who are ready to work quickly with...
What You Might Provide the
Informatics Skunkworks
FINANCIAL/COURSE CREDIT SUPPORT
Internships, Co-ops, Senior design/Capst...
For Students Interested in Joining the
Materials Informatics Skunkworks
15
Joining the Materials Informatics Skunkworks
Who is welcome?
• Anyone – we focus on undergraduates but graduate students a...
Why Join Materials Informatics Skunkworks?
• Have fun doing creative, new, and impactful science!
• Learn about materials ...
Thank You for Your Attention
18
Upcoming SlideShare
Loading in …5
×

Materials informatics skunkworks overview 2015-11-18 1.1

3,965 views

Published on

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 ddmorgan@wisc.edu.

Published in: Education

Materials informatics skunkworks overview 2015-11-18 1.1

  1. 1. Opportunities with the UW Materials Informatics Skunkworks Information about who we are, what we do, how to work with us, how to join us Dane Morgan University of Wisconsin, Madison Department of Materials Science and Engineering ddmorgan@wisc.edu W: 608-265-5879 C: 608-234-2906 November 18, 2015 1
  2. 2. What is Materials Informatics? Materials informatics is a field of study that applies the tools and principles of information extraction from data (informatics) to materials science and engineering to better understand the use, selection, development, and discovery of materials. – Mining for materials information in large data sets – Applying new information technologies to enable new materials science 2
  3. 3. Materials Informatics is Not New! Mendeleev 1871 Ashby map 3
  4. 4. Turning Point for Materials Informatics Data availabilityData Production Informatics Tools -6 -5.5 -5 -4.5 -4 -3.5 -3 0 10 20 30 40 PredictedLogk*(cm/s) E above hull (meV/atom) LaBO3 YBO3 PrBO3 (Sr,Ba)BO3 4
  5. 5. The Undergraduate “Materials Informatics Skunkworks” We are establishing ~10-20 undergraduates working together to provide materials informatics research for companies • Help researchers in academia and industry develop and utilize this new field • Provide training in rapidly growing field of informatics to undergraduates to enhance employment opportunities and key workforce development • Be supported financially/academically through credits, internships, senior design/capstone projects, funded projects from industry • Be supported intellectually through group culture of teamwork and knowledge continuity (more senior train more junior members) with limited faculty involvement for advanced issues 5
  6. 6. Focus Area: Machine Learning for Knowledge Discovery in Large Data Sets Use machine learning techniques to • Organize your data by putting all relevant, cleaned input and output into one place • Understand your data by finding the most important factors controlling output values • Expand your data by interpolating and extrapolating • Optimize your data by finding correlations between input and output data to optimize desired output 6
  7. 7. Example • Organize: Build a database of all the relevant factors (impurity concentrations, processing conditions, testing conditions, …) and output performance. • Understand: Which impurities matter most. Size of impurity effects vs. other contributions. • Expand: Interpolate/extrapolate to other impurity concentrations to assess performance under conditions we have not yet explored. • Optimize: Determine impurity concentrations that lead to optimal performance. I know impurities impact my device lifetime, so … 7
  8. 8. Example: Predicting Impurity Diffusion in FCC Alloys • 15 FCC hosts x 100 impurities = 1500 systems, ~15m core- hours (~$500k to produce, ~2 years). • We have computed values for ~10% • How can we quickly (and cheaply) get to ~100% coverage? 8 M Al Ca Ni Cu Sr Rh Pd Ag Yb Ir Pt Au Pb Ac Th X 13 20 28 29 38 45 46 47 70 77 78 79 82 89 90 H 1 He 2 Li 3 Be 4 B 5 C 6 N 7 O 8 F 9 Ne 10 Na 11 Mg 12 Al 13 Si 14 P 15 S 16 Cl 17 Ar 18 K 19 Ca 20 Sc 21 Ti 22 V 23 Cr 24 Mn 25 Fe 26 Co 27 Ni 28 Cu 29 Zn 30 Ga 31 Ge 32 As 33 Se 34 Br 35 Kr 36 Rb 37 Sr 38 Y 39 N/A N/A Zr 40 Nb 41 Mo 42 N/A Tc 43 N/A N/A Ru 44 N/A N/A Rh 45 N/A N/A Pd 46 N/A Ag 47 Cd 48 In 49 Sn 50 Sb 51 Te 52 I 53 Xe 54 Cs 55 Ba 56 La 57 N/A N/A Ce 58 Pr 59 Nd 60 Pm 61 Sm 62 Eu 63 Gd 64 Tb 65 Dy 66 Ho 67 Er 68 Tm 69 Yb 70 Lu 71 Hf 72 Ta 73 W 74 Re 75 Os 76 Ir 77 Pt 78 Au 79 Hg 80 Tl 81 Pb 82 Bi 83 Po 84 At 85 Rn 86 Fr 87 Ra 88 Ac 89 Th 90 Pa 91 U 92 Np 93 Pu 94 UNPUBLISHED DATA – CONFIDENTIAL – DO NOT DISSEMINATE
  9. 9. Materials Informatics Approach – Regression and Prediction • Assume Activation energy = F(elemental properties) • Elemental properties = melting temperature, bulk modulus, electronegativity, … • F is determined using a one of many possible methods: linear regression, neural network, decision tree, kernel ridge regression, … • Fit F with calculated data, test it with cross-validation, then predict new data. M Al Ca Ni Cu Sr Rh Pd Ag Yb Ir Pt Au Pb Ac Th X 13 20 28 29 38 45 46 47 70 77 78 79 82 89 90 H 1 He 2 Li 3 Be 4 B 5 C 6 N 7 O 8 F 9 Ne 10 Na 11 Mg 12 Al 13 Si 14 P 15 S 16 Cl 17 Ar 18 K 19 Ca 20 Sc 21 Ti 22 V 23 Cr 24 Mn 25 Fe 26 Co 27 Ni 28 Cu 29 Zn 30 Ga 31 Ge 32 As 33 Se 34 Br 35 Kr 36 Rb 37 Sr 38 Y 39 N/A N/A Zr 40 Nb 41 Mo 42 N/A Tc 43 N/A N/A Ru 44 N/A N/A Rh 45 N/A N/A Pd 46 N/A Ag 47 Cd 48 In 49 Sn 50 Sb 51 Te 52 I 53 Xe 54 Cs 55 Ba 56 La 57 N/A N/A Ce 58 Pr 59 Nd 60 Pm 61 Sm 62 Eu 63 Gd 64 Tb 65 Dy 66 Ho 67 Er 68 Tm 69 Yb 70 Lu 71 Hf 72 Ta 73 W 74 Re 75 Os 76 Ir 77 Pt 78 Au 79 Hg 80 Tl 81 Pb 82 Bi 83 Po 84 At 85 Rn 86 Fr 87 Ra 88 Ac 89 Th 90 Pa 91 U 92 Np 93 Pu 94 Train F(properties) M Al Ca Ni Cu Sr Rh Pd Ag Yb Ir Pt Au Pb Ac Th X 13 20 28 29 38 45 46 47 70 77 78 79 82 89 90 H 1 He 2 Li 3 Be 4 B 5 C 6 N 7 O 8 F 9 Ne 10 Na 11 Mg 12 Al 13 Si 14 P 15 S 16 Cl 17 Ar 18 K 19 Ca 20 Sc 21 Ti 22 V 23 Cr 24 Mn 25 Fe 26 Co 27 Ni 28 Cu 29 Zn 30 Ga 31 Ge 32 As 33 Se 34 Br 35 Kr 36 Rb 37 Sr 38 Y 39 N/A N/A Zr 40 Nb 41 Mo 42 N/A Tc 43 N/A N/A Ru 44 N/A N/A Rh 45 N/A N/A Pd 46 N/A Ag 47 Cd 48 In 49 Sn 50 Sb 51 Te 52 I 53 Xe 54 Cs 55 Ba 56 La 57 N/A N/A Ce 58 Pr 59 Nd 60 Pm 61 Sm 62 Eu 63 Gd 64 Tb 65 Dy 66 Ho 67 Er 68 Tm 69 Yb 70 Lu 71 Hf 72 Ta 73 W 74 Re 75 Os 76 Ir 77 Pt 78 Au 79 Hg 80 Tl 81 Pb 82 Bi 83 Po 84 At 85 Rn 86 Fr 87 Ra 88 Ac 89 Th 90 Pa 91 U 92 Np 93 Pu 94 Y. Zeng and K. Bai, Journal of Alloys and Compounds 624, p. 201-209 (2015). 9
  10. 10. Model Predictive Ability • Leave one out cross validation • Predictive RMS = 0.24 eV – predicts diffusion of new impurity within ~10x at 1000K • Time to predict new system < 1s! 0 1 2 3 4 5 6 DFT Activation Energy [eV] 0 1 2 3 4 5 6 PredictedActivationEnergy[eV] Al Cu Ni Pd Pt Au Ca Ir Pb Leave One Out Cross Validation y = 0.9909x R 2 = 0.9312 UNPUBLISHED DATA – CONFIDENTIAL – DO NOT DISSEMINATE 10
  11. 11. Who Did the Work? Undergraduate Informatics Team! Benjamin Anderson Liam Witteman Team guru and postdoc Henry Wu Aren Lorenson Haotian Wu Zachary Jensen 11
  12. 12. For Researchers Interested in Working with the Materials Informatics Skunkworks 12
  13. 13. What the Informatics Skunkworks Might Provide You WORKFORCE A team of talented students who are ready to work quickly with your company to get the most out of your data DATA ANALYTICS Technical skills to help you organize, understand and expand data sets and utilize data to optimize materials development 13
  14. 14. What You Might Provide the Informatics Skunkworks FINANCIAL/COURSE CREDIT SUPPORT Internships, Co-ops, Senior design/Capstone projects, Research projects, Research funding or course credits SHARED DATA Data sets of materials related performance and property data that are large (> ~50), can be shared (ideally published), and are worth mining 14
  15. 15. For Students Interested in Joining the Materials Informatics Skunkworks 15
  16. 16. Joining the Materials Informatics Skunkworks Who is welcome? • Anyone – we focus on undergraduates but graduate students and postdocs are welcome. We are centered at UW Madison but would love to branch out to other schools. • Prerequisites – an excitement about the topic and a willingness to work hard on it. No informatics skilled needed. We strongly encourage those interested in at least a year, ideally longer, commitments. But we also encourage those interested in doing shorter term class and capstone projects. What would I do? • We work with you to define a data set and problem and get you the necessary tools to do the work. • You apply informatics tools to materials problems, learn from and train other students, help organize events, and join in the fun. How do I join? • Send an email to Dane Morgan at ddmorgan@wisc.edu and we can discuss how to get you involved. 16
  17. 17. Why Join Materials Informatics Skunkworks? • Have fun doing creative, new, and impactful science! • Learn about materials informatics, one of the hottest new areas in materials science. • Learn computer science methods from informatics, machine learning, data mining, knowledge discovery, etc. that are changing our world, from diagnosing cancer to self-driving cars. • Learn critical skills in teamwork and initiative by independently driving your own work as part of larger team projects. • Learn modern programming and IT tools, including python coding, GitHub and team code development, teamwork with Slack, Matlab, Excel, etc. • Generate impactful publishable undergraduate research that is critical to support applications for jobs and graduate school. • Accomplish more than you can working alone by joining a team whose DNA is sharing and teamwork for the benefit of everyone involved. • Strengthen your resume by working with an established team that is part of a world-class research group and will soon have published scientific papers, a strong online, and wide notoriety. • Engage your personal creativity – materials informatics is a transformative new field and you can help work with us to define it. 17
  18. 18. Thank You for Your Attention 18

×