This talk describes opportunities for companies and academics to work with undergraduates at University of Wisconsin to engage in the exciting new field of Materials Informatics. It was originally given in slightly altered form at the UW AMIC meeting on 2015-09-11.

1. Company Opportunities with the
Materials Informatics Skunkworks
Dane Morgan
University of Wisconsin, Madison
Department of Materials Science and Engineering
ddmorgan@wisc.edu
W: 608-265-5879
C: 608-234-2906
April 20, 2015
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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
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3. Materials Informatics is Not New!
Mendeleev 1871
Ashby map 3

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
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5. Plan: An Undergraduate “Materials
Informatics Skunkworks”
Establish ~10 undergraduates working together to
provide materials informatics research for companies
• Help companies 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 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
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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
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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 …
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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?
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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. 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).
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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
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11. Who Did the Work?
Undergraduate Informatics Team!
Benjamin Anderson
Liam Witteman
Team guru and postdoc
Henry Wu
Aren Lorenson
Haotian Wu
Zachary Jensen
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12. What the Informatics Skunkworks
Might Provide Companies
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
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13. What Companies Might Provide the
Informatics Skunkworks
FINANCIAL SUPPORT
Internships, Senior design/Capstone projects, Research projects
with Faculty+Skunkworks Teams
SHARED DATA
Data sets of materials related performance and property data that
are large (> ~50), can be shared (ideally published), and are worth
mining
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14. Thank You for Your Attention
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