Zach and Aren talk on Materials Informatics at UW WIMSEA 2016-02-12
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Informatics Skunkworks - Machine Learning & Materials Science Applications Zach Jensen Aren Lorenson Presented at UW Madison, WIMSEA 2016-02-12
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Transcript of Zach and Aren talk on Materials Informatics at UW WIMSEA 2016-02-12
Informatics Skunkworks - Machine Learning & Materials
Science Applications
Zach JensenAren Lorenson
Presented at UW Madison, WIMSEA2016-02-12
What we doWe’re 15 interdisciplinary undergrads practicing data science
engineers + programmers w/ computer science backgrounds
“applying the tools and principles of information extraction (informatics) to materials science and engineering to better understand the use, selection, development, and discovery of materials”
Look through large or complex datasets w/ computational assistancework in dimensions or time scales humans don’t handle well
organize, understand, expand data
find relevant inputs, put them in one place, and learn what they can tell us
optimize outcomes
UtilityInter/extrapolation and correlation
fill in what’s missing or make predictions
data de-noising
Experiment design find optimal paths w/ search algorithms
Image recognitionread papers and browse the web for you
automated data collection from micrographs, thermography, etc.
More tools and data captured than ever before = opportunity
Materials informatics - A strong history
Periodic table1871
Ashby map1983
2 ExamplesZach Jensen
Glass forming properties of aluminum alloy systems.
Aren LorensonEnergy barriers to vacancy diffusion in binary metallic systems
Glass forming properties of Al alloy systemsMotivation
Bulk metallic glass has been an area of active research for decades.
Very attractive structural properties including high strength, corrosion resistance, and biocompatibility.
Prediction of glass forming properties could lead to discovery of stable, mass-producible bulk metallic glass.
Problem:Can glass forming properties of aluminum alloys be predicted from the alloy
composition?
Which alloying elements have the greatest potential to aid in glass formation?
What compositions give the highest primary aluminum crystallization temperature?
Can we expand the model to include other glass forming properties ie. liquidus temperature, intermetallic crystallization?
Amorphous - ProcessWe have a large collection of
amorphous alloy data from literature.
Includes primary aluminum crystallization temperatures for various alloy composition.
Alloying elements include Fe, Y, Ni, La, Co, Cu, Ti, ….
We train the model using linear regression.
We test the predictive ability using a cross validation test.
Create model for most of the data.
Predict the Tx for the left out data.
Run test many times analyzing the RMS value to determine predictive accuracy.
Predict new compositions with high Tx values.
Tx = constant + ∑ Ci*Xi
Amorphous - ResultsElement Coefficient
(weight)
Fe 40 ± 2.9
Ti 33 ± 7.2
La 31 ± 2.5
Co 30 ± 3.1
Ce 28 ± 2.8
Y 25 ± 1.8
Ni 20 ± 1.8
Cu 3.5 ± 1.8
● Linear regression models are able to successfully predict Tx for most samples.
● 20% out cross validation tests result in an RMS of 22 ± 11 °C○ Standard deviation of experimental Tx in the data set is 50 °C.
● Coefficient magnitude help determine the relative importance of each element on Tx.
Aren - Energy barriers to vacancy diffusionMotivation: diffusion is everywhere!
microstructural and mechanical evolution of metals
compound semiconductor doping and interfaces
Problem: for a metallic impurity vacancy X in FCC metallic host Y…what is the energy barrier (ΔE) to X diffusing in Y?
what about X and Y determine the energy barrier?
can we generalize for broader problems?
other lattices?
non metals?
Aren - ProcessWe need prior beliefs to base a model on
210 ‘feature vectors’ fa(x1, x2 … xn) = ∆Ea
some xi: periodic trends, atomic sizes, electronic state quantities
We need to train a modellinear and ridge regression - fit a function minimizing an L2 norm
decision trees - classify ∆Ea so as to reduce information entropy
neural nets - ‘neurons’ receiving and modulating inputs that learn after every use
We need to evaluate modelsRandomly split our 210 data into two groups & cross validate as before
Aren - OutcomesApproaching feasibility as an alternative
consensus of the physics describing our diffusion problem
fa(x1, x2 … xn) reduced from length 130 to <10
effective lattice constant
∆ ground state energy
melting temperature
pending submission to a publication
Thank you for your timeWe are happy to field questions
The group does work on industrial datasets!inquiries are welcome - contact Prof. Dane Morgan ([email protected], 608-265-
5879)
