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Combinatorial Experimentation and Machine Learning for Materials Discovery
1. No impurity Ti (3 Å) Ti (6 Å) Ti (9 Å) Cu (3 Å) Cu (6Å) Cu (9 Å)
5 Å
10 Å
15 Å
20 Å
25 Å
35Å
45 Å
55 Å
ti(Å)
ts(Å)
Permanent magnet library
Ferroelectric library
Superconductor library
Combinatorial Experimentation and Machine
Learning for Materials Discovery
Ichiro Takeuchi
University of Maryland
2. - Combinatorial search of superconductors
- Active learning for directing high-throughput
experiments
Outline
Supported by NIST, ONR, and AFOSR
University of Maryland
V. Stanev
X.-H. Zhang
H. Yu
S. Lee
Y. Liang
J.P. Paglione
NIST
A. G. Kusne
B. DeCost
J. Hattrick-Simpers
Duke Univ.
S. Curtarolo
C. Oses
SLAC
A. Mehta
3. Composition Spreads of
Ternary Metallic Alloy Systems
Co-sputtering scheme Ni
Mn
Al
3” spread wafer
Al Ni
Mn
Phase diagram
Composition is mapped using an electron probe (WDS)
Review article: Green et al., JAP 113, 231101 (2013)
4. Rapid mapping of magnetic properties:
scanning SQUID
SQUID assembly
inside vacuum
Room temperature samples are measured
0 13 25 38 50 63 75
80
60
40
20
0
col
row
-2.50e+007 0.00e+000 2.50e+007
rho1_25
Mn
Ni
Ga
Raw data
Nature Materials 2, 180 (2003)
5. Rapid mapping of magnetic properties:
scanning SQUID
0 13 25 38 50 63 75
80
60
40
20
0
col
row
-2.50e+007 0.00e+000 2.50e+007
rho1_25
Mn
Ni
Ga
GaNi 0 1 2 3 4 5 6 7 8 9 10
Mn
50 100 150 200 250
M (emu/cc)
0 13 25 38 50 63 75
80
60
40
20
0
col
row
-2.50e+007 0.00e+000 2.50e+007
rho1_25
Mn
Ni
Ga
Raw data
Nature Materials 2, 180 (2003)
7. Rapid mapping of magnetic properties:
comparison with phase diagram
Nature Materials 2, 180 (2003)
C. Wedel and K. Itagaki,
Journal of Phase Equilibria 22, 324 (2001)
9. Ch 11
Ch 3
Ch 13
Middle region:
FeB2 – FeB4
more Bmore Fe
temperature
resistance
4.2 K 300 K
Fe-B composition spread: FeBx(x =2-4), 16 spots on one 1 cm2 chip
12. Solution: create our ow
from literature
29000 entries in MatNavi (2014)
Visualization and mining of NIMS SuperCon database
29000 entries
(Cuprates: more than 10000)
Data mining via supervised machine
learning using Magpie descriptors
Removing misentries, duplicates,
etc. results in 14000 entries
Magpie descriptors developed
by Wolverton et al
27. Procedures for active learning of
autonomous phase diagram mapping
# of all possible
measurements:
12 x 9 = 108
Start measurements
at room temperature
“Project” to higher
temperatures
Move to higher
temperatures once
enough confidence is
established
Need to measure less
and less points at
higher temperatures
29. Evolution of the combinatorial strategy
(and a future forecast)
Circa 1990 2000 2010 2017 and beyond
Challenges:
How to make
hundreds of samples
fast
How to measure
large number of
samples: speed,
number and
quantitative accuracy
How to quickly
analyze large
amount of data
Machine learning
to control and
reduce number
of samples# of samples:
x 100-1000
# of samples: x 0.1 – 0.2
# of samples: x 0.1 – 0.2 Reducing the number of data points