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Microstructure and Materials Informatics

University of Florida - May 20th 2013

Krishna Rajan

Microstructural Data and Information Role of informatics and microstructure Microscopy and informatics - SEM - TEM - APT Summary

Outline

Krishna Rajan: Iowa State University

1. “Crystallographic Evolution in Directionally Solidified Microstructures” J. Trogolo and

K.Rajan in Microstructure Evolution: Characterization and Modeling, pp.39-47 eds. J. Dantzig and S. Marsh, TMS ,Warrendale (1998)

2. Microtexture and Anisotropy in Wire Drawn Copper”, R.Petkie and K.Rajan Materials Science and Engineering A 257 185-197 ( 1998).

3. Rodrigues-Frank Representations of Crystallographic Texture in Electron Backscatter Diffraction in Materials Science Eds. Schwartz, A.J.; Kumar, M.; Adams, B.L pp. 39-50 Kluwer Academic NY (2000).

4. Refining Spatial Distribution Maps for Atom Probe Tomography via Data Dimensionality Reduction Methods". S.K.Suram and K.Rajan ; Journal of Microscopy and Microanalysis 18 , 941-952 (2012)

5. "A Graph-Theoretic Approach for Characterization of Precipitates in Alloys from Atom Probe Tomography data S. Samudrala, O. Wodo, S. K. Suram, S. Broderick, K. Rajan, B. Ganapathysubramanian, " Computational Material Science, vol. , (in press-2013)

6. Data Mining for Isotope Discrimination in Atom Probe Tomography: S.R. Broderick, A. Bryden, S.K. Suram and K. Rajan : Ultramicroscopy (in press - , http://dx.doi.org/10.1016/j.ultramic.2013.02.001 -2013)

References


http://dx.doi.org/10.1016/j.ultramic.2013.02.001

Multidimensional

Microstructural characteristics

Features controlling microstructure –property relationships

Challenge:

To construct Robust Correlations

between microstructure, chemistry, processing and properties

Methods:

Data analysis

•Statistical learning

Materials modeling

•Electronic structure

•Microstructure

•Continuum property

Informatics

Materials functionality= F ( x1 , x2 , x3 , x4 , x5 , x6 , x7 , x8 ……)


Signal and Spatial Domains


N. Bonnet J. Microscopy 190 2-18 (1997)

G. Mobus, R. Schweinfest, T. Gemming, T. Wagner and M. Ruhle J. Microscopy vol 190 , pts 1 / 2 April / May 1998 , pp. 109-130

Data Cube



Mechanical property- microstructure data cube

Mechanical property- microstructure data cube- outlier detection



Informatics guided imaging: where to look

“Crystallographic Evolution in Directionally Solidified Microstructures” J.

Trogolo and K.Rajan in Microstructure Evolution: Characterization and Modeling, pp.39-47 eds. J. Dantzig and S. Marsh, TMS ,Warrendale (1998

N data channels

Energy loss

DE

E

DE

Extracted image spectrum

K.Kelton, X.Li and K. Rajan J. Non-Crystalline Solids (2005)

Al-RE-Ni Glass

x

y

Krishna Rajan

Chemistry- microstructure data cube- correlation imaging


Atom Probe : imaging modalities


Atom Probe : a multidimensional problem


Chemical Noise Chemical Noise f(Crystal structure, pulse energy, pulse fraction, surface diffusion, specimen geometry …) .

APT

Spatial Noise in APT

Spatial Noise f(Crystal structure, dhkl , missing data, local magnification effects, reconstruction errors, pulse energy, pulse fraction, surface diffusion …)

Phenomenological relationships between most of these parameters and their effect on spatial/chemical noise are unknown. Thus, data driven treatment is necessary for analysis of noise in APT data.

Signal/ Noise enhancement


PCA

NLDR

Geometric

Distance

Euclidian Geodesic

(eg. IsoMap)

Other

(eg. KPCA)

Topology

PDL

(eg. SOM)

DDL (eg. LLE)

Other

AA NN

Data dimensionality reduction


Spatial –Signal Domain noise


Plane A Plane B

Anti-correlation

d110

A B A B A B A B A

Ideal Material

APT data

z-SDMs d0

11

Crystal structure can be observed

and anti-correlation in the

structure between adjacent planes is

observed

Does this slice have any crystallographic information. Can we salvage this information?

Spatial –Signal Domain noise


(40000*200)

Right Singular Vectors capturing correlations in the SDM data

Projection of SDM data onto right singular vectors

Spatial –Signal Domain noise : quantification


Noise Reduction achieved consists of two aspects: Noise reduction based on data reconstruction using only the Structural Relevant Singular

Vectors. Noise reduction by identifying xy-SDMs that contribute to noise within the SRSVs.

EVSUX T

SRSVSRSVSRSV

(40000x200)

Structure Signal Noise alienated in the error term.

Spatial –Signal Domain noise : quantification


Sectioning of Data set

Eigenvalue Decomposition

(PCA)

TOF

Load

ings

Co

un

t

TOF

Raw TOF Spectra: No obvious patterns

Eigenspectra: “Hiddn” patterns

Spatial –Signal Domain noise : uncovering “hidden” patterns


Data Mining for Isotope Discrimination in Atom Probe Tomography: S.R. Broderick, A. Bryden, S.K. Suram and K. Rajan : Ultramicroscopy (in press - , http://dx.doi.org/10.1016/j.ultramic.2013.02.001 -2013)


24Mg2+

25Mg2+ 26Mg2+

27Al2+

Mg2+ Al2+

Before After

TOF TOF

Spatial –Signal Domain noise : uncovering “hidden” patterns


Data Mining for Isotope Discrimination in Atom Probe Tomography: S.R. Broderick, A. Bryden, S.K. Suram and K. Rajan : Ultramicroscopy (in press - , http://dx.doi.org/10.1016/j.ultramic.2013.02.001 -2013)


What data do we need?


Extracting information beyond models and experiment 3D imaging Quantification of uncertainty and noise Stereology: back to basics

Summary


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