Hierarchical Materials Informatics. Surya R. Kalidindi. Funding: ONR, NIST

Transcription

1 Hierarchical Materials Informatics Surya R. Kalidindi Funding: ONR, NIST

2 Advanced Materials = Hierarchical Materials Strongly coupled structure and physics at multiple scales What are Materials Genomes at different hierarchical structure scales? Kalidindi and DeGraef, Annual Review of Materials Research, 45, pp , 2015.

3 Central Challenges in Hierarchical Materials Number of material structure features involved is too large to allow a systematic study using the established scientific approaches with modest resources Available experimental and computational modeling tools generally provide incomplete and uncertain information on the phenomena of interest Legacy data, codes, and multi-physics knowledge are highly fragmented, distributed, and not easily accessible

4 MGI/ICME: Goals & Strategies Scale-up of Innovation: Digital Knowledge Systems Accelerated Innovation: Templated Workflows Culture Change: e-collaborations

5 Materials Knowledge = Process-Structure-Property (PSP) Linkages How far up do we need to climb up this pyramid? How do we get there within stipulated cost and time constraints?

6 Templated Workflows for Mining Materials Knowledge Employ Available Data Science Tools Multivariate Polynomial Regression Decision Table Instance Based KNN KStar (Entropy KNN) Support Vector Machines Linear Regression (Line) Robust Regression (Line) Pace Regression (Clustering) Artificial Neural Networks M5 Model Tree Cross-validation tools Take advantage of emerging high performance data science toolkits and cyberinfrastructure Allow automation, efficient large scale explorations, and highly productive e- collaborations Novel objective Decision Support Systems or Recommendation Systems

7 Template for Microstructure Quantification Step 1: Convert microstructure image into a digital signal Step 2: Compute n-point spatial correlations Step 3: Obtain low dimensional representations using principal component analyses (PCA) Polymer Chains Simulation Data

8 Step 1: Ensemble of 2-Phase Microstructures Inclusions in Steel Matrix Assume Basic Inclusion Shapes Volume Fractions between 0% and 20%; 900 microstructures 1) Randomly Scattered 2) Vertical Bands 3) Horizontal Bands 4) Clustered Gupta et al., Acta Materialia, 91, pp , 2015

9 Step 2: 2-pt Statistics of Microstructure Example Microstructure Corresponding 2-pt Statistics Histogram of all patterns defined by two points The complete set has enough information to be able to reconstruct the original image in most cases

10 Step 3: Feature Extraction Using PCA Random Placement (300) Horizontal Bands (200) Vertical Bands (200) PC3 Clustered Placement (200)

11 PCA Representations Structure = (7.95, 0.46, 0.78)

12 Application: Atomic Structure Classification Kalidindi, Gomberg, Trautt, Becker, Nanotechnology, 26, , 2015

13 Atomic Structure Classification

14 Templated Structure-Property Linkages D microstructures Each microstructure quantified using PCs of 2-pt statistics Property evaluated using FE model Polynomial Fit for P-S linkage Second Order Polynomial Regression with 5 PCs

15 PSP Linkages from Experiments Dual Phase (DP) Steels Annealing 2hrs at 450 C in vacuum Intercritical Annealing 750 C 4 mins-salt bath 780 C 4 mins-salt bath 810 C 4 mins-salt bath Plastic Deformation 5% TR 10 % TR 5% TR 10 % TR 5% TR 10 % TR Bake Hardening 170 C 20 mins 170 C 20 mins 170 C 20 mins 170 C 20 mins 170 C 20 mins 170 C 20 mins Sample ID C 780 C 810 C

16 Mechanical Properties Using Microindentation 1500 σ y = MPa, a y = µm Stress (MPa) E s =207.8 GPa elastic post-elastic modulus offset yield Strain Initial loading segment used to determine modulus and zero point Elastic modulus, yield point, strain hardening etc can be studied High throughput approach for quantifying variance in mechanical properties at different length scales (from 50 nms to 500 microns)

17 Structure Measurements Using EBSD Low IQ identified as Martensite IPF map Segmented map High dislocation density areas identified with KAM values Large number of measurements to ensure statistical relevance Cold work results in a thicker layer with high KAM around martensite 2-pt statistics + PCA

18 Low-Dimensional P-S Visualizations A set of 30 scans on each sample were collected, analyzed and plotted in PCA space Microstructures associated to each processing path cluster in well separated groups. Some samples show less variation in microstructure and some show larger variation.

19 Low-Dimensional S-P Linkages 1600 σ y (experimental values) σ y (predicted values) σ (Mpa) = PC PC2

20 Localization Linkages Localization ε &( x) = a( x) ε& ( x) Homogenization Scale-bridging needs to accommodate bi-directional flow of information, i.e. both homogenization and localization need to be addressed. Should be able to exercise scale-bridging with minimal computational effort.

21 Metamodels for Localization Microstructure, Local response, Physics Based Models : local response Regression methods Microstructure, Calibration of is a one time computational cost It serves for any microstructure in the material system Combine best known physics with data science techniques Can be a series of kernels

22 Localization Linkages (MKS) Data driven framework $ = "! # %& + '!! ( ( $ $ %& ( %& " ( ( # ( ## ( + : influence function : local microstructure Convolution : local response Calibrate on selected microstructures and their FE predicted response fields; use the same on new microstructures of the same material system to predict their response fields.

23 Stress Contours in Polycrystals 45 x 45 x 45 Microstructure. Each color represents a distinct crystal lattice orientation randomly selected from cubic FZ. FEM prediction: 3 minutes with 16 processors on a supercomputer MKS prediction: 30 seconds with only 1 processor on a standard desktop computer Yabansu and Kalidindi, Acta Materialia, 94, pp , 2015

24 Concentration Fields from Phase-Field Models Calibration to a range of initial concentrations fields (different average concentration and different spatial distribution) Legendre MKS Computational Cost: Simulation 500 time steps sec Legendre MKS Basis e-3 sec

25 Practical Multiscaling Using MKS-FE 637 C3D8 Elements 5,899,257 C3D8 Elements (55 s on a standard desktop computer with 2.6 GHz CPU and 4 GB RAM) (15 hrs when using 64 processors on a supercomputer) Al-Harbi et al., Modelling and Simulation in Materials Science and Engineering, 20, , 2012

26 MKS-FE Simulations A B

27 MKS-FE Simulations

28 Materials Data Sciences and Cyberinfrastructure Properties/Responses and Performance High-Throughput Characterization High-Throughput Prototyping Low-Cost, Robust, Reduced order models Hierarchical Structure and Interfaces Low-Cost, Robust, Reduced order models Synthesis and Processing Data Management Capture, storage, aggregation, retrieval, and sharing protocols Knowledge Databases Data Analytics Mining the embedded high value information via filtering, data fusion, uncertainty analyses, statistical analyses, dimensionality reduction, pattern recognition, regression analyses, machine learning, and statistical learning e-collaborations Online tools designed to seed, nurture, and sustain synergistic cross-disciplinary research collaborations among distributed team members by facilitating intimate discussions and annotations of intermediate research results Workflow tracking for RFT-MII projects to assess acceleration of materials development

29 e-collaboration environment built on existing web services (Github, Jekyll, Disqus, etc.) Centralizes data, codes, discussions, visualizations, and annotations to facilitate e- collaborations High level commands to materials data analytics tools to produce process-structure-property (PSP) linkages Instant updates from the lab to the cloud with intermediate research results Most features accessible on smartphones

30 New Course: Materials Informatics First course in the Synthesis and Integration Track Offered for the first time in Fall 2014; ongoing now 17 PhD students registered for the course o Student s backgrounds included materials science and engineering, mechanical engineering, civil engineering, chemical and biological engineering, and computational science and engineering o Formed nine cross-disciplinary teams with one student taking responsibility for the domain application and the other student responsible for data sciences o MATIN was employed as the central e-collaboration platform

31 New Course: Materials Informatics The teams picked up ongoing research projects (from their research groups) as the topics for the class projects o o o o o o o o o Geet Lahot and Alicia White Developing Structure-Property Linkage for Glass Fibre Reinforced Polymer Composites Nils Persson and Dalar Nazarian Quantifying the Structure-Property Relationship in P3HT Thin Film Transistors Noah Paulson and Alex Lohse Crystallinity Analysis in Polyethylene Molecular Dynamics Dipen Patel and Akash Gupta Structure-Property Linkages for Polycrystalline Materials Ali Khosravani Extract Microstructure-Property-Processing Linkages in Ageing of Al6061 Aluminum Alloy Jason Allen and David Zhao Identifying Multi-Layer Formation and Growth on Tungsten Nano-Wires Patxi Fernandez-Zelaia and Ahmet Cecen Exploring Process Structure Linkages in Machining of Commercial Purity Titanium David Brough and Abhiram Kannan Data Science Solutions to X-ray Scattering Datasets Mahdi Roozbahani and Jie Cao Structure-Property Linkages for Packed Soil Particles

32 New Course: Materials Informatics authors 25 posts 159 images 293 comments 97 links 985 presentations 42

33 Summary Statements Emerging concepts and toolsets in Data science and Cyberinfrastructure can be a strong enablers for systematic mining and capture of Materials Knowledge and its dissemination using broadly accessible open platforms (e.g., MATIN, PyMKS) Examples presented in this talk serve to establish promise/potential. Clearly, much work needs to be done to establish this potential on a firm basis; need productive collaborations to critically evaluate this potential