DATA ANALYTICS IN NANOMATERIALS DISCOVERY
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1 DATA ANALYTICS IN NANOMATERIALS DISCOVERY Michael Fernandez OCE-Postdoctoral Fellow September
2 Materials Discovery Process Materials Genome Project Integrating computational methods and information with sophisticated computational and analytical tools to shorten the duration of materials development from years to 2 or 3 years. 3 Data Analytics for Nanomaterials Michael Fernandez
3 Materials and Molecular Modeling Sun Baichuan Team leader Amanda Barnard Collaborators Big data and HPC integration Piotr Szul Yulia Arzhaeva Deep learning and GPU computations Chris Watkins 2 Data Analytics for Nanomaterials Michael Fernandez
4 Outline Material Discovery Process Methods for atomistic simulations of materials Experimental confirmation Data-driven Computatioanl Nanomaterials Discovery Hypothetical material space sampling (structure generation and statistics) In silico high-throughput characterization (atomistic simulations and machine learning) Data storage, analytics, exploitation and integration 2 Data Analytics for Nanomaterials Michael Fernandez
5 Atomistic Simulations of Materials Theory can predict properties of materials Quantum chemistry methods can cover any chemistry Empirical potentials exist for large number of elements Computation is scalable and generically deployable N H = E nuclei ({R I })-σ e i=1 2 i + V nuclei r i + 1 σ N e 1 2 i j r i r j 4 Data Analytics for Nanomaterials Michael Fernandez
6 Atomistic Simulations of Materials Computational predictions later confirmed by experiments Self-assembly mechanism of nanodiamonds Barnard, A. S. et al. Nanoscale 3, (2011) DFTB simulations of the surface electrostatic potential of dodecahedral diamond nanoparticles of a) 2.2 nm and b) 2.5 nm The arrow indicates (111) (111) interface between two 4 nm sized nanodiamonds. 5 Data Analytics for Nanomaterials Michael Fernandez
7 Modern Nano- materials discovery cycle Hypothetical materials Experiment design Materials libraries Database Performance Measurement Polydispersive systems Exponential increase in complexity and diversity Nearly infinite combinatorial problem Theory, modeling and informatics In-silico screening Potyrailo, R. et al. ACS Comb. Sci. 13, (2011). Lead materials scale up Knowledge for rational design Banks of materials 6 Data Analytics for Nanomaterials Michael Fernandez
8 Nanomaterials Screening Departing from the Edisonian approach We can accurately predict a property, so it can be computed for entire materials spaces In silico structure generation vs. Combinatorial In silico design 7 Data Analytics for Nanomaterials Michael Fernandez
9 Nanomaterials Screening Systematic and extensive materials performance ranking. Big data discovery of structure-property relationships in unknown materials domains. Accelerated identification of high potential candidates and rational design principles. 7 Data Analytics for Nanomaterials Michael Fernandez
10 Data Analytics Challenges Information representation Fingerprints Information extraction Multivariate statistical analysis Knowledge discovery Data mining and machine learning Knowledge representation Visualization 7 Data Analytics for Nanomaterials Michael Fernandez
11 Polydispersity Challenge in Nanomaterials $$$ Controlled synthesis Purification Polydispersive sample Quasi-monodisperse sample Polydispersity can be detrimental for high-performing applications Purification of polydispersive nanoparticles samples is expensive 9 Data Analytics for Nanomaterials Michael Fernandez
12 Data Analytics of Nanocarbons Virtual structures relaxed using TB-DFT Nanodiamonds Graphenes 10 Data Analytics for Nanomaterials Michael Fernandez
13 Archetypal Analysis (AA) Finds a k m matrix Z that corresponds to the archetypal or pure patterns in the data in such a way that each data point can be represented as a mixture of those archetypes. In other words, the archetypal analysis yields the two n k coefficient matrices α and β, which minimize the residual sum of squares: Cutler, A. & Breiman, L. Archetypal Analysis. Technometrics 36, (1994). The predictors of X i are finite mixtures of archetypes Z j, which are convex combinations of the observations. 10 Data Analytics for Nanomaterials Michael Fernandez
14 Archetypal Analysis of Nanocarbons Nanodiamonds Graphene nanoflakes Fernandez, M. & Barnard, A. S. ACS Nano 9, (2015). 11 Data Analytics for Nanomaterials Michael Fernandez
15 Nanocarbons Prototypes Nanodiamonds prototypes Graphene prototypes Fernandez, M. & Barnard, A. S. ACS Nano 9, (2015). 13 Data Analytics for Nanomaterials Michael Fernandez
16 Estimation of Nanodiamonds Properties Fernandez, M. & Barnard, A. S. ACS Nano 9, (2015). 13 Data Analytics for Nanomaterials Michael Fernandez
17 Structural Diversity Challenge Graphene nanoflakes Trigonal Rectangular Hexagonal Defects, oxidation and edge passivation yield large structural diversity 13 Data Analytics for Nanomaterials Michael Fernandez
18 Structural Diversity Challenge Silicon qbits P P P Single Si substitution by P yields a large structural diversity 13 Data Analytics for Nanomaterials Michael Fernandez
19 Structural Diversity Challenge Metal-Organic Framework (MOF) ZIF-68 benzimidazole nitroimidazole 13 Data Analytics for Nanomaterials Michael Fernandez Zn 2+ CO 2 capture and sequestration (Science, 2008)
20 Structural Diversity Challenge Metal-Organic Framework (MOF) In-silico Combinatorial design 13 Data Analytics for Nanomaterials Michael Fernandez
21 Structural Diversity Challenge Metal-Organic Framework (MOF) MOF ZBP a) Organic Linkers b) Modification with of 35 functional groups gives a total of ~1.5 million (35 4 ) unique combinations 13 Data Analytics for Nanomaterials Michael Fernandez
22 Machine Learning Approach Machine learning prediction of functional properties Feature fingerprints Machine learning 13 Data Analytics for Nanomaterials Michael Fernandez
23 Data Analytics Challenge Binary decision tree of the Band Gap of graphene Accuracy 80% Features Surface area Number of atoms Shape aspect ratio Fernandez, M., Shi, H. & Barnard, A. S Carbon (2016). doi: /j.carbon Data Analytics for Nanomaterials Michael Fernandez
24 Machine Learning vs. Atomistic Simulations Estimation of the graphene Band Gap from topological features P i and P j, are the values of a bond order of the carbon atoms in graphene, while L is the topological distance, whilst L ij is a delta function delta function Fernandez, M. et al. ACS Comb. Sci. (2016) doi: /acscombsci.6b Data Analytics for Nanomaterials Michael Fernandez
25 Machine Learning of Graphene Radial Distribution Function (RDF) scores for graphene the summation is over the N atom pairs in the graphene structure, and r ij is the distance of these pairs and B is a smoothing parameter set to 10. Fernandez, M.; Shi, H.; Barnard, A et al. J. Chem. Inf. Model. (2015), 55, Data Analytics for Nanomaterials Michael Fernandez
26 Machine Learning vs. Atomistic Simulations Energy of the Fermi level Ionization Potential From RDF scores Fernandez, M.; Shi, H.; Barnard, A et al. J. Chem. Inf. Model. (2015), 55, Data Analytics for Nanomaterials Michael Fernandez
27 Machine Learning vs. Atomistic Simulations Machine learning prediction of gas adsorption in MOF Fernandez, M., et al. J. Phys. Chem. C 117, (2013). 13 Data Analytics for Nanomaterials Michael Fernandez
28 System size Accuracy and Coverage Challenges 5000 atoms TBDF/ Semiempirical 500 atoms Accuracy of electronic calculations methods vs. system size Density Functional Quantum Monte Carlo Coupled Cluster 100 atoms 20 atoms Accuracy 13 Data Analytics for Nanomaterials Michael Fernandez
29 Data-driven Challenge Machine learning for large material spaces Full Database Predictions Machine Learning 3 Partial Database Screening Ramakrishnan, R. et al. J. Chem. Theory Comput. 11, (2015). Fernandez, M et al. J. Chem. Inf. Model. (2015), 55, Machine learning predictions: -Functional property value or threshold =f( ) structure -Accuracy of different quantum-chemistry methods 15 Data Analytics for Nanomaterials Michael Fernandez
30 Challenges and Limitations Accuracy Gap Between Different Levels of Theory 6,095 isomers of C 7 H 10 O 2 E B3LYP QMC big gap big gap 13 Data Analytics for Nanomaterials Michael Fernandez
31 Accuracy Gap Predictions Predictions Machine learning calibration 13 Data Analytics for Nanomaterials Michael Fernandez
32 Data-driven High-throughput Screening Computational resources Input jobs Queue management Resubmit or kill failed runs Finished runs Data storage outputs Accuracy refinement 13 Data Analytics for Nanomaterials Michael Fernandez
33 Self-organization Map (SOM) of NPs SOM Ag-NP Electrostatic Potential 13 Data Analytics for Nanomaterials Michael Fernandez
34 Deep Learning for Nanomaterials Feature extraction Classification input 32x32 C 1 Feature maps 28x28 C 2 Feature maps S 1 10x10 Feature maps 14x14 S 2 Feature maps 5x5 B1 B2 Output Image 32x32 5x5 convolution 2x2 subsampling 5x5 convolution 2x2 subsampling Fully connected 13 Data Analytics for Nanomaterials Michael Fernandez
35 Thank you Data61 Michael Fernandez e michael.fernandezllamosa@csiro.au w
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