Combining Heuristic and Physics-Based Methods for Predicting Nanocomposite Properties

Transcription

1 Combining Heuristic and Physics-Based Methods for Predicting Nanocomposite Properties Curt M Breneman*, Ke Wu, Lisa Morkowchuk, Linda Schadler, Cate Brinson, Yang Li, Michael Krein and Bharath Natarajan Nanoinformatics Workshop, October 15, 2013

2 POLYMERS Nanoparticle dispersion in polymers NANOMATERIALS Fundamental Processing & Knowledge Manufacturing Gaps Our Challenge: How do we predict the properties of new materials using methods that are scalable, grounded in physics, and are not so slow or expensive that they are unfeasible? BIOMOLECULES

3 Presentation Outline Problem Overview Modeling Methods Domain Applications 1. Polymer Materials 2. Multi-scale Modeling on Polymer Nanocomposites

4 Materials with exotic properties are needed Multiple-property optimization is required Design of Application-Specific High-performance Materials Existing design methods fail to address these issues This is the driving force for a Paradigm Shift, with scale-bridging Hybrid MQSPR and Physicsbased modeling leading the change

5 Materials and Manufacturability Not only do we need to predict properties under EQUILIBRIUM conditions, we need predict the response due to NON-EQUILIBRIUM processing (microstructure and properties) e.g. - Just scaling up the processing can change material properties

6 Manufacturability and Informatics QSPR Informatics system analyzes materials, trends and relationships and generates side-byside comparisons of processing options The set of the most optimal designs we are able to generate Seek a Pareto-Efficient Frontier of composable designs that can most closely achieve desired performance thru Virtual Experiments

7 Problem Domains: Atomic Scale to Bulk Materials Quantum Nano Meso Micro Macro Length (meters) Bharath Natarajan, Ph.D. Dissertation RPI 2013

8 Focused Problem Domains: Pure Polymers and Nanocomposites Nanofillers < 100nm Nanofibers CNT Graphene Platelets Colloidal Particles Carbon Black Bharath Natarajan, Ph.D. Dissertation RPI 2013

9 Key Aspects of a Hybrid Approach Hypothesis Driven Perhaps machines can generate the hypothesis, but better if we do. Multiscale and can t be linked with fidelity for now heuristics are needed (MQSPR) e.g. we can predict solubility and compatibility pretty well using heuristics Scale-appropriate Descriptors - must be identified or developed to represent the physics that control the interactions responsible for observable behavior (MQSPR) Scale-specific Machine Learning - appropriate for Descriptors and dataset sizes Heuristics!

10 Data Representation: Polymer Descriptors Directly from first-principle of physics-based calculations Indirectly from first-principle calculations (e.g TAE descriptors) From empirical methods, partial charges (e.g. PEOE partial charges) and especially surface properties (ALP and EP surface color bin descriptors).

11 Glass transition temperatures Application Domain: Pure Polymers Dielectric constants (electronic and polar components) Band gaps All descriptors in this case had to be modified to better represent polymer structures

12 Glass Transition Temperature Glass transition is the reversible transition between glassy and rubbery structure Stiffness of the chemical structure can increase Tg A total number of 162 polymers were used for training while 32 of them were kept as an external test set. (Bicerano)

13 QSPR informatics workflow Tg Surface Energy Dielectric Constant Breakdown Strength Descriptor Calculation Machine Learning Molecular Representation Prediction

14 Glass Transition Temperature After Objective Feature selection, 32 descriptors were identified for SVM/Regression Some of the descriptors show clear trends with the Tg value, capturing part of the information (which is not often observed)

15 Glass Transition Temperature Training and Test

16 Glass Transition Temperature Y-scrambling and Cross-validation

17 Dielectric Constant High dielectric constant materials are needed in many areas Dielectric constants can be divided into several components caused by different mechanisms. A total number of 58 polymers was used while 12 of them were kept as an external test set

18 Dielectric Constant: Training and Test After feature selection, 26 descriptors were selected for SVM/Regression modeling

19 Dielectric Constant Y-scrambling and Cross-validation

20 Band Gap Band gap is closely related to the dielectric constant, conductivity loss and breakdown strength, which are other key factors in optimizing the energy density of te dielectric materials The band gap data is calculated from DFT by Rampi Ramprasad s group A total number of 152 polymers were used while 32 of them were kept for external testing

21 Band Gap Training and Test After Objective feature selection, 55 descriptors were selected for SVM/Regression modeling

22 Band Gap Y-scrambling and Cross-validation

23 Pure Polymer Dielectric Modeling using Modular MQSPR Binary Fingerprints can be used directly for search and retrieval (Tanimoto) or for non-linear modeling by creating a Fingerprint Kernel Limitations: Each structural component must be present explicitly in the Fingerprint definition.

24 Application Domain: Polymer Nanocomposites To develop a hybrid nanocomposite design tool that uses MQSPR (Materials Quantitative Structure-Property Relationships) together with FEM (Finite Element Modeling) to enable quantitative high-throughput thermomechanical property predictions for new nanocomposite materials. To allow nanocomposite designers and engineers with a quantitative method for exploring what if scenarios on proposed polymer nanocomposite materials.

25 Nanocomposite Materials Informatics: In Practice Take experimental data on nanocomposites from lab and literature: We know constituents We know morphology We know macroscopic properties Perform chemical modeling Obtain parameters describing nanoparticle surface Obtain parameters describing polymer Perform meso scale modeling Correlate degree of dispersion to interphase properties and connectivity Predict macroscale properties Integrate results across scales through nanomaterials MQSPR Brinson Advanced Materials Lab

26 Workflow

27 Representing Nanostructures Structural Descriptors Physicochemical Descriptors Topological Descriptors Geometrical Descriptors Molecular Structures Descriptors Model Property

28 Multi-Scale Nanocomposite Descriptors Electronic Thermodynamic Imagebased Shape-derived Constituent Properties Atomic QM/MM, statistical thermodynamics / integration. NAMD on small model systems TAE, ab-initio electron densitybased properties, topological distributions of properties N/A PEST or PESD, based on electronic isodensity VdW surface shape and electronic properties or interchain interactions Electronic or ionic carrier mobility Electronic Trap depth and density, Dielectric behavior Mesoscale/ coarsegrained MC/MD / Brownian dynamics thermodynamic integration Classical Electrostatic, Hydration, Lipophilicity N/A QPEST, shape/ property hybrid descriptors based on solvent excluded surface shape and classical or mapped surface properties Molecular mobility Phase Nanoscale to Microscale Experimental, spectroscopic data and parameters N/A Density, dispersion, Statistical descriptors of SEM data Statistical, Wavelet characterization of morphology Particle stiffness Polymer Morphology Bulk Experimental, calorimetric data N/A SIFT, TEM particle distribution encoding N/A Polymer relaxation spectra, bulk modulus

29 RECON/TAE Histogram Descriptors

30 Hybrid Informatics Approach - Heuristic Scale Bridging Breneman, Brinson, Schadler FEA Brinson Group

31 Interphase properties Unlike particle and matrix, interphase properties unknown Contributing factors: Geometry of nanoparticle Functional groups on nanoparticle Polymer chains on nanoparticle Polymer characteristics Percolated interphases affect bulk properties Attractive interactions, wettability of interface: Tg Repulsive interactions, dewetted interface: Tg Stiffness, strength, toughness impacted Success depends on properly characterizing features of the interphase Brinson Advanced Materials Lab

32 1. MQSPR - Surface Energy (30 polymer training set) Histogram descriptors from molecular vdw surfaces 20-mer energy-minimized structure of poly(n-butyl acrylate)

33 2. Mapping dispersion to Energetics Dimensionless Parameter Empirical functions where VF is vol fraction, r o is primary particle size, A c, B c, A d, B d are dimensionless parameters obtained through least square fitting Can now create a microstructure after predicting surface energies

34 E or E (Pa) Northwestern University Yang Li, Hua Deng: Brinson Group 3. Interphase Reconstruction Calculate properties of polymer and interphase at each T Adding interphase layer according to energy parameters Training System: PS with 3 wt% Chloro-Silica Shifting neat polymer master curves in frequency domain. 1E9 1E8 Shifting decades (S d ) 1E7 from bulk in the 2-layered interphase : (W s /5ΔW a ) for the inner layer and 1E6 (W s /10ΔW a ) for outer layer Temperature ( o C) Extract viscoelastic response of the composites (Tanδ) Tg of the composite found

35 Comparison of FEA Prediction with Experiment

36 !dielectric!constant! Dielectric Behavior Nanocomposite Dielectric Materials MQSPR to Bridge Length Scales Ab Initio CONTROLLED Nanofiller/ Matrix Interface Interactions MESOSCALE??? Polymer Dynamics Repulsive Surface Continuum Mechanics UNIQUE & OPTIMIZED Nanocomposites for Advanced Technologies Short Molecules with dielectric functionality Long molecules for dispersion control PARTICLE SHAPE EFFECTS Attractive Surface Distance from the Particle Surface PREDICTABLE & TAILORABLE Nanofiller Morphologies Dielectric Constant Breakdown Strength Energy Storage Aspect ratio increases Filler!volume!frac2on!

37 Nanocomposite Breakdown

38 Modeling Nanocomposite Breakdown

39 Materials Informatics Web Tool Polymer: Build Structure Bharath Natarajan

40 Where this is going: Prospective Predictions We can move from this to design - Given particle + polymer Given functionality Calculate interaction parameters Can then predict materials properties Predictions can then be validated Domain of Applicability Assessment Comparison with Experiment or Physics-based model

41 Key Applications Applications where the structuring or chemistry at the molecular or nanoscale is key. At the moment the potential applications seem almost endless Dielectric Materials by Design: Nanodielectrics but now the biomolecule is the component sensitive to electric field and carrier trapping - are nanobiocomposites the next great energy storage material? High Energy-Density capacitors for energy storage (Civilian and Military) Structural Materials by Design: Nanocomposites with tailored thermomechanical properties Materials with Pareto-optimized sets of properties INTERFACES: Current strengths and future developments could focus on the prediction and exploitation of Rensselaer s ability to model, synthesize and test materials with properties related to specialized interphase regions and meso-structural properties.

42 Energy Storage Technologies High energy density capacitors 1 msec 1 sec Adapted from: Abruna, Kiya & Henderson, Physics Today, December 2008 issue 1000 sec Capacitors are the only option for rapid discharge (e.g., pulsed power) applications

43 An Example: Pulsed Power Rapid release of electrical energy from an energy storage capacitor allows power to be amplified many fold at modest average power consumption Civilian Use Food preservation Surface Processing Metal Forming, Joining Health Care Energy Delivery Systems Department of Energy Inertial Fusion Magnetic Fusion Nuclear Weapons Effects Simulations Adapted from: Pulsed power technology and applications North America, EPRI Report (1999) Defense Industry High Resolution Radar Kinetic Energy Weapons Weapons Simulation Radiation Weapons Beam Weapons

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45 Thank you!