Stalking the Materials Genome: A Data‐Driven Approach to the Virtual Design of Nanostructured Polymers

Abstract

Accelerated insertion of nanocomposites into advanced applications is predicated on the ability to perform a priori property predictions on the resulting materials. In this paper, a paradigm for the virtual design of spherical nanoparticle-filled polymers is demonstrated. A key component of this “Materials Genomics” approach is the development and use of Materials Quantitative Structure-Property Relationship (MQSPR) models trained on atomic-level features of nanofiller and polymer constituents and used to predict the polar and dispersive components of their surface energies. Surface energy differences are then correlated with the nanofiller dispersion morphology and filler/matrix interface properties and integrated into a numerical analysis approach that allows the prediction of thermomechanical properties of the spherical nanofilled polymer composites. Systematic experimental studies of silica nanoparticles modified with three different surface chemistries in polystyrene (PS), poly(methyl methacrylate) (PMMA), poly(ethyl methacrylate) (PEMA) and poly(2-vinyl pyridine) (P2VP) are used to validate the models. While demonstrated here as effective for the prediction of meso-scale morphologies and macro-scale properties under quasi-equilibrium processing conditions, the protocol has far ranging implications for Virtual Design.

Perspective: NanoMine: A material genome approach for polymer nanocomposites analysis and design

“…This process is informed by the predicted surface energy of the constituents. 55 This work is currently being refined into a tool to incorporate into NanoMine. The suite of data and tools can be used to assist in microstructure design to optimize properties for specific nanocomposite systems as illustrated in our previous paper, in which the descriptor-based methodology was applied to characterize and reconstruct a nanodielectric microstructure and understand the impact of dispersion on properties.…”

“…As a widely adopted technique in composite material simulation, finite element analysis (FEA) has been demonstrated as an effective method for prediction of macroscopic composite properties as it includes both explicit input of microstructure geometry and distinction of phases among different constituents. [57][58][59] As illustrated in previous work on the development of FEA models, 55,60,61 available data from NanoMine can be taken as input to the FEA models in order to simulate a macroscopic composite response.…”

“…The interphase property prediction tool for general nanocomposite materials is still under development; however, initial algorithms for interphase property approximations can be found in publications. 55,61 Integrated with materials data and microstructure analysis, the FEA web-tools provide predicted composite performance upon user-defined material constituents and will be refined and added with more functionalities in ongoing development.…”

Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data

“…Acceleration in the rate of material development and deployment has been the focus of several recent efforts in current literature (e.g., [1][2][3][4][5][6]). In this regard, multiscale modeling and simulation has been identified as a key enabler [7][8][9][10][11], because of its potential to dramatically reduce time and effort expended in experimentation.…”

“…There is a critical need for the development and deployment of a suitable supporting data infrastructure that efficiently integrates closed-loop iterations between experimental and multiscale modeling/simulation efforts. This need is being addressed by a new cross-disciplinary field known as materials data science and informatics [1,3,[12][13][14][15][16][17][18][19][20].…”