Technical Thrust Area

Nanotechnology and Lower Scale Phenomena

Chair: Anter El-Azab, Purdue University
Vice-Chair: Jaroslaw Knap, U.S. Army Research Laboratory
Members-at-Large: Celia Reina, University of Pennsylvania

Amartya Banerjee, University of California Los Angeles

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Webinar Series

July 27, 2022, 2pm CDT

Join via Zoom:
(Meeting ID: 845 4127 4013/Passcode: 799065)


Speaker: Michael Falk, Johns Hopkins University
Discussant: TBD

Title: TBD


June 29, 2022

Speaker: Markus Hütter, Eindhoven University of Technology
: Joerg Rottler, University of British Columbia

Title: Molecular Approach to Plasticity in Polymer Glasses: A Journey


A major benefit of multiscale modeling is that it helps to shed light on constitutive assumptions in macroscopic approaches to mechanics [1]; the stress tensor and the rate of plastic deformation are of particular interest. Nonequilibrium statistical mechanics is a powerful technique in this field, and it has been applied to study the plastic deformation of solids [2,3]. In this presentation, the focus is on multiscale modeling of solids in the glassy state. Structural glasses are particularly interesting (and challenging) for two reasons: they can age in the course of time, and the microscopic carriers of plastic deformation have not been identified to date (in contrast to the well-known dislocations for crystalline materials). Molecular simulations will be used to study glassy materials, in particular polymers. The goal is to establish a fine level of description that is suitable for a subsequent coarse-graining step to the macroscopic continuum level.
When studying polymer glasses on a molecular level, the structural rearrangements related to physical ageing and plastic deformation are rare events, in comparison to the rapid and continuously ongoing molecular vibrations. For studying in an efficient manner these rare events while still keeping molecular detail, a procedure has been proposed that scans the space of molecular configurations specifically for local minima and transitions between them [4]. A key ingredient in this procedure is the calculation of the free energy under appropriate mechanical boundary conditions [5]. In this contribution, we present results of the rare-event sampling for atactic polystyrene - as a prototypical example - below its glass-transition temperature. In the absence of deformation, we obtain rate constants for the minimum-to-minimum transitions extended over 30 orders of magnitude, with well-defined peaks at the time scales corresponding to the subglass relaxations of polystyrene [6]. As deformation is imposed, we observe that the transition states go through an instability and eventually collapse abruptly onto one of the connected local minima; furthermore, we present results about the transition rates as a function of deformation [7]. It will be discussed how these observations will eventually relate to the rate of macroscopic plastic deformation.
Acknowledgment: Part of this work is support by the Dutch Polymer Institute (DPI), projects no. 745ft14 and 820.
[1] E. van der Giessen, et al., Modelling Simul. Mater. Sci. Eng. 28 (2020) 043001 (61pp).
[2] M. Hütter, T.A. Tervoort, Adv. Appl. Mech. 42 (2008) 253-317.
[3] M. Kooiman, M. Hütter, M.G.D. Geers, J. Mech. Phys. Solids 90 (2016) 77-90.
[4] G.C. Boulougouris, D.N. Theodorou, J. Chem. Phys., 127(8) (2007) 084903.
[5] G.G. Vogiatzis, et al., Comput. Phys. Commun., 249 (2020) 107008.
[6] G.G. Vogiatzis, L.C.A. van Breemen, M. Hütter, J. Phys. Chem. B, 125(26) (2021) 7273-7289.
[7] G.G. Vogiatzis, L.C.A. van Breemen, M. Hütter, J. Phys. Chem. B (submitted, 2022).



May 25, 2022

Speaker: Alejandro Strachan, Purdue University; 
Discussant: Arun Mannodi Kanakkithod, Purdue University

Title: Enhancing Physics-Based Modeling and Extracting Physical Insight from
Data Using Machine Learning 

The synergy between principles-based modeling and data science is playing an increasingly important role in materials science and engineering. In addition, there is significant interest in using machine learning (ML) tools to extract physical laws as well as symmetries and associated invariants from data. I will discuss recent progress in my group on machine learning applied to multiscale modeling and in the development of interpretable models that balance accuracy with parsimony.
ML for multiscale modeling. ML interatomic potentials have shown near quantum mechanical accuracy at a fraction of the cost, enabling large-scale atomistic simulations. We developed an iterative approach to address the challenge of generating training data and address the stochastic nature of NN training. I will demonstrate the approach with the development of neural network reactive force fields (NNRF) for energetic materials over a wide range of temperatures and pressures and the phase change material GeSbTe of interest in electronics. The next step of the multiscale ladder is coarse-graining atomistic simulations, and I will show how dimensionality reduction techniques like non-negative matrix factorization and autoencoders can extract physically interpretable collective variables from MD simulations. Specifically, we developed a reduced-order chemical kinetics model with three components and two reactions starting from a 280-dimensional space.
Making workflows and data FAIR. Finally, I will also describe recent developments in nanoHUB, an open cyberinfrastructure for cloud scientific computing, towards making simulation workflows and their data findable, accessible, interoperable, and reusable (FAIR). We introduce Sim2Ls (pronounced sim tools) and the Sim2L Python library that allow developers to create and share end-to-end computational workflows with well-defined and verified inputs and outputs. The Sim2L library makes Sim2Ls, their requirements, and their services discoverable, verifies inputs and outputs, and automatically stores results in a globally accessible simulation cache and results database.

1)Yoo P, Sakano M, Desai S, Islam MM, Liao P, Strachan A. Neural network reactive force field for C, H, N, and O systems. npj Computational Materials. 2021 Jan 22;7(1):1-0.
2)Sakano MN, Hamed A, Kober EM, Grilli N, Hamilton BW, Islam MM, Koslowski M, Strachan A. Unsupervised learning-based multiscale model of thermochemistry in 1, 3, 5-Trinitro-1, 3, 5-triazinane (RDX). The Journal of Physical Chemistry A. 2020 Oct 28;124(44):9141-55.
3)Hunt M, Clark S, Mejia D, Desai S, Strachan A. Sim2Ls: FAIR simulation workflows and data. Plos one. 2022 Mar 10;17(3):e0264492.

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