Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session F24: Matter at Extreme Conditions: SimulationsFocus Recordings Available

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Sponsoring Units: GSCCM Chair: Kien Nguyen Cong, University of South Florida Room: McCormick Place W186C 
Tuesday, March 15, 2022 8:00AM  8:36AM 
F24.00001: Multiscale modeling of amorphous glasses Invited Speaker: William Schill We develop a criticalstate model of fused silica plasticity on the basis of data mined from molecular dynamics (MD) calculations. The MD data is suggestive of an irreversible densification transition in volumetric compression resulting in permanent, or plastic, densification upon unloading. We show that these characteristic behaviors are wellcaptured by a critical state model of plasticity, where the densification law for glass takes the place of the classical consolidation law of granular media and the locus of constant volume states denotes the criticalstate line. A salient feature of the criticalstate line of fused silica, as identified from the MD data, that renders its yield behavior anomalous is that it is strongly nonconvex, owing to the existence of two welldifferentiated phases at low and high pressures. We argue that this strong nonconvexity of yield explains the patterning that is observed in molecular dynamics calculations of amorphous solids deforming in shear. We employ an explicit and exact construction to upscale the microscopic criticalstate model to the macroscale. Remarkably, owing to the equilibrium constraint the resulting effective macroscopic behavior is still characterized by a nonconvex criticalstate line. Despite this lack of convexity, the effective macroscopic model is stable against microstructure formation. We extend the study of the inelastic behavior of silica glass to include the effect of many different temperatures, pressures, and strain rates using MD and maximum entropy atomistics calculations. Owing to the temperature dependence of the model, the macroscopic model becomes unstable against adiabatic shear localization. Thus, the material adopts small interfacial regions where the shear strain is extremely high. We characterize the shear band size, thereby predicting a yield knockdown factor at the macroscale, and compare the results to behavior reported in flyer plate impact experiments. 
Tuesday, March 15, 2022 8:36AM  8:48AM 
F24.00002: PhaseField Modeling and Peridynamics for Defect Dynamics, and an Augmented PhaseField Model with Viscous Stresses Janel Chua, Kaushik Dayal, Vaibhav Agrawal, George Gazonas, Timothy Breitzman This work begins by applying peridynamics and phasefield modeling to predict 1D interface motion with inertia in an elastic solid with a nonmonotone stressstrain response. In classical nonlinear elasticity, it is known that subsonic interfaces require a kinetic law, in addition to momentum balance, to obtain unique solutions; in contrast, for supersonic interfaces, momentum balance alone is sufficient to provide unique solutions. This work finds that peridynamics agrees with this classical result, in that different choices of regularization parameters provide different kinetics for subsonic motion but the same kinetics for supersonic motion. In contrast, conventional phasefield models coupled to elastodynamics are unable to model, even qualitatively, the supersonic motion of interfaces. This work identifies the shortcomings in the physics of standard phasefield models to be: (1) the absence of higherorder stress to balance unphysical stress singularities, and (2) the ability of the model to access unphysical regions of the energy landscape. 
Tuesday, March 15, 2022 8:48AM  9:00AM 
F24.00003: Probing Structural and Magnetic Phase Changes in the Shock Response of Iron with Molecular Dynamics Svetoslav Nikolov, Julien Tranchida, Attila Cangi, Kushal Ramakrishna, Mitchell A Wood For ferromagnetic materials like iron which exhibit strong spinlattice interactions, magnetic fluctuations have a significant impact on the underlying mechanical, thermal, and structural material properties. Utilizing a large training database of high temperature/pressure ab initio calculations we apply the machinelearned Spectral Neighbor Analysis Potential method to capture the magnon and phonon degrees of freedom within a simultaneous molecular and spin dynamics simulation. Our method has been validated by examining the thermalmechanical properties and static transition pressures, which show good agreement with experiments. To further test our approach, we carry out large scale simulations that probe the shock response of iron for both single and polycrystalline domains. Doing so we characterize the shock driven bcctohcp structural transition along with the corresponding ferrotoparamagnetic transition which is directly observed in the simulations within the spin subsystem. This presentation will highlight the unprecedented predictive capability enabled for magnetic materials from atomistic molecular dynamics which is underpinned by advances in machine learned interatomic potentials. 
Tuesday, March 15, 2022 9:00AM  9:12AM 
F24.00004: Interfacing experimental data with machine learned interatomic potentials Ben Nebgen, Richard A Messerly, Namita Kharat, Nick Lubbers, Kipton Barros, Tom Murphy, Eric N Loomis, David S Montgomery Construction of Machine Learned (ML) interatomic potentials from quantum mechanical energy and force data has become routine. To produce a new generation of validated interatomic potentials for simulating materials under extreme conditions, it is critical to incorporate experimental data. Experimental data can be used to validate potentials constructed from quantum mechanical data and may be incorporated into the training procedure itself. In this talk, we will present a recently developed YZn interatomic alloy potential constructed using our Active Learning Framework (ALF). ALF automates the process of constructing ML interatomic potentials using active learning, where ML uncertainties are used to augment training datasets in an optimal way. ALF is freely available and can be deployed on HPC resources such as Sierra and other GPU accelerated compute clusters. Dynamic properties of both pure Zn and YZn alloys will be computed and compared to EXAFS measurements obtained from the Dynamic Compression Center (DCS) at the Advance Photon Source (APS) on shock compressed Zn. Finally, future methods discussing possible ways to incorporate experimental data into the training procedure itself will be discussed. 
Tuesday, March 15, 2022 9:12AM  9:24AM 
F24.00005: DeepLearning Potentials to Simulate ThermoMechanical and Physical Behavior of EntropyDriven Diborides Nur Aziz Octoviawan, Bikash Timalsina, Gregory E Hilmas, William G Fahrenholtz, Ridwan Sakidja The thermomechanical and physical properties of entropydriven diborides have been modeled by utilizing the deeplearning (DL) algorithms. The results from abinitio molecular dynamics (AIMD) simulations and Density Functional Theory (DFT) calculations at the ground state level including forces, energy, and virial data have been fitted into DeepMDkit to generate the DL potentials to estimate a series of critical materials properties at ultrahigh temperatures. The predictions on the properties have been found to be favorably comparable to the theoretical predictions and/or experimental results including the bulk modulus, elastic constants, cohesive and point defect energy of the diboride phases. In addition, the thermal properties including the thermal expansion coefficients and the thermal conductivity at elevated temperature can also be predicted quite well. The support from CMMI Division of NSF (Award No. 1902069) is gratefully acknowledged. We also acknowledge the computational supports from NERSC 
Tuesday, March 15, 2022 9:24AM  9:36AM 
F24.00006: Uncertainty Propagation in ErrorsinVariables EquationofState Model Lin H Yang, Jim A Gaffney, Suzanne J Ali, Amy E Jenei Highfidelity equation of state (EOS) models are essential in the modelling of a wide range of material properties under the influence of temperature and pressure. Desired quantities of EOS are accuracy, consistency, robustness, and predictive ability outside of the domain where they have been fitted. A much less recognized criterion for the choice of an EOS is the influence of the uncertainty from the fitting data. These data have associated uncertainties arising from the measurements and simulations and how the EOS model incorporates the values provide an interesting challenge. Current approaches to the EOS model construction do not capture these data uncertainties, potentially underestimating the total uncertainty in extrapolation regions while overfitting the data in interpolation regions. 
Tuesday, March 15, 2022 9:36AM  9:48AM 
F24.00007: Uncertainty Quantification of High Energy Density Material Models using Bayesian Analysis Philip D Powell, Nathan R Barton, Tom Lockard, HyeSook Park, Bruce A Remington, Robert E Rudd, Camelia V Stan, Damian C Swift, James M McNaney The development of accurate material models in the high energy density regime is of considerable interest in a variety of fields, including the study of astrophysical impacts and inertial confinement fusion. While plasma driven ramp compression, such as is now readily performed at the National Ignition Facility (NIF) and OMEGA Laser Facility, provides a powerful tool for probing this regime, the highly integrated nature of these experiments, together with the limitations on data quantity inherent in using such shared facilities, make it difficult to extract reliable model constraints valid across the entire range of relevant pressures (≤10 Mbar), temperatures (≤10^{4} K), and strain rates (≤10^{8} s^{1}). By performing large ensembles of hydrodynamic simulations, we leverage existing data on tantalum strength up to 8 Mbar, to systematically constrain the parameters of a variety of analytical strength models. Through Bayesian techniques, we obtain quantitative estimates of these parameters and their uncertainties and propagate these values to estimates and uncertainties in the material strength itself. Moreover, by explicitly constraining the model parameter space, this method allows us to clearly identify those relatively unconstrained dimensions, thereby pointing the way to future experiments with the greatest potential for producing further improvements in our material models. 
Tuesday, March 15, 2022 9:48AM  10:00AM 
F24.00008: A QuantumBased Test of Chemical Degradation Pathways in Silicones Matthew P Kroonblawd, Nir Goldman, Amitesh Maiti, James P Lewicki The study of chemical damage in irradiated silicone polymers (primarily polydimethylsiloxane, PDMS) date to the 1950's, with several works attempting to correlate smallmolecule offgassing with proposed radiationinduced reaction schemes. We develop a general conditional probability analysis approach that tests causal connections between proposed experimental observables to reexamine this chemistry through quantumbased molecular dynamics (QMD) simulations. Analysis of the QMD simulations suggests that the established reaction schemes are qualitatively reasonable but lack strong causal connections between offgassing and crosslinking. Further assessment of the QMD data uncovers a strong quantitative connection between hardtomeasure chain scission events and formation of silanol (SiOH) groups. A benefit of the proposed approach is that it enables independent quantumbased tests of assumed connections between experimental observables without the burden of fully elucidating entire reaction networks. 
Tuesday, March 15, 2022 10:00AM  10:12AM 
F24.00009: Relationship between thirdorder elastic coefficients and stressinduced changes on secondorder elastic coefficients Chenxing Luo, Jeroen Tromp, Renata M Wentzcovitch The secondorder elastic coefficients (SOECs) must be modified to account for the medium deformation to describe acoustic wave propagation in a medium under stress or strain. Early studies have shown this can be achieved by introducing additional thirdorder elastic coefficient (TOEC) related terms (e.g., [1]). This study reevaluates these formulations and provides ab initio verifications. We first examine the effect of hydrostatic stress on the pressure derivative of SOECs. Then, as a more general case, we investigate the modifications needed to obtain SOECs under hydrostatic and deviatoric stress. We show, in both cases, the change applied to SOECs is a linear combination of SOECs and TOECs. These relationships are tested with ab initio calculations of SOECs and TOECs vs. pressure on NaCl and MgO. The method to compute finitepressure TOECs is also selfconsistently tested. 
Tuesday, March 15, 2022 10:12AM  10:24AM 
F24.00010: A Newtonian Algorithm for Constant Pressure Molecular Dynamics with Periodic Boundary Conditions Noham Weinberg, Essex Edwards, Liam Huber, Zachary Sentell, Jacob A Spooner The concept of constant pressure molecular dynamics (MD) introduced by Andersen forty years ago became a staple of modern MD calculations. Most of the algorithms proposed so far to maintain constant pressure in the course of an MD simulation utilize scaled coordinates to describe particle dynamics. We propose an alternative approach where such dynamics is described by Newtonian equations of motion for particles in their unscaled Cartesian coordinates, as in constant volume simulations. The MD cell size dynamics, described by Newtonian equations of motion for timedependent cell vectors, is driven by a balance of the compressive force of the external pressure and the sum of interparticle forces across MD cell boundary. The performance of the proposed algorithm is verified by test numerical calculations. 
Tuesday, March 15, 2022 10:24AM  10:36AM 
F24.00011: Thermodynamic properties at high temperature with machine learning Johann Bouchet, Alois Castellano, Francois Bottin, Marc Torrent It is of crucial importance at high temperature to take into account anharmonic effects to correctly describe the thermodynamics properties of materials. 
Tuesday, March 15, 2022 10:36AM  10:48AM 
F24.00012: Manybody Effects in the Warm Dense Electron Gas Revealed by Variational Diagrammatic Monte Carlo method Pengcheng Hou The study of warm dense matter (WDM), which exists in various astrophysical objects and stateoftheart experiments with highpower lasers, is of high current interest for many applications. However, due to the lack of a thorough theoretical description of WDM, accurate numerical data for the density response in finitetemperature electron gas is essential. In this work, we investigate the local field correction (LFC), which incorporates all the quantum manybody effects in the density response of the warm dense electron gas by the stateoftheart variational diagrammatic Monte Carlo technique. We establish the static LFC G(q,T) for a wide range of the momentum q and the temperature T with high accuracy. Our numerical results for G(q,T) demonstrate nontrivial quantumtoclassical crossover in the warm dense regime, and reveal a novel scaling relation G(q/√T) at the highT and largeq limits. We further prove and numerically verify the asymptotic expression of G(q) at the largeq limit, allowing a systematic calculation of the exchangecorrelation kernel K_{xc}(r,T) in real space for various temperatures. Our study paves the way for more systematic ab initio calculations of WDM, and is directly relevant for experiments probing the manybody effects. 
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