Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session S59: Matter under Extreme Conditions III |
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Sponsoring Units: DCOMP Chair: Rahul Jangid, University of California, Davis Room: Room 301 |
Thursday, March 9, 2023 8:00AM - 8:12AM |
S59.00001: Phase-Field Modeling and Peridynamics for Defect Dynamics, and an Augmented Phase-Field Model with Viscous Stresses Janel Chua This work begins by applying peridynamics and phase-field modeling to predict 1-d interface motion with inertia in an elastic solid with a non-monotone stress-strain 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 phase-field 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 phase-field models to be: (1) the absence of higher-order stress to balance unphysical stress singularities, and (2) the ability of the model to access unphysical regions of the energy landscape. |
Thursday, March 9, 2023 8:12AM - 8:24AM |
S59.00002: “Research output software for energetic materials based on observational modelling/ machine learning” (RoseBoom©) Sabrina Wahler There is huge scope for the implementation of sustainable methods in the research of new energetic materials. It is certainly one of the most important aspects which must be considered and implemented in current and future modern scientific research. There are a number of ways this can be achieved, and with the development of the program “Research output software for energetic materials based on observational modelling/ machine learning” (RoseBoom©) it is hoped that the development of new modern energetic materials will be advanced, since it aims to provide access to quick and easy prediction methods which will indicate performance parameters (e.g. the detonation velocity and pressure, the key indicator for the power of an explosive) – before they have been synthesized. The software allows fast estimation of the performance, enthalpy of formation and density of new energetic compounds only based on the structural formula. To do this it combines empirical and machine learning models into one program, that can be used to evaluate performance of new energetic materials before synthesis and after synthesis within experimental uncertainty. The user-friendly design allows fast computation of hundreds of molecules within a few minutes with minimal user-input. A picture of a compound is sufficient, which can be taken using the screenshot function implemented in RoseBoom©, the molecule can be copied from a molecule editor, or a list of molecules/mixtures can be loaded into the program, obtaining the results in an Excel spreadsheet. |
Thursday, March 9, 2023 8:24AM - 8:36AM |
S59.00003: Uncertainty propagation in the equation-of-state model for gold Lin H Yang High-fidelity equation of state (EOS) models is essential in modeling a wide range of material properties under the influence of temperature and pressure. The desired quantities of EOS are accuracy, consistency, robustness, and predictive ability outside the domain where they have been fitted. A much less recognized criterion for choosing an EOS is the influence of the uncertainty from the relevant data. These data have associated uncertainties arising from the measurements and simulations, and how the EOS model incorporates the values provide an exciting 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. |
Thursday, March 9, 2023 8:36AM - 8:48AM |
S59.00004: First-Principles Modeling of Structural and Mechanical Properties of High-Entropy Borides Luke C Moore, Bria C Storr, Shane A Catledge, Yogesh K Vohra, Cheng-Chien Chen High-Entropy Materials are of substantial interest due to their desirable properties, which include stability at high temperatures, oxidation resistance, high hardness, to name a few. However, an accurate prediction of the properties of these materials is particularly challenging due to the number of combinatoric possibilities – both in structure and composition. To overcome the challenge, we consider two distinct structural generation techniques, Special Quasi-Random Structures (SQS) and Automated FLOW Partial Occupation (AFLOW-POCC), as well as a quantity known as the Entropy Forming Ability to predict the synthesizability of high-entropy materials. Since these materials are stabilized by entropy effect at high temperature, harmonic phonon approximations can fail. We thereby also explore ab initio molecular dynamics (AIMD) calculations to predict the dynamic properties and PVT curves. We believe that each of these techniques utilized in conjunction enables powerful predictive power of high-entropy material properties for laboratory verification. |
Thursday, March 9, 2023 8:48AM - 9:00AM |
S59.00005: Synthesis of Nitrogen Doped Graphene via Gas Phase Explosive Synthesis Everett V Baker, William G Fahrenholtz, Jeremy L Watts, Catherine E Johnson, Sean Bailey Graphene is a two dimensional hexagonal lattice structure of carbon atoms that has tremendous potential for uses ranging from electronics and sensors to new catalysts for chemical reactions. Recently, interest has grown in the potential to "dope" graphene with elements such as nitrogen to alter the physical and chemical properties for expanded uses including semiconductors, supercapacitors, and chemical catalysts. Current methods of production for nitrogen doped graphene, or N-graphene, are often vapor deposition processes or treating existing graphene with a nitrogen plasma, processes that require expensive equipment to do properly. This research used gas phase explosive synthesis of acetylene gas as a basis for graphene production, with the addition of nitrogen containing gasses to add a nitrogen dopant during the synthesis process. Using a single step synthesis reaction that directly incorporates nitrogen into the graphene lattice simplifies production, eliminating the need for a post-synthesis addition step to add nitrogen. The identification of suitable nitrogen precursor gasses and a repeatable process for N-graphene production will allow further research into new uses for N-graphene at a far lower cost than traditional methods currently allow. |
Thursday, March 9, 2023 9:00AM - 9:12AM |
S59.00006: Suppression of superconducting transition temperature in MoB2 via niobium substitution Shubham Sinha, Jinhyuk Lim, Ajinkya C Hire, Jung S Kim, Philip M Dee, Ravhi Kumar, Dmitry Popov, Russell J Hemley, Richard G Hennig, Peter Hirschfeld, Gregory R Stewart, James J Hamlin Recently, superconductivity was discovered in diborides like MoB2 and WB2. It was found that MoB2, in an MgB2-like structure, superconducts at temperatures above 30 K near 100 GPa. Following these discoveries, we explored the high-pressure superconducting behavior of Nb-substituted MoB2 (Nb0.25Mo0.75B2). High pressure x-ray diffraction measurements revealed that Nb0.25Mo0.75B2 stays in P6/mmm structure to at least 160 GPa. Resistivity measurements showed a Tc of 8.15 K (confirmed by specific heat to be a bulk effect) at ambient pressure. The Tc is suppressed to ∼ 4 K at 50 GPa, before gradually rising to ∼ 5.5 K at 170 GPa. The critical temperature at high pressure in Nb0.25Mo0.75B2 is significantly lower than that found in MoB2 at high pressure (30 K), revealing that Nb-substitution results in a strong suppression of the superconducting critical temperature. The calculated Allen Dynes Tc at high pressure is significantly higher than the observed Tc. Possible explanations for these observations are discussed. |
Thursday, March 9, 2023 9:12AM - 9:24AM |
S59.00007: Tuning high-strain rate deformation of self-assembled block copolymers Hongkyu Eoh, Jinho Hyon, Edwin L Thomas The mechanical behavior of materials at high strain rates and at small length scales is often surprising and has attracted significant attention across the materials community. The laser-induced projectile impact test (LIPIT) can be used to investigate the various deformation mechanisms and kinetic energy absorption characteristics during impact using a hard sphere to perforate a thin polymer specimen-target as well as impacting a polymer specimen-sphere into a rigid substrate. A self-assembled poly(styrene-b-2-vinylpyridine) (PS-b-P2VP) block copolymer (BCP) thin films consist of alternating in-plane lamellar layers. LIPIT allows launching of micron-scale silica projectiles at an incident velocity of over 300 m/s toward free-standing BCP films, producing extreme strain rates of ~107 s-1. The P2VP layers can be selectively swollen by ionic salts or ionic liquids. Pristine and swollen BCPs exhibit very different deformation morphologies and specific penetration energies due to the differences in the interaction between swelling agents and P2VP, such as the shape of radial and tangential crazes and the surface wrinkle, after impact. |
Thursday, March 9, 2023 9:24AM - 9:36AM Author not Attending |
S59.00008: Ab initio calculations of the melting line of MgSiO3 and Fe Felipe J Gonzalez, Burkhard Militzer The phase diagram silicates and iron at high pressure is of considerable interest for geophysics and planetary science. Understanding the process of crystallization of the mantle and core of rocky exoplanets largely depends on our ability to simulate the states of planetary materials at high pressure and high temperature that are considerably different from those at the Earth interior. Using density functional theory molecular dynamics, we investigate the melting curve of MgSiO3 and iron at megabar pressures. We derived the melting temperature by equating the Gibbs free energies of solid and liquid phases that we derived through the thermodynamic integration method. The melting curve allows us to study the crystallization behavior under high compression, and the access to free energies allows us to obtain isentropes, which are the thermodynamic paths that represent the interior of convective planetary interiors. We compare the melting curves of these two materials and the adiabatic gradients to determine how the cores of super-Earth planets crystallize. |
Thursday, March 9, 2023 9:36AM - 9:48AM |
S59.00009: Computing equilibrium properties by a dissipative non-equilibrium process Kangxin Liu, Grant M Rotskoff, Eric Vanden-Eijnden, Glen M Hocky A dissipative algorithm called quench was proposed that allows one to compute an equilibrium density of states (DOS) using a non-equilibrium estimator; this method was previously successfully applied to a mean field spherical Ising model. Multiple equilibrium starting points are drawn from a high temperature distribution that is easy to sample and quenched independently to low temperature regions that are usually difficult to sample in plain simulations. Here, we extend this method to molecular systems to a compute free energy surface (FES) rather than a DOS at any intermediate temperature. We implement this method in LAMMPS, which makes it possible for almost every molecular system and run simulations in parallel using Parsl to allow these independent quenches to be run simultaneously on high performance computing (HPC) resources. We investigate the influence of simulation parameters on our results and suggest heuristic choices of simulation parameters for general application. Our results demonstrate that sampling is focused properly in low free energy regions. In order to get an accurate FES at all energy levels, we couple quenching to umbrella sampling, and find that it can approximately accelerate convergance by 50 times for some model molecular systems. |
Thursday, March 9, 2023 9:48AM - 10:00AM |
S59.00010: Effects of Thin Film Coating on Ultra-Precision Machining of Single-Crystalline Sapphire Dalei Xi, Yiyang Du, Aditya Nagaraj, Suk Bum Kwon, Dae Nyoung Kim, Sangkee Min, Woo Kyun Kim Sapphire has been widely used in a variety of applications thanks to its superior thermal, electrical, optical, and mechanical properties. Since sapphire is a brittle material, it is of critical importance to have a complete understanding of mechanisms leading to plastic deformation and crack formation for the machining of this material. In experiments, the machining of sapphire is commonly assisted by thin film coatings with materials such as mineral oil, wax, glue and other adhesives and it has been known that these coatings play significant roles in influencing the critical depth of cut (CDC) and the formation and propagation of cracks. In this study, we introduce a model using the molecular dynamics (MD) method to investigate the effects of different coating films on the machining mechanisms of sapphire. Ultra-precision cutting simulations are performed on coated sapphire models with thin films of different adhesion, and the results are compared with uncoated models. The adhesion of coating films is controlled by adjusting the interaction parameters between the thin film and the sapphire substrate. Effects of thin film coating are further analyzed by investigating the deformation and fracture mechanisms at atomistic levels. |
Thursday, March 9, 2023 10:00AM - 10:12AM |
S59.00011: Discovering Quantum Phase Transitions with Fermionic Neural Networks Gino W Cassella, Halvard Sutterud, Sam Azadi, Neil Drummond, David Pfau, James Spencer, W Matthew C Foulkes Deep neural networks have been very successful as highly accurate wave function ansatze for variational Monte Carlo calculations of the ground states of solids and molecules. We demonstrate that deep neural network ansatze with identical architectures are capable of representing quantum phases with completely distinct qualitative characteristics. We investigate the ground-state wavefunction of the homogeneous electron gas either side of the famed Wigner transition using the Fermionic neural network (FermiNet) architecture. Without any hand-crafted features indicating the presence of a phase transition, the neural network correctly converges to a crystalline state at low density and a gaseous state at high density. We stress that this is a unique advantage of neural approaches which do not depend upon basis functions: traditional electronic structure methods require the selection of a basis which is appropriate for the qualitative nature of the phase being studied, hindering the study of hitherto unknown phases. Our results suggest variational calculations with deep neural network wavefunction ansatze could be used to detect unforseen quantum phase transitions, or discover new phases of quantum matter. In this talk, I will discuss the present limitations of the method and provide perspectives on future research. |
Thursday, March 9, 2023 10:12AM - 10:24AM |
S59.00012: Cross-sectional Scanning Tunneling Microscopy on clean m-plane GaN and Ga vacancy identification Edoardo G Banfi, Tomas J.F. Verstijnen, Michael Flatte’, Eva Monroy, Paul M Koenraad Gallium nitride has been studied thoroughly in the last decades as the material is relevant for applications in blue |
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