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 A61: Understanding Amorphous Matter Through Modeling and SimulationFocus
|
Hide Abstracts |
Sponsoring Units: DCOMP Chair: Robert Riggleman, University of Pennsylvania Room: Room 418 |
Monday, March 6, 2023 8:00AM - 8:36AM |
A61.00001: Non-metric interaction rules in soft and living matter Invited Speaker: Daniel M Sussman The effect of what might be called “non-metric” interactions – for instance Voronoi-like interaction graphs that determine forces in models of dense cellular matter or k-nearest-neighbor rules dictating alignment interactions in models of flocking – arise naturally when studying active and living material systems. Do these novel forms of interaction support new material properties and dynamical behavior, and if so, how? In this talk, I will describe two vignettes from my group’s recent work that highlight how these types of interactions give rise to new phenomena in amorphous matter. In the context of cellular matter, I will discuss both the unusual mechanics and dynamics that arise when modeling disordered epithelial monolayers in this way, and I will comment on a puzzlingly robust data-driven approach for predicting local cellular rearrangements. I will then discuss models of flocking animals, describing how non-metric interaction rules are a natural source of microscopic non-reciprocity. This in turn has dramatic implications for the disordered flocking phases that can be observed. |
Monday, March 6, 2023 8:36AM - 8:48AM |
A61.00002: Mechanical loss in doped amorphous oxides with machine learning potentials Jun Jiang, Rui Zhang, James N Fry, Riccardo Bassiri, Martin M Fejer, Hai-Ping Cheng Doped amorphous oxides (TiO2-doped Ta2O5) providing very good optical and mechanical properties are used as mirror coatings in the Laser Interferometer Gravitational-Wave Observatory (LIGO) detector. To increase sensitivity, it is desirable to reduce the noise in the coatings. In this work we focus on thermal noise manifested by mechanical loss. Modeling doped amorphous oxides is extremely difficult due to their complicated energy landscape. Determining some properties requires a large simulation box to capture a fair sample of configurations. Hence fast and accurate potentials (or force field) are essential. We develop machine learning potentials based on spectral neighbor analysis (SNAP) for doped amorphous systems (ZrO2-doped Ta2O5, TiO2-doped GeO2). Mechanical spectroscopy is used to simulate the stress responses from applied strains, which allows us to calculate the mechanical loss Q-1 from molecular dynamics (MD) simulations. The calculated mechanical losses increase as the temperature increases at high frequencies (> 109Hz). At 1kHz, the low-frequency power-law extrapolation Q-1 is 4x10-3 at 300K and 5x10-4 at 50K for 50% ZrO2-doped Ta2O5. The mechanical loss dependence of the doping is also studied to find the optimal dopant and doping concentration. |
Monday, March 6, 2023 8:48AM - 9:00AM |
A61.00003: Does fluid structure encode predictions of glassy dynamics? Tomilola Obadiya, Daniel M Sussman Understanding and predicting the failure, flow, and rearrangement dynamics of amorphous solids has greatly benefited from data-driven methods for correlating local structures with dynamical features. Some of these approaches, such as the "Softness" method based on linear Support Vector Machines, have uncovered combinations of local structural characteristics that predict energy barriers to particle rearrangements in supercooled fluids based on a particle's local environment. The Softness method also was shown to predict the onset temperature of dynamical heterogeneity by estimating the temperature above which local structures are no longer predictive of dynamical activity. In this talk we implement a transfer learning approach and first show that simple classifiers can be trained to predict dynamical activity even well above the onset temperature. We then demonstrate that applying these classifiers to data from the supercooled phase yields results that are nearly identical to those obtained by softness in terms of the physical information about the relationship between local structures and energy barriers. We further show that the predicted onset temperature is independent of the training temperature, for training temperatures both above and below the onset temperature itself. |
Monday, March 6, 2023 9:00AM - 9:12AM |
A61.00004: Raman Spectra and Structure Analysis of LIGO Coating Amorphous Oxides Rui Zhang, Jun Jiang, Alec S Mishkin, James N Fry, Riccardo Bassiri, Martin M Fejer, Hai-Ping Cheng Ti-doped GeO2 amorphous oxides are a promising candidate for mirror coatings in near future LIGO experiments. Raman spectroscopy is sensitive to local vibrations of structures with short- and medium-range order found in glasses. To explore local structure we calculate Raman spectra of Ti doped GeO2 samples with various doping percentages ranging from 10% to 50% via first-principles density functional theory. Structure analysis applied to those samples enables us to relate each Raman peak with particular atomic movements. Differences in Raman spectra of annealed and as-deposited samples show the changes in local structure for samples prepared using different methods. A few machine learning techniques are also implemented, with varying success, to identify the structural features important in Raman calculations. |
Monday, March 6, 2023 9:12AM - 9:24AM |
A61.00005: Accurate identification of basins of attraction in jammed and glassy systems Praharsh Suryadevara, Mathias Casiulis, Stefano Martiniani The mapping of liquid configurations to their inherent structures has been a critical component of |
Monday, March 6, 2023 9:24AM - 9:36AM |
A61.00006: Amorphous Carbon and the Importance of Hybridization on Thermal Properties Paul Desmarchelier, Jean-Yves Raty, Valentina M Giordano, Konstantinos Termentzidis
|
Monday, March 6, 2023 9:36AM - 9:48AM |
A61.00007: Low energy excitations in Mean Field Spin Glasses at zero temperature Flavio Nicoletti, Federico Ricci-Tersenghi, Silvio Franz, Giorgio Parisi, Cosimo Lupo The problem of low energy excitation at low temperatures of glassy system has arisen a great deal of interest in the last decades. The vibrational density of states of glassy systems (VDOS) is found to follow a seemingly universal quartic law approaching zero frequency. The corresponding eigenmodes are found to be localised. When phonons are present, these excitations are in excess with respect to the Debye prediction. Given the crucial importance of the soft modes of excitations for the low temperatures physics, it is most welcomed to achieve a theoretical understanding of this phenomenon. |
Monday, March 6, 2023 9:48AM - 10:00AM |
A61.00008: Size and quality of quantum mechanical data-set for training Neural Network Force Fields for liquid water Márcio S Gomes-Filho, Alberto Torres, Alexandre R Rocha, Luana Pedroza Water is arguably the most important substance on Earth, however, there are still some of its properties that are not yet fully understood. Atomistic simulations have shown to be an important tool, providing one way to improve our comprehension of water. In particular, quantum mechanical simulations seem to be the most appropriate choice, since they have, by construction, an accurate predictive potential. Therefore, ab initio molecular dynamics (AIMD) has the accuracy of Density Functional Theory (DFT), and thus is limited to small systems and relatively short simulation time. In this scenario, Neural Network Force Fields (NNFF) have an important role, since it provides a way to circumvent these caveats. In this work we investigate NNFF designed at the level of DFT to describe liquid water, focusing on the size and quality of the training data-set considered. We show that structural properties are less dependent on the size of the training data-set compared to dynamical ones (such as the diffusion coefficient), and a good sampling (selecting data reference for training process) can lead to a small sample with good precision. |
Monday, March 6, 2023 10:00AM - 10:12AM |
A61.00009: Atomic insights into fibril elongation: beyond dock-and-lock mechanisms Sharareh Jalali, Cristiano L Dias An amyloid fibril can grow for several micrometers in length while preserving the structure of its tips with atomic fidelity. This phenomenon, which determines the fate of almost all amyloid peptides in diseases like Alzheimer’s, is poorly understood. In particular, it remains unknown how sticky regions of the fibril surface affect the kinetics of growth and what are molecular forces enabling peptides to target the fibril tip with high fidelity. Here, we perform all-atom molecular dynamics simulations of the amphipathic Ac-(FKFE)2-NH2 peptide in explicit solvent to study the atomic mechanisms accounting for fibril growth. In these large-scale simulations, peptides are attracted to either non-polar regions at the surface of a pre-formed fibril or to the tip of this fibril. When the peptide binds to the fibril tip, it remains bound for several microseconds, never detaching from it during the simulation. However, with increasing temperature, peptides desorb more promptly from non-polar regions of the fibril surface. At high temperatures, these detached peptides always end up locked onto the fibril tip in less than one μs. In simulations performed with several peptides (conc. ~ 19 mM), peptides accumulate at non-polar regions of the fibril surface, where they nucleate new fibrils. The latter phenomena, known as secondary nucleation, gives rise to fibrils that tend to grow perpendicularly to the pre-formed fibril. A detailed description of the pathways and forces driving these phenomena will be discussed in this presentation. |
Monday, March 6, 2023 10:12AM - 10:24AM |
A61.00010: Simulating the insertion dynamics of an anionic model protein into a cationic triblock copolymer membrane Sylvia M Luyben Experiments suggest that the reconstitution of anionic proteins into synthetic, cationic triblock copolymer membranes proceeds spontaneously through a charge-mediated mechanism. This mechanism is yet to be elucidated. We recently developed a dynamical self-consistent field theory for the self-assembly of charged polymers in an electrolyte solution. Here, we use this theory to examine the dynamical process whereby an anionic model protein, which we model as a triblock copolymer with charged solvophillic end-blocks, inserts into a self-assembled triblock copolymer membrane with cationic solvophillic end-blocks. By tracking the centres of mass of the two end-blocks of the protein we obtain a detailed description of the stages in the process of protein insertion. In particular, by repeated simulation we generate the distribution of insertion times. We examine how this distribution depends on the charge of both the model protein and the membrane. These results enhance our understanding of the mechanism for charge-mediated protein reconstitution. |
Monday, March 6, 2023 10:24AM - 10:36AM |
A61.00011: PyQMC: an all-Python real-space quantum Monte Carlo module in PySCF William A Wheeler PyQMC is a Python-based package for high accuracy correlated electron calculations using quantum Monte Carlo (QMC) in real space. PyQMC implements variational Monte Carlo (VMC) and fixed node diffusion Monte Carlo (DMC) for ground and excited states of molecules and solids, and supports computation of a number of properties, in particular including one- and two-particle reduced density matrices. |
Monday, March 6, 2023 10:36AM - 10:48AM |
A61.00012: Active Learning of Diffusion Pathways for Machine-Learned Interatomic Potentials Michael J Waters, James M Rondinelli In training machine-learned interatomic potentials, the application of on-the-fly active learning during molecular dynamics simulations as a sampling strategy often struggles to efficiently sample regions of the potential energy surface associated with rare-events such as reaction barriers. These issues are further compounded in chemically heterogeneous environments where there may be many chemical permutations of similar reaction pathways. To remedy this, we explore active learning with less common sampling strategies specifically targeting reaction pathways, saddle points, and/or diffusion barriers. We use interstitial oxygen and vacancy diffusion in a multi-principal element alloy as use cases owing to their high chemical complexity and technological relevance. |
Follow Us |
Engage
Become an APS Member |
My APS
Renew Membership |
Information for |
About APSThe American Physical Society (APS) is a non-profit membership organization working to advance the knowledge of physics. |
© 2024 American Physical Society
| All rights reserved | Terms of Use
| Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 20740-3844
(301) 209-3200
Editorial Office
100 Motor Pkwy, Suite 110, Hauppauge, NY 11788
(631) 591-4000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 20045-2001
(202) 662-8700