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 N04: Chemical Physics at the Middle Scales of Soft Matter IFocus
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Sponsoring Units: DCP Chair: Aurora Clark, Washington State University Room: Room 127 |
Wednesday, March 8, 2023 11:30AM - 12:06PM |
N04.00001: Elucidating the mechanisms of synthesis of zeolites using data science and molecular simulation Invited Speaker: Valeria Molinero Zeolites are porous silicates that constitute the main solid catalysts used by the chemical industry. These structurally complex solids are synthesized from aqueous solutions through a multi-stage process that involves multiple phase transformations mediated by the chemistry of polymerization of silica. Organic cations, typically tetraalkylammonium ions, are used to direct the synthesis towards specific zeolite polymorphs. Nevertheless, the molecular mechanisms by which the cations and silicates form the zeolites are not well understood. This presentation will discuss our current work using molecular simulations and machine learning to elucidate what is the smallest size of nanozeolite that can be synthesized, and at which stage zeolitic order emerges from the synthesis mixture, the roles of nucleation and growth in the selection of zeolite polymorphs. |
Wednesday, March 8, 2023 12:06PM - 12:18PM |
N04.00002: Novel analysis of structural dynamics of small bimetallic clusters Darya Aleinikava, Julius Jellinek Results of a computational study of the complexities of structural dynamics in small bimetallic clusters based on fitted semiempirical potentials will be presented and analyzed. These dynamics are much more complex than those of their bulk analogs. Particular attention will be paid to the added intricacy of the dynamics that stems from the two-component nature of the systems. The characterization and analyses will be performed in terms of descriptors such as mixing energy, mixing coefficient, and coordination numbers – both component-specific and global – that will be shown to be sensitive gauges of the changes in the structural dynamics as a function of time at different fixed values of the energy of the systems. The range of covered energies is broad enough to encompass the entire spectrum of the multistage transitions involved in the phase-like transformation from the solidlike to the liquidlike state of the clusters. |
Wednesday, March 8, 2023 12:18PM - 12:30PM |
N04.00003: A nonadiabatic generalized-dividing-surface instanton rate theory Rhiannon A Zarotiadis, Joseph E Lawrence, Jeremy O Richardson
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Wednesday, March 8, 2023 12:30PM - 1:06PM |
N04.00004: Middle Scales of Complex Materials Invited Speaker: Rigoberto Hernandez We know the governing equations for atoms, though we can't necessarily keep track of every atoms or describe how they assemble into molecules, aggregates, and even larger structures of a cell. While Feynman recognized that there was a lot of room—that is combinatoric complexity—at the atomic scale, we see that the combinatorics increase quickly at the middle scales where a myriad of heterostructures (of sub micron size) have been assembled and aggregated. We report on our work to characterize and predict properties of sustainable nanoparticles that have emergent function at the middle scales, and characterizable function leading to non-toxic exposure in the environment. We will also report our findings in designing polymer-networked nanoparticle arrays which exhibit computing functions at the middle scales. |
Wednesday, March 8, 2023 1:06PM - 1:18PM |
N04.00005: Realizability of Iso-$g_2$ Processes via Effective Pair Interactions Haina Wang, Frank H Stillinger, Salvatore Torquato An outstanding problem in statistical mechanics is the determination of whether prescribed functional forms of the pair correlation function $g_2(r)$ [or equivalently, structure factor $S(k)$] at some number density $ ho$ can be achieved by $d$-dimensional many-body systems. We study the realizability problem of the nonequilibrium iso-$g_2$ process, i.e., the determination of density-dependent effective potentials that yield equilibrium states in which $g_2$ remains invariant for a positive range of densities. Using a precise inverse methodology that determines effective potentials that match $g_2(r)$ at all $r$ and $S(k)$ at all $k$, we show that the unit-step function $g_2$, which is the zero-density limit of the hard-sphere potential, is numerically realizable up to the packing fraction $phi=0.49$ for $d=1$. For $d=2$ and 3, it is realizable up to the maximum ``terminal'' packing fraction $phi_c=1/2^d$, at which the systems are hyperuniform. For $phi |
Wednesday, March 8, 2023 1:18PM - 1:30PM |
N04.00006: A differential lithium isotope effect on amorphous calcium phosphate formation at the nanoscale Manisha Patel, Joshua Straub, Matt Helgeson, Matthew Fisher, Mesopotamia Nowotarski Calcium phosphate plays an important and multi-faceted role in biological systems, ranging from mitochondrial signaling pathways to biomineralization processes such as bone growth. More recently, symmetric calcium phosphate nanoparticles known as Posner molecules have been theorized to serve as a putative ‘neural qubit’ via protected phosphorus nuclear spin states in a biological environment. Thus, understanding calcium phosphate nucleation and aggregation is key to testing the theory for Posner-mediated quantum activity in the brain. Here, we demonstrate using a combination of in vitro experimental methods that the prenucleation and aggregation behavior of amorphous calcium phosphate is differentially influenced by two isotopes of lithium, 6Li and 7Li. The two isotopes have nearly identical chemical behavior in solution, suggesting a quantum mechanical effect on calcium phosphate nucleation and aggregation. We situate this differential isotope effect within the context of calcium phosphate phase behavior, which helps to isolate pathways in which quantum mechanics may be at play. We expect that our results will help explain lithium isotope effects seen in vivo in various contexts, and advance our understanding of a wider range of nonclassical isotope effects in biology. |
Wednesday, March 8, 2023 1:30PM - 1:42PM |
N04.00007: Deep Learning Potential Molecular Dynamics Simulation of Chemical Conversion-Absorption Coupling of CO2 at Air-Reactive Deep Eutectic Solvent Interface Yuhua Duan, Manh Tien Nguyen, Qing Shao, Yueh-Lin Lee, Fan Shi Capturing CO2 from air is urgent to battle against the climate change. Traditional CO2 capture by alkaline solution or aqueous amine has high regeneration cost, equipment corrosion, and amine leakage. Deep eutectic solvents (DESs) are promising alternatives with low corrosion, non-toxicity, and biodegradable nature. Functionalized DESs can capture and convert CO2 at low partial pressures. Developing reactive DESs for efficient CO2 capture needs a foundational understanding of the reaction-transport coupling of CO2 at the air-DES interface. In this study, we develop a deep learning potential (DLP) to investigate the absorption and reaction of CO2 at the air-DES interface (DES: 1-ethyl-3-methylimidazolium 2-cyanopyrrolide ([Emim][2-CNpyr]) and ethylene glycol (EG)). The DLP model allows us to simulate chemical reactions at a lower computational cost with ab initio accuracy. By analyzing reaction free energy surfaces, molecular interactions among CO2, DES components and reaction products, and transport of CO2 through the interface, we identify three main reaction pathways of CO2 at the interface: forming carboxylate with [Emim], carbamate with [2-CNpyr], and carbonate with EG. The mechanistic understanding of CO2 chemisorption at the interface will facilitate the development of novel direct air capture technology using DESs. |
Wednesday, March 8, 2023 1:42PM - 1:54PM Author not Attending |
N04.00008: Atomic graph-based symmetry recovery for machine learning force fields. Anton Charkin-Gorbulin, Igor Poltavsky, Alexandre Tkatchenko Machine-learning force fields (MLFF) based on kernel ridge regression show high accuracy and efficiency for molecules, materials, and interfaces [1,2]. However, the performance of MLFFs greatly depends upon incorporating the physical symmetries of the considered systems. Finding all relevant symmetries becomes a challenging task for large system sizes. Here we develop a data-driven symmetry search method based on molecular graphs for revealing relevant symmetries in molecules and materials. We demonstrate that our approach allows distinguishing atoms with different chemical environments, as well as controlling the accuracy of the MLFF by adjusting the level of symmetry. Effective MLFFs were constructed for complex periodic systems allowing the efficient investigation of defects behavior in CsPbBr3 and the study of the free energy landscape for graphene interface with ethanol, 1,8-naphthyridine, D-histidine, D-alanine, and D-proline. |
Wednesday, March 8, 2023 1:54PM - 2:06PM |
N04.00009: Complete machine learning description of chemical reactions in solution Timothée Devergne, Leon Huet, Théo Magrino, Arthur France-Lanord, Fabio Pietrucci, A. Marco Saitta The projection of a 3N dimensional space onto a low dimensional collective variable (CV) space is one of the bottlenecks of the study of physical transformations. Many machine learning (ML) schemes have been proposed to devise an optimal CV using classical forcefields. These kinds of ML methods are however out of reach for ab initio simulations that would require millions of CPU.h only to produce the training data. Even with an optimal CV, complete ab initio studies of physical transformations are very demanding in computational time, this problem can be solved using machine learning potentials (MLP). Here, we propose to combine a MLP method devised in the team along with a machine learning CV to accurately study the properties of a benchmark chemical reaction in solution with ab initio accuracy and state of the art CV. |
Wednesday, March 8, 2023 2:06PM - 2:18PM |
N04.00010: Improving physically predictive force field by adding many body effect corrections with machine learning Seungwon Jeong, Chang Yun Son The ionic liquids (IL) electrolyte have been utilized in the development of safe and high-performance lithium-ion batteries. As is well known, the important determinant of the performance of a lithium-ion battery is the association of anions and lithium-ions. These properties can be predicted through molecular dynamics simulation, but developing a high-accuracy prediction model remains a challenge due to the problem that the size of lithium ions is too small. Here we developed the machine learning (ML)-based force field by adding the many-body effect correction to the physics-based predictive force field utilizing ML. The ML-based force field significantly improved the accuracy and can be used to capture the dynamics of lithium-ions and clustering of lithium-ions – the anion of IL in lithium/IL mixed electrolyte systems more precisely. Our results can predict the transport properties of lithium/IL and provides novel design path for obtaining high-performance lithium-ion batteries. |
Wednesday, March 8, 2023 2:18PM - 2:30PM |
N04.00011: Interpretation of autoencoder-learned collective variables using Morse-Smale complex: an application on molecular trajectories Shao Chun Lee, Y Z Nonlinear dimension reduction is a key step towards a minimalist yet accurate understanding of physical systems. A number of methods based on data science and machine learning have shown great promise to automate the process. However, the physical meaning of the automatically-discovered collective variables (CVs) is mostly elusive. In this work, we constructed a framework that 1. determines the optimal number of CVs capturing essential molecular motions using an ensemble of hierarchical autoencoders, and 2. interpreted the physical meanings of the CVs learned by a traditional autoencoder with Morse-Smale (MS) complex and sublevelset persistence homology. We demonstrated this approach with several small molecular systems. This work can be considered as an explainable nonlinear dimensionality reduction method. |
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