Bulletin of the American Physical Society
APS March Meeting 2019
Volume 64, Number 2
Monday–Friday, March 4–8, 2019; Boston, Massachusetts
Session P16: Aqueous Solutions, Solvated Interfaces, and Ionic Polarization IIIFocus
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Sponsoring Units: DCOMP DCP Chair: Marivi Fernandez Serra, Stony Brook University Room: BCEC 155 |
Wednesday, March 6, 2019 2:30PM - 3:06PM |
P16.00001: The Role of Interfaces for Chemical Transformations and Transport under Confinement Invited Speaker: Teresa Head-Gordon Chemical transformations, selectivity, and transport rarely occur in a single homogeneous aqueous phase, but instead occur in niches, crevices, and impurity sites at confining interfaces between two or more phases of gases, liquids or solids. The effects of confinement are ubiquitously present across diverse fields spanning nanochemistry and chemical catalysis, environmental and energy sciences, geosciences, and functional materials. Fundamentally, confinement at interfaces alters water and solution compositions and phases to reformulate the thermodynamics of selectivity, transition states and pathways of chemical reactions, nucleation events, and kinetic barriers for transport. I will provide three different examples of theoretical studies of confinement around anhydrous clays, synthetic enzymes, and a general non-equilibrium phenomena of confinement which we refer to as dynamical inversion of the energy landscape. |
Wednesday, March 6, 2019 3:06PM - 3:42PM |
P16.00002: Water/mineral interfaces: Structure and Dynamics from Nonlinear Vibrational Spectroscopy and Ab Initio Molecular Dynamics Invited Speaker: Luana Pedroza Water–mineral interfaces are important for several environmental, industrial, biological, and geological processes. In particular, gypsum is a widespread mineral of high technological, medical, and environmental relevance, but little is known about its surface structure and its interaction with water. A molecular-level understanding of gypsum/water interface is given here by a combined experimental/theoretical study. We investigate the structure and dynamics of water adsorbed from vapor on the gypsum (010) single-crystal surface at room temperature, combining sum-frequency generation (SFG) vibrational spectroscopy experiments and ab initio molecular dynamics (AIMD) simulations. The theoretical results corroborate the experimental ones and provide an accurate atomic characterization of the surface structure. |
Wednesday, March 6, 2019 3:42PM - 3:54PM |
P16.00003: Efficient calculation of level alignment at weakly coupled molecule-metal interfaces using substrate screening within the GW approach Zhenfei Liu, Felipe Da Jornada, Steven G. Louie, Jeffrey B Neaton The physics of level alignment at molecule-metal interfaces can often be accurately captured by the ab initio GW approach. However, the computational cost for such GW calculations for typical interfaces is significant, given their large system size and chemical complexity. In the past, approximate self-energy corrections constructed from image-charge models have been used to compute level alignment with good accuracy. However, this approach neglects dynamical effects of the polarizability and requires the definition of an image plane. In this work, we propose a new approximation for GW calculations of molecule-metal interfaces, where we greatly simplify the evaluation of the polarizability of the combined system. This is done by first computing the polarizability of each individual system in smaller cells, followed by unfolding and interpolation techniques to efficiently combine these quantities. Overall, this approach greatly reduces the computational cost for GW calculations of level alignment without sacrificing the accuracy. Moreover, this approach captures both dynamical and nonlocal polarization effects without the need to invoke a classical image charge expression. We benchmark our approximation for the case of a benzene molecule physisorbed on Al(111) surface. |
Wednesday, March 6, 2019 3:54PM - 4:06PM |
P16.00004: Dielectric dependent hybrid functionals for surfaces and interfaces Huihuo Zheng, Marco Govoni, Giulia Galli We present a dielectric dependent hybrid density functional which accurately describes the electronic properties of heterogeneous interfaces, as well as those of three- and two-dimensional bulk solids. The functional is constructed by generalizing the dielectric hybrid functional for solids proposed in Ref. [1] to include a spatially varying, local dielectric function. The latter is determined self-consistently using a finite field approach. We present results for the band gap and dielectric constants of bulk materials (2D and 3D), and band offsets for interfaces (Si/Si3N4, Si/H2O) and surfaces (H-Si), as obtained with the Qbox code [2]. |
Wednesday, March 6, 2019 4:06PM - 4:18PM |
P16.00005: Direct Z-Scheme Water Splitting Photocatalyst Based on Two-Dimensional Van Der Waals Heterostructures Ruiqi Zhang, Lili Zhang, Qijing Zheng, Pengfei Gao, Jin Zhao, Jinlong Yang Mimicking the natural photosynthesis in plants, Z-scheme water splitting is a promising strategy to improve photocatalytic activity. Searching for the direct Z-scheme photocatalysts is urgent and the crucial factor for the photocatalytic efficiency is the photogenerated electron–hole (e–h) recombination rate at the interface of two photosystems. In this report, based on time-dependent ab initio nonadiabatic molecular dynamics (NAMD) investigation, we first report a two-dimensional (2D) metal-free van der Waals (vdW) heterostructure consisting of monolayer BCN and C2N as a promising candidate for direct Z-scheme photocatalysts for water splitting. It is shown that the time scale of e–h recombination of BCN/C2N is within 2 ps. NAMD simulations based on frozen phonon method prove that such an ultrafast interlayer e–h recombination is assisted by intralayer optical phonon modes and the interlayer shear phonon mode induced by vdW interaction. In these crucial phonon modes, the interlayer relative movements which are lacking in traditional heterostructures with strong interactions, yet exist generally in various 2D vdW heterostructures, are significant. Our results prove that the 2D vdW heterostructure family is convincing for a new type of |
Wednesday, March 6, 2019 4:18PM - 4:30PM |
P16.00006: Volume-Dependent Atomic Polarizabilities for Vibrational Spectroscopy Mark DelloStritto, Ruiyu Wang, Michael L Klein, Eric U Borguet
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Wednesday, March 6, 2019 4:30PM - 4:42PM |
P16.00007: Transferability of local density assisted implicit solvation models for homogeneous fluid mixtures David Rosenberger, Tanmoy Sanyal, M. Scott Shell, Nico Van der Vegt Dependency on density or concentration of the state chosen during parametrization leads to low transferability in density/concentration space in bottom-up coarse graining. |
Wednesday, March 6, 2019 4:42PM - 4:54PM |
P16.00008: Molecular Force Fields with Gradient-Domain Machine Learning: Dynamics of Small Molecules with Coupled Cluster Forces Huziel Sauceda, Stefan Chmiela, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko Molecular dynamics (MD) simulations using conventional force fields constitute the cornerstone of contemporary atomistic modeling in biology, chemistry, and materials science. However, the predictive power of these simulations is only as good as the underlying interatomic potential. Here we present the reconstruction of molecular force fields for small molecules using the recently developed symmetric gradient-domain machine learning (sGDML) approach. The sGDML approach faithfully reproduces complex high-dimensional potential-energy surfaces from just a few 100s of molecular conformations generated by ab-initio MD simulations. The data efficiency of the model allows employing high-level wavefunction-based atomic forces and energies for training, such as the “gold standard” CCSD(T) method. We demonstrate that the flexible nature of this fully data-driven model recovers any local and non-local quantum interaction coming from -F=〈φ*│∂H/∂x│φ〉 (e.g. H-bonding, proton transfer, lone pairs, changes in hybridization states, steric repulsion and n->pi* transitions) without relying on prior knowledge of the phenomena. The analysis of MD@sGDML trajectories yields new qualitative insights into dynamics, chemistry, and spectroscopy of small molecules close to spectroscopic accuracy. |
Wednesday, March 6, 2019 4:54PM - 5:06PM |
P16.00009: Nanosecond-scale ab initio molecular dynamics of fully solvated biomolecules with periodic boundary conditions using the Oak Ridge Leadership Computing Facility (OLCF) supercomputers Ada Sedova, Micholas Dean Smith, Arnold Tharrington, Jeremy Christopher Smith We present nanosecond-scale molecular dynamics simulations of small ribonucleic acids with full solvation shells of explicit water and ions at the density functional theory (DFT) level, using high performance ab initio molecular dynamics programs and the Oak Ridge Leadership Computing Facility (OLCF) supercomputers. Comparison of the trajectories to classical molecular dynamics is performed, including effects of polarization, changes in molecular conformations, and dynamics of the system in metastable states and with respect to barrier crossing. We find important differences in the conformational dynamics and analyze the effects of dynamic changes in charge density and polarization that are available from the first-principles description and which may be essential to correct simulation of conformational ensembles of these difficult-to-model molecules. |
Wednesday, March 6, 2019 5:06PM - 5:18PM |
P16.00010: Advances in machine learned potentials for molecular dynamics simulation Kipton Barros, Nicholas Lubbers, Justin S. Smith Recent machine learning techniques allow emulation of quantum chemistry with stunning fidelity. For example, deep neural networks can now predict molecular properties with accuracy approaching that of coupled cluster theory, at a tiny fraction of the computational cost. We present methods for building machine learned potentials based on the following key ideas: (1) encoding physical symmetries, (2) active learning to dynamically grow the training dataset, and (3) transfer learning to incorporate data from varying sources. The aim is to enable large-scale and highly accurate molecular dynamics simulations, e.g., for chemistry, materials science, and biophysics applications. |
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