Bulletin of the American Physical Society
APS March Meeting 2018
Monday–Friday, March 5–9, 2018; Los Angeles, California
Session C38: Advances in Computational Statistical Mechanics and their Applications: Part 3Focus

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Sponsoring Units: DCOMP DCMP GSNP Chair: Markus Eisenbach, Oak Ridge National Lab Room: LACC 501A 
Monday, March 5, 2018 2:30PM  3:06PM 
C38.00001: Fast irreversible Markov chains in statistical mechanics Invited Speaker: Werner Krauth The Markovchain Monte Carlo method is an outstanding computational tool in science. Since its origins, it has relied on the detailedbalance condition and the Metropolis algorithm to solve general computational problems under the condition of thermodynamic equilibrium with zero probability flows. 
Monday, March 5, 2018 3:06PM  3:18PM 
C38.00002: Rapid heterogeneous molecular simulation in time and space with parsemonious domain decomposition scheme Horacio Andres Vargas Guzman, Kurt Kremer, Torsten Stuehn Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, mapping time and space of the different regions of a simulation with different resolution constitutes a new question worth exploring. Mainly, this question arises from the method development viewpoint for the domain decomposition of the molecular dynamics machinery. Here, we introduce the heterogeneous timespatial domain decomposition approach which is a combination of an heterogeneity sensitive spatial domain decomposition with a time evolution average of particles' diffusion domainwise estimated. Within this approach, the spatial domain decomposition is theoretically modeled and results in scalinglaws for the force calculation simulationtime, while timewise the domains are tackled by the communicationtime expended in the parallelization scheme. We explore the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load balancing schemes for archetypical molecular systems. 
Monday, March 5, 2018 3:18PM  3:30PM 
C38.00003: Worldline Quantum Monte Carlo Simulation of the HubbardHolstein Model Bo Xiao, Richard Scalettar, Frederic Hebert, George Batrouni Quantum Monte Carlo (QMC) method is a powerful tool to understand the physics of strongly correlated interacting quantum system. Persistent questions concerning the role of phonons in strongly correlated materials like manganites, cuprates, iron pnictides, and organic superconductors keep the study of electronphonon Hamiltonians at the forefront of research. We use worldline quantum Monte Carlo (WLQMC) to explore the ground state and finite temperature properties of onedimensional manybody systems based on the extended HubbardHolstein Hamiltonian. This method is based on a directspace, imaginarytime representation of the fermion and boson fields, which allows us to describe the regimes with dominant chargedensitywave, superconductivity and metallic behavior. We show the ground state and finite temperature phase diagrams of the HubbardHolstein model as a function of Hubbard U, nearest neighbor interaction V, phonon frequency ω and electronphonon coupling constant λ. We show the simulation results for an extension of the Hamiltonian to a situation where electronphonon interaction is long range. 
Monday, March 5, 2018 3:30PM  3:42PM 
C38.00004: Critical nonequilibrium relaxation in clusterupdate quantum Monte Carlo algorithms and its application to quantum phase transitions Yoshihiko Nonomura, Yusuke Tomita Although improved quantum Monte Carlo (QMC) algorithms are based on the clusterupdate scheme, previous applications of the nonequilibrium relaxation (NER) scheme to QMC calculations were based on the localupdate scheme [1,2] because of “too fast relaxation of the clusterupdate scheme for NER analyses”. Recently we found that the critical NER in cluster algorithms is described by the stretchedexponential simulationtime dependence, not the powerlaw one [3]. In the present study we analyze the Néeldimer quantum phase transition of the S=1/2 columnardimerized antiferromagnetic Heisenberg model on a square lattice with the continuoustime loop algorithm and NER from the isolated dimer configuration. Our estimate of the critical point δ_{c}≈0.9095 (ratio of the strength of dimerized bonds to normal ones is (1+δ):1) is consistent with a recent QMC estimate δ_{c}≈0.90947(3) [4], and the relaxation exponent σ is consistent with that of the threedimensional classical Heisenberg model, σ≈1/2 [5]. 
Monday, March 5, 2018 3:42PM  3:54PM 
C38.00005: Spatially Dependent Thermodynamic Integration: A Method to Compute Chemical Potentials of Dense Fluids and Concentrated Liquid Mixtures Maziar Heidari, Kurt Kremer, Raffaello Potestio, Robinson Cortes Huerto Well established computational methods aiming at calculating chemical potentials rely on inserting test particles in the target system. The increase in density or concentration renders this procedure unfeasible, and the use of more sophisticated sampling techniques becomes inevitable. We propose an alternative strategy based on the Hamiltonian adaptive resolution framework. Here, the molecules of the target system, described with the appropriate resolution, are in physical contact with a reservoir of molecules modeled as ideal gas particles. To enforce a uniform density profile across the simulation box, a singlemolecule external potential is computed, applied and identified with the excess chemical potential of the target system. We validate the method by computing chemical potentials of various molecular liquids, including aqueous solutions of sodium chloride. 
Monday, March 5, 2018 3:54PM  4:06PM 
C38.00006: ForwardFlux Sampling with Jumpy Order Parameters Amir HajiAkbari Forwardflux sampling (FFS) [1] is a path sampling technique that has gained popularity in recent years, and has been used to compute rates of rareevent driven phenomena such as crystallization, condensation, hydrophobic evaporation and protein folding. The popularity of FFS is not only due to its ease of implementation, but also because of its lack of sensitivity to the particular choice of an order parameter. The order parameter utilized in conventional FFS, however, still needs to satisfy a stringent smoothness criterion in order to assure sequential crossing of FFS milestones. This condition is usually violated for order parameters utilized for describing aggregation phenomena such as crystallization. Here, we present a generalized FFS algorithm for which this smoothness criterion is no longer necessary, and apply it to compute homogeneous crystal nucleation rates in several systems. Our numerical tests reveal that conventional FFS can underestimate rate by several orders of magnitude. 
Monday, March 5, 2018 4:06PM  4:18PM 
C38.00007: GibbsSampling Enhanced ReplicaExchange Simulations Thomas Vogel, Danny Perez We recently introduced a novel replicaexchange scheme in which an individual replica can sample from states encountered by other replicas at any previous time by way of a global configuration database, enabling the fast propagation of relevant states through the whole ensemble of replicas. This mechanism depends on the knowledge of global thermodynamic functions which are measured during the simulation and not coupled to the heat bath temperatures driving the individual simulations. Therefore, this setup also allows for a continuous adaptation of the temperature set. In this talk, we will review the new scheme and demonstrate its capability. The method is particularly useful for the fast and reliable estimation of the microcanonical temperature T(U) or, equivalently, of the density of states g(U) over a wide range of energies. 
Monday, March 5, 2018 4:18PM  4:30PM 
C38.00008: Accelerated histogramfree multicanonical Monte Carlo algorithm for the basis expansion of density of states Ying Wai Li, Alfred Farris, Markus Eisenbach We propose a novel Monte Carlo algorithm to obtain the density of states (DOS) in the form of a basis expansion, for physical systems with continuous state variables [1]. Our algorithm inherits the strengths of various previous methods such as multicanonical sampling and WangLandau sampling. Additionally, we advance the capability of these methods: Firstly, instead of a numerical array of finite resolutions, our algorithm obtains a basis set expansion for the DOS, with the number of terms and coefficients determined and refined iteratively during the simulation. Secondly, since the visited energies are stored directly in a data set instead of a histogram, this reduces the undesirable statistical noise and errors caused by the discretization of state observables. Thirdly, as the random walkers are directed to achieve uniform sampling of the phase space, our scheme is more efficient and we have demonstrated an order of magnitude of speedup compared to existing methods. We will show how this method is applied to accelerate the simulation of materials properties. 
Monday, March 5, 2018 4:30PM  4:42PM 
C38.00009: Random number generators for the largescale MonteCarlo simulations Lev Shchur, Lev Barash, Maria Guskova

Monday, March 5, 2018 4:42PM  4:54PM 
C38.00010: Inverse Design of PressureInduced Solid—Solid Transitions in Colloids Using the Alchemical Ensemble Xiyu Du, Greg Van Anders, Paul Dodd, Julia Dshemuchadse, Sharon Glotzer Recent developments in anisotropic particle synthesis have shown promise for using these particles as building blocks for functional materials. However, due to the large design spaces that are available to us, it can be challenging to find appropriate building blocks for target behaviors. Here, instead of mapping out the phase behavior for a range of particle shapes, we present a new, alternative computational statistical mechanical approach that couples together multiple systems within the “alchemical ensemble” — a generalized, statistical mechanical framework in which model attributes are allowed to fluctuate along with thermodynamic variables. The alchemical ensemble has application to a wide variety of design problems within statistical mechanics. Here, we demonstrate its use in the design of particle shapes for target materials properties, and present examples of candidate colloidal and nanoparticles designed within the alchemical ensemble specifically for structurally reconfigurable colloidal crystals. 
Monday, March 5, 2018 4:54PM  5:06PM 
C38.00011: A random walk in function space: Statistical optimization of functional forms for physical models Markus Eisenbach, Ying Wai Li For computer simulations of physical systems, it is necessary to obtain models that represent the underlying physics, while reducing the computational complexity required for its evaluation. Traditionally, this model construction has been a very timeconsuming task. We present a method that allows us to automatically derive symbolic forms of model Hamiltonians from reference data that can be obtained from higher accuracy calculations, such as electronic structure methods and from experimental input. Our method is based on Gene Expression Programming (GEP) [1] techniques to sample the space of possible functional forms of classical Hamiltonians by statistical sampling of symbolic representations that guarantee wellformed expressions within a genetic algorithm. We compare the original GEP method to our twostep approach that separates functional and parameter optimization for test cases of function fitting and reproduction of pairpotentials and we show the performance improvements due to this decomposition of search spaces. 
Monday, March 5, 2018 5:06PM  5:18PM 
C38.00012: Identification of Liquidlike and Gaslike Particles in Supercritical Fluid via Machine Learning Approach Min Young Ha, Won Bo Lee Abrupt phase transition between vapor and liquid terminates at the liquidgas critical point, where a single phase of supercritical fluid (SCF) emerges. In this work, adopting machine learning techniques, we propose a novel viewpoint that SCF is an inhomogeneous mixture of liquidlike and gaslike particles. We trained a neural network with local structure data of vapor and liquid particles, generated by molecular dynamics simulations in the nearcritical condition; the neural network was trained to label individual particles as 'liquidlike' or 'gaslike'. The trained neural network was then used to identify the coexisting liquidlike and gaslike particles in SCF. The proportion of gaslike particles showed a welldefined dependence on bulk density, agreeing well with the prediction from twostate thermodynamics of interchangeable particles. From the distributions of liquidlike and gaslike particles, we present new explanations on some important properties of SCF, including the local density augmentation and divergent partial molar volume in supercritical solution. 
Monday, March 5, 2018 5:18PM  5:30PM 
C38.00013: Computational Design of Microfabricated Thermionic Converters Peter Scherpelz, Arvind Kannan, Stephen Clark, HsinI Lu, Jason Parker, Max Mankin Thermionic generators are a promising alternative to traditional mechanical generators for heattoelectricity conversion. Here we discuss the use of computational studies for the design and analysis of high efficiency and high power density microfabricated thermionic converters. Our calculations use kinetic particleincell electron dynamics simulations to capture nonequilibrium thermodynamic properties. We demonstrate quantitative agreement between predicted and experimentally measured properties of the thermionic devices, and discuss how simulations can be used to study device performance. This approach is widely applicable to vacuum electronic devices that incorporate charged particle optics. 
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