Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session S45: Computational Methods for Statistical Mechanics: Advances and Applications IIFocus

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Sponsoring Units: DCOMP GSNP Chair: Nathan Clisby, Swinburne Univ of Tech Room: 706 
Thursday, March 5, 2020 11:15AM  11:51AM 
S45.00001: Progress in stochastic coupled Molecular Dynamics and Spin Dynamics Invited Speaker: Pascal Thibaudeau Coupled Molecular Dynamics and Spin Dynamics display very attractive features that allow to get new insights of the intriguing interplay between structure and magnetism in Febased alloys. 
Thursday, March 5, 2020 11:51AM  12:03PM 
S45.00002: Quantumaccurate multiscale modeling of ramp compressions and magnetoelastic phase transitions in iron Julien Tranchida, Attila Cangi, Mitchell Wood, Aidan Thompson, Michael Paul Desjarlais Magnetic spin fluctuations have a significant impact on the thermodynamic properties of magnetic metals. Accurately predicting magnetostructural phase transitions in compressed iron hence requires accounting for those effects. 
Thursday, March 5, 2020 12:03PM  12:15PM 
S45.00003: Absolute entropy calculation from liquid state correlation functions Michael Widom, Michael Gao Because entropy is a function of thermodynamic state it can be determined without need for thermodynamic integration. Concepts from information theory relate the entropy of a liquid metal to its correlation functions, which can be accurately determined from abinitio molecular dynamics. This relationship is equivalent to the "S2" method that has previously been applied to model liquids. We demonstrate the accuracy of the approach by calculating the absolute entropy of liquid aluminum entirely from first principles, and demonstrate excellent 
Thursday, March 5, 2020 12:15PM  12:27PM 
S45.00004: Absolute free energies from abinitio calculation: application to phase stability of liquid metal alloys Yang Huang, Michael Gao, Michael Widom Absolute liquid state entropies calculated using the "S2" method can be combined with abinitio enthalpies to predict absolute Gibbs free energies. We apply this method to model the phase behavior of the liquid alkali metal alloy LiNa, which is known to phase separate, compared with NaK, which exhibits a eutectic. Our abinitio simulations employ hybrid Monte Carlo/molecular dynamics to accelerate sampling of the ensemble and provide accurate pair correlation functions for the entropy calculation. Both alloys exhibit effective unlikeatom repulsion, but the weaker repulsion of NaK allows an entropically stabilized eutectic, while the stronger repulsion of LiNa overwhelms the entropy below a high temperature critical point which we approximately locate. 
Thursday, March 5, 2020 12:27PM  12:39PM 
S45.00005: Investigation of Fe by means of atomistic spin dynamics coupled with ab initio molecular dynamics simulations Davide Gambino, Bjorn Alling Ab initio molecular dynamics (AIMD) is a mature approach for the investigation of nonmagnetic materials. Magnetism in condensed matter, however, introduces a further challenge for the theoretical community. A recently developed method that allows to take advantage of the accuracy of AIMD and couples the magnetic and vibrational degrees of freedom is the atomistic spin dynamics  ab initio molecular dynamics (ASDAIMD) method [1]. In this approach, the atomic and magnetic dynamics are run in parallel while communicating with each other. The applicability of the method has been shown in the study of CrN, a semiconducting system with well localized magnetic moments. 
Thursday, March 5, 2020 12:39PM  12:51PM 
S45.00006: Spin dynamics simulations on Surface for a nanoscale Heisenberg antiferromagnet Zhuofei Hou Monte Carlo and spin dynamics techniques with fourthorder 
Thursday, March 5, 2020 12:51PM  1:03PM 
S45.00007: Effective diffusion in rough potential energy landscapes Thomas Gray Diffusion in spatially rough, confining, onedimensional energy landscapes is treated using Zwanzig's proposed formalism, which is based upon the Smoluchowski Equation. Disagreement between its predictions and the results of numerical simulations is observed. We use the configurational partition function to amend Zwanzig's formalism, and resolve the disagreement. The analogous overdamped Langevin Equation is proposed, and a numerical simulation scheme offering potentially significant reductions in computational time is derived. The case of random roughness is treated. We then extend the above into higher dimensions and calculate effective diffusion coefficients for motion in nonconfining, rough, multidimensional potential energy landscapes. This leads us to propose an expression for the mean firstpassage time from one potential minimum to any one of the immediately adjacent potential minima. As before, simulation schemes offering significant improvements upon those derived from the unmodified Langevin Equation are obtained. Good agreement between our theory's predictions and both numerical simulation schemes  unmodified and modified  is observed. 
Thursday, March 5, 2020 1:03PM  1:15PM 
S45.00008: Stefan–Maxwell diffusivities extracted from molecular dynamics simulations via Onsager’s regression hypothesis Charles Monroe, Maxim Zyskin We report on our development of a method to measure macroscopic diffusion coefficients in silico by analysing moleculardynamics (MD) data. Application of Onsager’s regression hypothesis to the macroscopic constitutive laws for multicomponent Stefan–Maxwell mass transport allows application of Casimir’s fluctuation theory, which shows how the decay rates of certain autocorrelation functions relate to macroscopic mutual diffusivities. The autocorrelation functions are then determined numerically, using periodic MD simulations in the Gibbs ensemble. As an illustrative example, we compute the diffusivities for a ternary Lennard–Jones gas mixture, a case where the diffusivities measured by MD can be compared to diffusivities determined analytically by the Chapman–Enskog kinetic theory. The ultimate application of this method will be to quantify component activities and transport properties in liquids, with the ultimate aim of studying electrochemical transport properties in electrolytic solutions. 
Thursday, March 5, 2020 1:15PM  1:27PM 
S45.00009: Machine Learning the Effective Hamiltonian in High Entropy Alloys with Large DFT Datasets Xianglin Liu, Jiaxin Zhang, Yang Wang, Markus Eisenbach The development of machine learning sheds new light on the Monte Carlo simulation of complex alloys. One major challenge, however, is that machine learning models are generally datahungry, while the data from density functional theory (DFT) are computationally expensive. To solve this problem, we use the atomic local energy as the target variable, and harness the power of the linearscaling DFT method to obtain large DFT data sets. This method is used to calculate the energy data of a range of MoNbTaW refractory high entropy alloys, with machine learning techniques including kernel ridge regression, Gaussian process, and artificial neural network applied to construct the effective Hamiltonian. The results demonstrate that machine learning model built on the configurational space, which naturally incorporates nonlinear and multisite interactions, can efficiently and accurately predict the DFT energy. 
Thursday, March 5, 2020 1:27PM  1:39PM 
S45.00010: Quantifying the disassembly of viral capsids from a multiscale molecular simulation approach Horacio Andres Vargas Guzman, Christopher Cooper, Adolfo Poma Molecular simulation of large biological systems, such as viral capsids, remains a challenging task in soft matter research. On one hand, coarsegrained (CG) models attempt to make feasible the description of the entire viral capsids. On the other hand, novel development of molecular dynamics (MD) simulation approaches, like enhance sampling which attempt to overcome the time scales required in biophysics. Those methods have a potential for delivering molecular structures and properties of biological systems. Nonetheless, exploring the process on how a capsid disassembles by allatom MD simulations has been rarely attempted. Here, we propose a methodology to analyze the disassembly process of viral capsids quantitatively. In particular, we look at the effect of pH and charge of the genetic material inside the capsid, and compute the free energy of a disassembly trajectory by combining CG simulatiosn to a PoissonBoltzmann solver. We employ such multiscale approach on the triatoma virus as a test case, and find that even though an alkaline environment enhances the stability of the capsid, the resulting deprotonation of the internal solvent generates an electrostatic repulsion that triggers disassembly 
Thursday, March 5, 2020 1:39PM  1:51PM 
S45.00011: Exploring the Potential of ParallelBiasing in Flat Histogram Methods Shanghui Huang, Jonathan K. Whitmer Metadynamics, a member fo the "flat histogram" class of advanced sampling algorithms, has been widely used in molecular simulations to drive exploration of states that are separated by high free energy barriers and promote the sampling of full free energy landscapes. A recently proposed variant, paralled bias metadynamics (PBMetaD) promises to aid in exploration of free energy landscape s along multiple important collective variables by exchanging the ndimensional free energy landscape required by standard methods for n onedimensional marginal free energy landscapes. In this study, we systematically examine how parallel biasing affects convergence of free energy landscapes along each variable relative to standard methods, and the effectiveness of the parallel biasing strategy for addressing common bottlenecks in the use of advanced sampling to calculate free energies. 
Thursday, March 5, 2020 1:51PM  2:03PM 
S45.00012: Superfluidity and dimensional crossover in Quasi1D systems Per Bollmark, Adrian Kantian 1D systems cannot attain longrange order (LRO) but have a wide range of other benefits. One method to cure the missing LRO is to construct an infinite array of weakly coupled 1D systems. 
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