Bulletin of the American Physical Society
APS March Meeting 2019
Volume 64, Number 2
Monday–Friday, March 4–8, 2019; Boston, Massachusetts
Session C21: Exploring Free Energy Landscapes in Biology and Materials Science IIFocus

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Sponsoring Units: DCOMP DBIO DMP GSOFT Chair: Jonathan Whitmer, University of Notre Dame Room: BCEC 157B 
Monday, March 4, 2019 2:30PM  2:42PM 
C21.00001: Direct and universal bounded entropy evaluation in complex simulations Ram Avinery, Roy Beck Complex physical simulations are ubiquitously employed to characterize thermodynamics in diverse systems; freeenergy, or related quantities, are often their goal. While enthalpy is calculable using the apriori choice of interactions (i.e., forcefield, coupling parameters), entropy remains a challenge to quantify directly. Typically, methods for freeenergy estimation are based on thermodynamic relations (e.g., Jarzynski’s equality) rather than the independent statistics of the target ensembles. 
Monday, March 4, 2019 2:42PM  2:54PM 
C21.00002: Predicting shear transformation events in glasses via energy landscape sampling Bin Xu, Michael Falk, Jinfu Li, Lingti Kong Shear transformation (ST) events, as the elementary process for plastic deformation of glasses, are of vital importance to understand the mechanical behavior of glasses. Here, by characterizing firstorder saddle points in the potential energy landscape, we develop a framework to characterize and to predict the triggering (i.e. locations, triggering strains, and local structural transformations under different shear protocols) of ST events. Verification undertaken with a model CuZr glass reveals that the predictions agree well with athermal quasistatic shear simulations. The proposed framework is believed to provide an important tool for developing a quantitative understanding of the deformation processes that control mechanical behavior of metallic glasses. 
Monday, March 4, 2019 2:54PM  3:06PM 
C21.00003: Utilizing finger prints to construct the disconnectivity graph. Deb De, Bastian Schaefer, Santanu Saha, Daniele Tomerini, Stefan A C Goedecker Theoretical studies have identified the dodecahedron of Si_{20} H_{20} as the lowest energy structure 
Monday, March 4, 2019 3:06PM  3:42PM 
C21.00004: Three birds with one stone: reaction coordinate, thermodynamics and kinetics from allatom molecular simulations Invited Speaker: Pratyush Tiwary Many molecular systems involve processes with intertwined spatiotemporal resolutions ranging from femtoseconds to days, making it hard to probe them completely using traditional experimental tools. It has been a holy grail to simulate these in allatom resolution using molecular dynamics methods, but these can go up to only a few hundred microseconds even with the most powerful and custombuilt supercomputers. Thankfully, over the decades several sampling algorithms have been proposed that can simulate these complex systems in an accelerated but controllable manner. However, a large class of these methods (arguably all!) need an a priori sense of a lowdimensional reaction coordinate (RC) even before performing the sampling. This has severely limited the usefulness of such sampling methods. In order to deal with this cyclic problem where one needs extensive sampling of the rare events to know the RC, but also needs to know the RC in the first place to perform sampling, it is thus extremely desirable to construct methods that learn the RC as they perform the sampling. Here we will describe two such methods, namely SGOOP [1,2] and RAVE [2] developed by us that use flavors of statistical mechanics and deep learning to solve this problem. We will demonstrate the generality and power of these methods by showing how they give direct predictive insight into biologically important problems such as mechanisms of ligandprotein (including T4L99A lysozyme and tyrosine kinases), and transcription factorDNA (including Epstein Barr virus binding domain) interactions. Our findings includes an allatom characterization of metastable and transition states, their stabilities, various rate constants, as well as prediction of deleterious point mutations in the system which could upend the functioning of the protein, DNA or the ligand. 
Monday, March 4, 2019 3:42PM  3:54PM 
C21.00005: Nucleation Kinetics using Generalized Ensemble Simulations Muralikrishna Raju, Deepti Ballal, Xueyu Song The recently developed generalized replica exchange method (gREM)^{ }is employed to sample various coexistence phases efficiently, especially the crystal cluster coexisting with its liquid phase. Namely, the gREM method facilitates comprehensive sampling of phasetransition regions by transforming metastable or unstable energy states in the canonical ensemble to stable ones in the generalized ensemble. Using weighted histogram analysis method (WHAM) analysis, the Gibbs energy as a function of temperature curves can be constructed for the liquid, crystal and various coexisting states. Thus, the nucleation barrier can be extracted without using the classification scheme of liquid and crystal particles. Using the classical nucleation theory, applications to the nucleation kinetics of mW water model and NaCl melt demonstrate that the nucleation rate obtained from our simulation method agrees well with the direct measured nucleation rate from simulations with the same model. 
Monday, March 4, 2019 3:54PM  4:06PM 
C21.00006: Understanding inverted solubility through specialized patchy particle models Irem Altan, Amir Khan, Susan James, Michelle Quinn, Patrick Charbonneau, Jennifer McManus The high specificity and anisotropy of proteinprotein interactions give rise to remarkably rich phase and assembly behaviors. A thorough understanding of these interactions is notably key to forming crystals for protein structure determination. Despite the inherent complexity of these biomolecular systems, coarsegrained patchy models can be used to elucidate the physicochemical processes that govern protein selfassembly. Here, we consider crystal formation in certain mutants of human γDcrystallin, which remarkably form assemblies that become less soluble as temperature increases. We find that using a minimal patchy model with temperaturedeactivated patches recapitulates this inverted solubility trend and provides microscopic insights into the origin of this unusual behavior. This finding provides physical constraints for the observation of retrograde solubility in soft matter more generally. 
Monday, March 4, 2019 4:06PM  4:18PM 
C21.00007: Spikes to Hills: A Continuation of Sticky Hard Sphere Clusters to Long Range Clusters Anthony Trubiano, Miranda HolmesCerfon Energy landscapes for particles with short range interactions are notoriously difficult to explore computationally. To study the sensitivity of these landscapes to parameters of the interaction potential, we start with the most rugged landscape, the set of sticky hard sphere clusters, and use a continuation procedure to evolve the clusters as the range of the potential increases. This procedure captures most local minima of the smoother landscapes, with those missing being mostly high energy clusters. As the potential smoothens, we characterize the merging of clusters by a graph with tree structure. We find these graphs to be insensitive to the interaction strength at short range, but the graphs corresponding to a LennardJones potential vary more than those for a Morse potential at longer range. 
Monday, March 4, 2019 4:18PM  4:30PM 
C21.00008: Surveying the Free Energy Landscape of Attractive Colloidal Spheres Shanghui Huang, Jonathan Whitmer Controlling the assembly of colloidal particles into specific structures has been a longterm goal in the soft materials community. Much can be learned from the process from the selfassembly by examining the early .stage assembly into clusters. For the simple case of hardspheres with shortrange attractions, the smallN rigid structures have been enumerated theoretically and tested experimentally. Less is known, however, about how the free energy landscapes are altered when the interparticle potential is longranged. In this work, we demonstrate how adaptive biasing in molecular simulations may be used to pinpoint shifts in the stability of colloidal clusters as the interparticle potential is varied. We also discuss the generality of our techniques and strategies for applications to related molecule systems. 
Monday, March 4, 2019 4:30PM  4:42PM 
C21.00009: Resolving the Interfaces in C_{60}SubPC Organic Solar Cells Using Molecular Dynamics Simulations Jacob Tinnin, Pengzhi Zhang, Eitan Geva, Barry Dunietz, Margaret Cheung Organic photovoltaic cells (OPVs) are still associated with relative low efficiencies despite recent advances. As the performance depends on the molecular dynamics (MD) and structure, it is crucial to understand this relationship at a quantitative level. To do this we analyzed the wellstudied dyad of boron subphthalocyanine chloride (SubPC) and C_{60} using MD simulations to understand the effects of device fabrication scheme on the materials interfaces. We developed order parameters to resolve the interface at the molecular level. Using importance sampling, we find an additional interfacial geometry over the two primary configurations addressed in the previous studies. In addition, we show that, due to an energy barrier between basins, the population of structures depends on the initial setup which is used to differentiate between the fabrication schemes. We expect that the insight we provide will enhance efforts to design effective OPVs. 
Monday, March 4, 2019 4:42PM  4:54PM 
C21.00010: Bivariate Transition Matrix Monte Carlo Method for Joint Density of States Calculations Yong Hwan Lee, David Yevick While most thermodynamic variables of a statistical system can be evaluated at all temperatures from the density of states, if a phase transition is present, quantities such as Landau free energy and the probability distribution of the order parameter must instead be determined from the joint density of states which is a function of both the energy and a second variable, typically the order parameter. 
Monday, March 4, 2019 4:54PM  5:06PM 
C21.00011: Policyguided Monte Carlo: Reinforcementlearning Markov chain dynamics Troels Bojesen We introduce Policyguided Monte Carlo (PGMC), a computational framework using reinforcement learning to improve Markov chain Monte Carlo (MCMC) sampling. The methodology is generally applicable, unbiased and opens up a new path to automated discovery of efficient MCMC samplers. After developing a general theory, we demonstrate some of PGMC's prospects on an Ising model on the kagome lattice, including when the model is in its computationally challenging kagome spin ice regime. Here, we show that PGMC is able to automatically machine learn efficient MCMC updates without a priori knowledge of the physics at hand. 
Monday, March 4, 2019 5:06PM  5:18PM 
C21.00012: Integrating cluster algorithms and transition matrix methods David Yevick, Yong Hwan Lee The transition matrix procedure accumulates in a single matrix all accepted and rejected transitions generated during biased sampling of statistical systems, increasing the accuracy of calculations of the density of states relative to standard methods that ignore rejected transitions. However, the efficiency of the transition matrix algorithm is limited by the requirement that the system realizations adequately sample the entire physically accessible configuration space. In the Ising model, the slow diffusion of the single spinflip procedure through this space severely limits the computation speed, especially for large systems. 
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