Bulletin of the American Physical Society
APS March Meeting 2018
Volume 63, Number 1
Monday–Friday, March 5–9, 2018; Los Angeles, California
Session R20: Free Energy Mapping in Biology and Materials Science IFocus
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Sponsoring Units: DCOMP GSNP DBIO GSOFT Chair: Jonathan Whitmer, Univ of Notre Dame Room: LACC 308B |
Thursday, March 8, 2018 8:00AM - 8:36AM |
R20.00001: Using the relative entropy to sample free energy landscapes with transferable coarse-grained models Invited Speaker: M. Scott Shell Coarse-grained molecular models permit quantitative simulations of complex systems with large length and time scales, often enabling free energy calculations that would be difficult or impossible with all-atom models. However, their utility tends to be highly constrained by accuracy over a limited set of state conditions. We discuss a fundamental approach to the identification of accurate coarse models and more generally of emergent physical behavior from many-body systems. The relative entropy measures the information lost upon coarse graining and we hypothesize that its minimization provides a universal variational principle for coarse-graining. This broad statistical-mechanical framework can improve and even detect both analytical and simulation models of complex systems. We discuss conceptual and numerical aspects of this approach. In particular we show recent efforts to create improved coarse grained models with high degrees of transferability suitable for free energy and phase equilibrium calculations, using unconventional interaction potentials that capture multibody effects. |
Thursday, March 8, 2018 8:36AM - 8:48AM |
R20.00002: Addressing Temperature Transferability of structure based Coarse Graining Models David Rosenberger, Nico van der Vegt Systematically derived coarse grained (CG) models for molecular liquids do not inherently guarantee transferability to a state point different from its reference, especially when derived on the basis of structure based CG methods like Inverse Monte Carlo (IMC). Several efforts made in the past years to improve the transferability of these models focused on including thermodynamic constraints or on the application of multistate parametrization. Das and Andersen (DA) proposed a new Ansatz.[1] They derived a correction term added to the system's Hamiltonian to reproduce the virial pressure and the volume fluctuations of the reference system in the CG resolution which does not require further adjustment of the effective pair potential. |
Thursday, March 8, 2018 8:48AM - 9:00AM |
R20.00003: Monte Carlo Sampling with Layered Auxiliary Potentials Michael Webb, Nicholas Jackson, Juan De Pablo In this talk, we describe two new Monte Carlo schemes, Cascading Auxiliary Potential Sampling (CAPS) and Rosenbluth-CAPS, that accelerate the computation of equilibrium system properties. The fundamental premise of both approaches is to generate a Markov chain of configurations for a ``true’’ potential energy surface (PES) using random walks on auxiliary potentials and an appropriate acceptance criterion. The power of the methods derive from the choice of auxiliary potentials, which may reduce the cost of the calculation and/or enable more efficient exploration of phase space; when the auxiliary potentials are equivalent to the ``true’’ PES, the methodologies reduce to standard MC sampling. We demonstrate the application of these schemes and discuss their performance in a series of both toy and molecular systems. It is shown that CAPS and RCAPS are competitive with other sampling methods, such as parallel tempering and metadynamics, but CAPS and RCAPS afford a degree of flexibility that make them uniquely suited to some applications for which other enhanced sampling methods are less obvious. Importantly, because CAPS and RCAPS simply generate configurations according to the Boltzmann distribution, they can be further complemented by existing enhanced sampling strategies. |
Thursday, March 8, 2018 9:00AM - 9:12AM |
R20.00004: Molecular enhanced sampling with autoencoders: On-the-fly nonlinear collective variable discovery and accelerated free energy landscape exploration Wei Chen, Andrew Ferguson Macromolecular and biomolecular folding landscapes typically contain high free energy barriers that impede efficient sampling of configurational space by standard molecular dynamics (MD) simulation. Biased sampling approaches improve sampling by driving the simulation along pre-specified collective variables (CVs) to accelerate exploration of configurational space. The success of these methods critically depends on the availability of good CVs of the system. Nonlinear manifold learning techniques can identify such CVs but typically do not furnish an explicit and differentiable relationship between the CVs and atomic coordinates necessary to perform biased MD simulations. In this work, we employ autoencoders to learn nonlinear CVs that are explicit and differentiable functions of atomic coordinates. By interleaving successive rounds of CV discovery and biased sampling, we establish an approach with capacity to simultaneously discover and directly accelerate along data-driven CVs. We demonstrate our approach in simulations of alanine dipeptide and Trp-cage, and have developed an open source framework available at: https://github.com/weiHelloWorld/accelerated_sampling_with_autoencoder. |
Thursday, March 8, 2018 9:12AM - 9:24AM |
R20.00005: Extracting collective motions underlying nucleosome dynamics via nonlinear manifold learning Ashley Guo, Joshua Lequieu, Joshua Moller, Juan De Pablo The identification of effective collective variables remains a challenge in molecular simulations of complex systems. Here, we use a nonlinear manifold learning technique known as the diffusion map to extract key dynamical motions from a complex biomolecular system, namely the nucleosome: a DNA-protein complex consisting of 147 base pairs of DNA wrapped around a disc-shaped group of eight histone proteins. We show that diffusion maps are effective at extracting collective variables previously found through a detailed free energy analysis in addition to revealing more subtle features involving looping conformations, in which DNA bulges away from the histone complex. This work demonstrates that diffusion maps can be a promising tool for analyzing very large molecular systems and its characteristic slow modes. |
Thursday, March 8, 2018 9:24AM - 9:36AM |
R20.00006: Learning Free Energies from Molecular Simulation using Artificial Neural Networks Hythem Sidky, Jonathan Whitmer The use of adaptive biasing potential methods to estimate free energies controlling phase changes and chemical processes from molecular simulation have become increasingly popular in recent years. The most widely applied method, metadynamics, has gained popularity due to its algorithmic simplicity and the ease with which it is incorporated into molecular dynamics simulation, but it is not without limitations due to its use of Gaussian kernels in the biasing process. Recent improvements have utilized basis function expansions to better match highly nuanced and nonlinear free energy landscapes, but present complications of their own in arbitrary bounded domains or on surfaces with sharp curvature. Here, we report on a recently-developed method which uses artificial neural networks to learn the free energy surface from incomplete data. The method is robust to user-defined parameters and shows dramatic improvement over currently available methods in reproducing the free energy of poorly sampled states. |
Thursday, March 8, 2018 9:36AM - 9:48AM |
R20.00007: Using Free Energy Perturbation to Differentiate ssDNA-Wrapped Single-Walled Carbon Nanotube Complexes Kevin Hinkle, Frederick Phelan Jr. The challenge of separating and purifying single-walled carbon nanotubes (SWCNTs) currently inhibits their widespread application in fields that harness their unique electrical and optical properties. Successful techniques involve dispersion in aqueous media before applying separation protocols such as aqueous two phase extraction to sort the dispersed SWCNTs by their physiochemical properties. Single-stranded DNA (ssDNA) is a very effective dispersant and displays sequence-specific behavior which allows one to tune the separation in favor of particular SWCNT chiralities. The nature of this specificity is not well understood and optimal ssDNA/SWCNT pairs must be searched by costly trial and error. We use molecular simulations and free energy perturbation (FEP) to quantify the differences in the various ssDNA-wrapping species in terms of both loading and sequence. Ultimately, it is our goal to provide insight into the sequence/chirality specific separation mechanism, and to develop a model that allows for more efficient design of the SWCNT purification process. |
Thursday, March 8, 2018 9:48AM - 10:00AM |
R20.00008: Adaptive Enhanced Sampling with FUNN: Force-biasing Using Neural Networks Ashley Guo, Emre Sevgen, Hythem Sidky, Jonathan Whitmer, Juan De Pablo We present an advanced sampling method that efficiently explores phase-space by directly estimating the derivatives of the free energy, also known as the generalized forces, with the aid of a self-regularizing artificial neural network (ANN). The inclusion of an ANN allows a continuous function as an instantaneous estimate of the current free-energy landscape which extends beyond explored regions, providing a significant speed-up over other algorithms that do not generate estimates for unexplored regions. Furthermore, due to the self-regularization scheme, even early, noisy estimates generate accurate, smooth estimates in earlier timepoints. Results from the method are highly transferable, and can be used to expedite sampling in costlier systems by providing estimates from cheaper ones, e.g. a classical molecular dynamics run can be used to provide a starting point for an ab-initio molecular dynamics run, significantly accelerating convergence. This method will be available as an open-source algorithm as a part of Software Suite for Advanced Generalized Ensemble Simulations (SSAGES). |
Thursday, March 8, 2018 10:00AM - 10:12AM |
R20.00009: Path-Accelerated Molecular Dynamics: Parallelizing Molecular Dynamics in Time using Path Integrals Jorge Rosa, Bin Zhang, Thomas Miller Ample availability of parallel computing resources has instigated efforts to accelerate all-atom molecular dynamics (MD) simulations by exploiting their inherent spatial parallelizability. However, as spatial decomposition schemes approach their parallel scaling limits, the need arises for new algorithms that can harness parallel computing architectures in orthogonal and complimentary ways to enable further computational speed-ups and the generation of longer MD trajectories. Here, we use the fact that the transition kernel for the time evolution of a system admits a path-integral representation to introduce an algorithm that employs path-sampling techniques to generate long MD trajectories in short wall-clock times. By employing massively parallelizable and rapidly convergent path-sampling techniques to identify paths for the system to evolve along, we effectively parallelize propagation of the system's equilibrium dynamics with respect to the discretization timestep. We apply the algorithm to simulate various diffusive systems including a Lennard-Jones liquid, and attain hundredfold speedups over a standard Euler integrator in terms of the length of equilibrium trajectory generated per wall-clock time unit. |
Thursday, March 8, 2018 10:12AM - 10:24AM |
R20.00010: ABSTRACT WITHDRAWN
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Thursday, March 8, 2018 10:24AM - 10:36AM |
R20.00011: Metadynamics study of protein crystal nucleation and growth Jens Glaser, Sharon Glotzer Using a coarse-grained, solvent-free rigid body model of a model protein, we |
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