Talks
| Speaker | Affiliation | Topic |
|---|---|---|
|
Shanadeen Begay |
Boston University, USA | Methionine Enkephalin simulation using the Statistical Temperature Molecular Dynamics algorithm |
|
Frédéric Cazals |
National Institute for Research in Computer Science and Control, France |
Multiscale Analysis of Sampled Energy Landscapes |
| Hai-Ping Cheng | University of Florida, USA | |
| Juan Cortés | Laboratory of Analysis and Architecture of Systems, France |
Randomized tree construction algorithm to explore energy landscapes |
|
Johannes Dieterich |
Princeton University, USA | Water cluster optimization |
|
Bernd Engels |
Julius-Maximilians-Universität Würzburg, Germany | New approaches for efficient simulation of complex soft matter |
|
Andreas Fischer |
Technische Universität Chemnitz, Germany | Understanding Preferential Trapping |
| Edwin Flikkema | Aberystwyth University, UK |
Global optimization studies of silica clusters and hydroxylated silica clusters |
| Bernd Hartke |
Christian-Albrechts-Universität zu Kiel, Germany |
|
|
Karl Heinz Hoffmann |
Technische Universität Chemnitz, Germany | Controlled Dynamics on Energy Landscapes |
|
Roy Johnston |
University of Birmingham, UK |
Direct DFT Global Optimization Using a Genetic Algorithm: Comparing Theory and Experiment |
|
Arnulf Möbius |
IFW Dresden, Germany | Structure optimization for models of protein folding via “partial distortion”-quench cycles |
| Mark Oakley | University of Birmingham, UK | |
|
Peter Salamon |
San Diego State University, USA | Free energy landscapes from rate constants |
|
Christian Schön |
MPI for Solid State Research, Germany | Energy Landscapes |
|
Lewis Smeeton |
University of Cambridge, UK | |
| David Wales |
University of Cambridge, UK |
Optimization Methods |
Methionine Enkephalin simulation using the Statistical Temperature Molecular Dynamics algorithm
A series of papers by Kim, Straub and Keyes introduce the statistical temperature molecular dynamics (STMD) algorithm [1], which samples a at distribution of the potential energy, U, and a range of statistical temperatures, T(U), in order to overcome quasi-ergodicity. Previous studies focused on coarse-grained, beta-barrel-forming BLN model proteins without explicit water, obtaining thermodynamic signatures of folding such as cooperativity, inherent structures, and heat capacities[1-3]. The implementation of STMD into CHARMM was done by Naouki Miyashita. Here, STMD-CHARMM studies of the Methionine Enkephalin (Met-Enk) ve-mer peptide, with explicit water, are reported, including distributions of Ramachandran angles, heat capacities, and other properties that show unexplored regions of the energy landscape. Canonical averages, to nd biologically relevant real-temperatures, are obtained using reweighting techniques. A discussion of the folding transition is given in simulations with a range of T(U) from 50K to 800K. It is shown that STMD exhibits improved eciency over other methods in the sampling of low-energy confirmations [4, 7-10, 12]. There is also evidence of greater sampling in particular backbone structuresof each amino acid residue and an attempt at initial comparisons to functionality of this peptide may be realized in future studies [4].
Multiscale Analysis of Sampled Energy Landscapes
In this talk, I shall discuss the problem of describing the topography of a sampled energy landscape. More formally, given a collection of points (conformations) of a physical system, together with an associated energy, I shall describe methods and algorithms addressing two goals. The first one is to identify substitutes for the critical points of any index of the energy function and their connexions (in particular the transition paths minimum - index one saddle - minimum), as well as their (un-)stable manifolds (i.e. their basins). The second one is to perform a stability analysis of these features (prosaically, is a basin ``stable'' or not?).
A proof of concept will be presented on polynomial landscapes in 3 dimensions.
An application will be presented for the analysis of combinatorial energy landscapes associated with the secondary structures of RNA molecules.
I will also present the associated software, which will hopefully stimulate discussions and tests on a variety of systems.
[1] A Heuristic construction of discrete Morse-Smale diagrams based on samplings and bifurcations diagrams, F. Cazals and C. Mueller and C. Robert and A. Roth
[2] Analyzing discrete energy landscapes, with applications to RNA secondary structure prediction, F. Cazals and J. Qin and A. Roth and P.F. Stadler
Barrier distribution functions in glasses
In this work, a new method for exploring conformational energy landscapes is described. The method, called transition-rapidly exploring random tree (T-RRT), combines ideas from statistical physics and robot path planning algorithms. A search tree is constructed on the conformational space starting from a given state. The tree expansion is driven by a double strategy: on the one hand, it is naturally biased toward yet unexplored regions of the space; on the other, a Monte Carlo-like transition test guides the expansion toward energetically favorable regions. The balance between these two strategies is automatically achieved due to a self-tuning mechanism. The method is able to efficiently find both energy minima and transition paths between them. As a proof of concept, the method is applied to two academic benchmarks and the alanine dipeptide.
References:
[1] Randomized tree construction algorithm to explore energy landscapes
New approaches for efficient simulation of complex soft matter
Beside accurate force fields reliable simulations of complex soft matter deserves efficient search algorithms for minima and the connecting paths. In this talk we will give a progress report about our work on force fields including the charge penetration energy term [1]. Here we will concentrate on the description of cation- interaction [2]. We will also discuss progress in our PathOpt method which allows the determination of possible reaction paths between two local minima [3] and new algorithm to generate water spheres around a given solute.
References:
[1] Tafipolsky, M.; Engels B. J. Chem. Theor. Comp. 2011, 6, 1791
[2] Ansorg, K.; Tafipolsky, M.; Engels B. submitted.
[3] Grebner, C.; Pason, L. P.; Engels B. J. Comp. Chem. 2013, ASAP, DOI: 10.1002/jcc.23307.
Understanding Preferential Trapping
Global optimization studies of silica clusters and hydroxylated silica clusters
Silica (SiO2) is a versatile material with many applications in such diverse fields as micro-electronics, chemistry (catalysis) and photonics. Many bulk polymorphs exist such as the dense quartz phase or the more open zeolites. This presentation is about trying to find the optimal geometries of various types of silica (nano-)clusters. The methodology consists of a two-step approach, where classical potentials are used first and promising candidates for the energetic global minimum are subsequently refined using Density Functional Theory (DFT) with the B3LYP functional and a 6-31G** basis set.
In an initial study [1] (SiO2)_N clusters consisting purely of silica were considered. The Basin Hopping global optimization algorithm was used together with a specifically parameterized potential to produce candidate geometries for the energetic global minimum, followed by DFT refinement. The potential used is of the same form as the BKS and TTAM potentials. However, the potential has been re-parameterized to give more accurate results for clusters (rather than the bulk material). Clusters of sizes up to 27 SiO2 units have been considered and global minima were proposed.
A second study [2] focuses specifically on `fully-coordinated' silica clusters, i.e. defectless clusters where every silicon atom is bonded to 4 oxygen atoms and every oxygen atom is bonded to 2 silicon atoms. This fully-coordinated arrangement of atoms is common in bulk silica, whereas for clusters defects tend to occur. Fully-coordinated clusters are expected to have special properties, such as an improved chemical stability, making them possible building blocks for cluster-assembled materials. An algorithm for specifically searching for (low energy) fully-coordinated clusters was developed. This algorithm is based on performing Monte Carlo moves in the space of graphs (rather than in coordinate space), the graph being the network of chemical bonds between atoms. This approach ensures that the clusters remain fully-coordinated during the search. Fully-coordinated clusters of sizes up to 24 SiO2 units are considered. The main purpose of this investigation is to find out how the energetic difference between the lowest-energy fully-coordinated cluster and the lowest energy defective cluster diminishes with cluster size.
Another recent study focuses on hydroxylated silica clusters (SiO2)_M (H2O)_N. Such clusters are more likely to occur in nature and are relevant to understanding the processes involved in the synthesis of zeolites. Here, a simplified version of the potential introduced by Hassanali and Singer is used in combination with the Basin Hopping global optimisation algorithm and DFT refinement. Structural and energetic trends with increasing level of hydroxylation are being studied [4]. This has led to a re-interpretation [3] of an experiment on atomic mixing in hydroxylated silica clusters in solution.
If time permits I could also present recent work on two-dimensional foams.
Application of a genetic algorithm to the global optimization of parameters in the reactive force field ReaxFF
I am going to present our recent work on global GA optimization of parameters of the reactive force field ReaxFF, for several test and application cases. There will be a brief intro to reactive force fields, followed by some aspects of GA implementation, testing/tuning, results and applications (the latter including e.g. silicon and silica, mechanochemistry of disulfides, and photochemical cis<->trans-switching of azobenzene).
Controlled Dynamics on Energy Landscapes
Direct DFT Global Optimization Using a Genetic Algorithm: Comparing Theory and Experiment
Over the past decade, there has been a significant growth in the development and application of methods for performing global optimization (GO) of cluster and nanoparticle structures using first-principles electronic structure methods coupled to sophisticated search algorithms. This has in part been driven by the desire to avoid the use of empirical potentials (EPs), especially in cases where no reliable potentials exist to guide the search toward reasonable regions of configuration space. This has been facilitated by improvements in the reliability of the search algorithms, increased efficiency of the electronic structure methods, and the development of faster, multiprocessor high-performance computing architectures. In this review, we give a brief overview of GO algorithms, though concentrating mainly on genetic algorithm and basin hopping techniques, first in combination with EPs. The major part of the review then deals with details of the implementation and application of these search methods to allow exploration for global minimum cluster structures directly using electronic structure methods and, in particular, density functional theory. Example applications are presented, ranging from isolated monometallic and bimetallic clusters to molecular clusters and ligated and surface supported metal clusters. Finally, some possible future developments are highlighted.
References:
[2] Bismuth-Doped Tin Clusters: Experimental and Theoretical Studies of Neutral Zintl Analogues
[3] Global optimization of clusters using electronic structure methods
Structure optimization for models of protein folding via “partial distortion”-quench cycles
Free energy landscapes from rate constants
Energy Landscapes of Cyclic Peptides
Cyclic tetrapeptides are an important class of biologically active molecules that exhibit interesting conformational dynamics, with slow interconversion of several different structures. We present calculations on their energy landscapes using discrete path sampling. In acyclic peptides and large cyclic peptides, isomers containing cis-peptide groups are much less stable than the all-trans isomers and separated from them by large barriers. Strain in small cyclic peptides causes the cis and trans isomers to be closer in energy and separated by much lower barriers. If d-amino acids or proline residues are introduced, isomers containing cis-peptides become more stable than the all-trans structures. We also show that changing the polarity of the solvent has a significant effect on the energy landscapes of cyclic tetrapeptides, causing changes in the orientations of the peptide groups and in the degree of intramolecular hydrogen bonding.
References:
Energy Landscapes
References:
[1] Finite-time thermodynamics and the gas-liquid phase transition
[2] Free enthalpy landscape of SrO
[4] Ab initio and empirical energy landscapes of (MgF2)n clusters (n = 3, 4)
[6] ‘Entropically’ stabilized region on the energy landscape of an ionic solid
[7] FiniteTime Thermodynamics and the Optimal Control of Chemical Syntheses
Energy Landscape Visualisation
A scheme for visualizing and quantifying the complexity of multidimensional energy landscapes and multiple pathways is presented employing principal component-based disconnectivity graphs and the Shannon entropy of relative “sizes” of superbasins. The principal component-based disconnectivity graphs incorporate a metric relationship between the stationary points of the system, which enable us to capture not only the actual assignment of the superbasins but also the size of each superbasin in the multidimensional configuration space. The landscape complexity measure quantifies the degree of topographical complexity of a multidimensional energy landscape and tells us at which energy regime branching of the main path becomes significant, making the system more likely to be kinetically trapped in local minima. The path complexity measure quantifies the difficulty encountered by the system to reach a connected local minimum by the path in question, implying that the more significant the branching points along the path the more difficult it is to end up in the desired local minimum. As an illustrative example, we apply this analysis to two kinds of small model protein systems exhibiting a highly frustrated and an ideal funnel-like energy landscape.
References:
[1] Topographical complexity of multidimensional energy landscapes
