PIM_wd: Module for sampling of the quantum Wigner distribution

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The PIM_wd module implements the exact quantum Wigner probability distribution function sampling algorithm of the Phase Integration Method [1], and is the main subroutine for the quantum correlation function calculations in the PaPIM code. The module samples the thermal Wigner density using a generalised Monte Carlo scheme for sampling phase space points. The scheme combines the Penalty [2] and Kennedy [3] algorithms to sample noisy probability densities. This is necessary because the estimator of the quantum thermal density is not known analytically but must be computed via a statistical average affected by uncertainty. The sampled points are the basis for the calculation of time-independent and time-dependent system observables.

The module was developed as the main component of the PaPIM code, but also as a standalone subroutine that can be easily implemented in other methods (e.g. the whole family of so-called linearised approximations of quantum dynamics) for which phase space sampling of the exact quantum Wigner probability distribution is required. Because the Phase Integration Method samples a set of independent phase space points, independent instances of the PIM_wd module can be run in parallel in order to parallelise the phase space sampling. In the PaPIM package, the parallelisation is accomplished using MPI, which has proved to provide good scalability of the PaPIM code. The module will also be adapted for HTC capabilities.

Practical application and exploitation of the code

The code has been extensively used for the calculation of the infrared absorption spectrum of CH5+ in the gas phase. [4] This highly flexible molecule is considered a standard benchmark of approximate quantum methods, and has experimental interest, for example, in the context of green chemistry.

This module is part of the modules in deliverable D3.3 which were developed during the E-CAM ESDW7.

 

[1] M. Monteferrante, S. Bonella, G. Ciccotti Quantum dynamical structure factor of liquid neon via a quasiclassical symmetrized method J. Chem. Phys. 138 (2013) 054118

[2] D. M. Ceperley, M. Dewing The penalty method for random walks with uncertain energies J. Chem. Phys. 110 (1999) 9812

[3] A. D. Kennedy, J. Kuti Noise without Noise: A New Monte Carlo Method Phys. Rev. Lett. 54 (1985) 2473

[4] O. Asvany, P. K. P, B. Redlich, I. Hegemann, S. Schlemmer, D. Marx Understanding the infrared spectrum of bare CH5+ Science 309 (2005) 1219

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Coarse-Graining module, a Component of the Hierarchical Equilibration Strategy for Polymer Melts

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To study the properties of polymer melts by numerical simulations, equilibrated configurations must be prepared. However, the relaxation time for high molecular weight polymer melts is huge and increases, according to reptation theory, with the third power of the molecular weight. Hence, an effective method for decreasing the equilibration time is required. The hierarchical strategy pioneered in Ref. [1] is a particularly suitable way to do this. The present module provides a part of that method.

To decrease the relaxation time, microscopic monomers are coarse-grained (CG) by mapping each subchain with N_{b} monomers onto a soft blob. The CG system is then characterized by a much lower molecular weight and thus is equilibrated quickly. The present module provides a python script which performs this coarse-graining procedure. The implementation details can be seen in the module’s documentation on our software Library here. This module is part of a set of codes that together implement the Hierarchical Equilibration strategy of Ref. [1], in the ESPResSO++ [2] (for the complete list of modules, see here under ESPResSO++).

 

Practical application and exploitation of the code

The development of a multiscale method for polymer blends and block copolymers is fundamentally new and needs to be based on first-principles theory. This is therefore an intellectual challenge in its own right. Furthermore, this paves the way to analyze the physical properties of novel composite materials that attract the attention of industrial companies. Such materials may be promising ingredients of new products like e.g. efficient and environment-friendly car tires. The implementation of the Hierarchical Equilibration strategy in the ESPResSO++ package is a step towards achieving this goal. In particular,  the practical application of this strategy is the E-CAM pilot project in collaboration with Michelin aimed at studying the Rheological Properties of New Composite Materials.

E-CAM deliverables D4.2 and D4.3 contain more information on the suite of programs developed under this pilot project.

 

[1] Zhang, G., Moreira, L. A., Stuehn, T., Daoulas, K. C., and Kremer, K., Equilibration of High Molecular Weight Polymer Melts: A Hierarchical Strategy, ACS Macro Lett., 3, 198-203 (2014)

[2] ESPResSo++ is the “Extensible Software Package for Research in Soft Matter based upon C++”, a general-purpose simulation package for soft-matter research, mainly developed at the Max Planck Institute for Polymer Research Mainz. It is freely available under the GNU Public License. http://www.espresso-pp.de/

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Contact Map – a package for analyzing and exploring contacts, from a trajectory generated by MD

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Contacts can be an important tool for defining (meta)stable states in processes involving biomolecules. For example, an analysis of contacts can be particularly useful when defining bound states during a binding processes between proteins, DNA, and small molecules (such as potential drugs).

The contacts analyzed by the contact_map package can be either intermolecular or intramolecular, and can be analyzed on a residue-residue basis or an atom-atom basis.

This package makes it very easy to answer questions like:

  • What contacts are present in a trajectory?
  • Which contacts are most common in a trajectory?
  • What is the difference between the frequency of contacts in one trajectory and another? (Or with a specific frame, such as a PDB entry.)
  • For a particular residue-residue contact pair of interest, which atoms are most frequently in contact?

It also facilitates visualization of the contact matrix, with colors representing the fraction of trajectory time that the contact was present. Full documentation available at http://contact-map.readthedocs.io/.

Information about software installation, testing and a link to the source code, can be found in our E-CAM software Library here.

Practical application and exploitation of the code

The practical application of this software module is the pilot project in collaboration with BiKi Technologies on “Binding Kinetics“, sustained by an E-CAM postdoctoral researcher at University of Amsterdam.  The project aims at investigating the binding/unbinding of a selective reversible inhibitor for protein GSK3β.

Contacts between a ligand and a protein are an excellent way to characterize “hotspots” – states where the ligand stays for a significant amount of time, but not nearly as long as in the final binding pocket. These hotspots are metastable states in path sampling, and should be treated with a multiple state approach. Therefore, attempting to identify those states would be a necessarily preliminary step to prepare the path sampling simulation.

Other more general applications to this module include protein-protein aggregation or DNA-protein binding, as well as large scale conformational changes in biomolecules, such as protein folding.

 

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LocConQubit, a module for the construction of controlled pulses on isolated qubit systems using the Local Control Theory

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The LocConQubit module implements the Local Control Theory[1,2], an algorithm for on-the-fly construction of a time-dependent potential that drives the evolution of a Hamiltonian towards one of its eigenstates. The algorithm is applicable to any Hamiltonian that is separable into a time-dependent and into a time-independent part, where the first part is directly incorporated into the algorithm, while the latter defines the basis of system states from which a designated target state is selected. States with vanishing interaction elements cannot be treated with the aforementioned algorithm. The algorithm is fine-tuned by the user with a single parameter in order to assure physical range of the generated time-dependent potential. This free parameter can be time-dependent while certain constrains in pulse generation can be directly incorporated into the algorithm. The module is accompanied with subroutines for pulse frequency analysis, post-processing, fidelity calculation and visualization of pulses and system evolution. The module is written in Python 3 programming language and is an addition to the open source QuTiP software package. The module uses the OpenMP functionalities available in QuTiP to parallelize the calculation of the pulse fidelity in order to search more efficiently for an optimal control pulse.

Additional module documentation, which includes background information on the Local Control Theory, information about software installation and testing and a link to the source code, can be found in our E-CAM software Library here. 

Practical application and exploitation of the code

The practical application of this software module is the pilot project with IBM on “Quantum Computing” sustained by an E-CAM postdoctoral researcher at École Polytechnique Fédérale de Lausanne (EPFL).

This module enables to construct more efficient control pulses for superconducting transmon qubits coupled to a single tunable coupler whose energy is controlled with an external electromagnetic pulse. By properly modulating the energy of the tunable coupler with an external control pulse, the coupler operates as a quantum logic gate between coupled qubits. To improve gate performance and thus overall performance of quantum computers, pulses are tailored to make gate operations faster while maintaining at the same time the highest possible fidelity. The Local Control Theory was applied to these systems to generate efficient state preparation pulses which transfer populations completely from one qubit state to the other, as well as pulses for the SWAP gates which completely exchange quantum states between two qubits. A set of pulses capable of transferring populations with a full fidelity to designated target states was generated and, by post-processing this set, an optimal set of pulses for experimental implementation was obtained. This set is currently being tested at IBM. In parallel, capabilities as well as limits of the Local Control theory to manipulate such systems have been investigated in detail. Results of this work are going to be published in two scientific papers. In addition, the current OpenMP parallelization will be upgraded with a more advance parallelization scheme that will enable more efficient utilization of \acs{HPC} resources and an easier implementation of parallelized optimization techniques.

 

[1] B. F. E. Curchod, T. J. Penfold, U. Rothlisberger and I. Tavernelli, Local control theory in trajectory-based nonadiabatic dynamics, Phys. Rev. A, vol. 84, p. 042507, 2011. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevA.84.042507

[2] V. Engel, C. Meier, and D. J. Tannor, Local Control Theory: Recent Applications to Energy and Particle Transfer Processes in Molecules, John Wiley Sons, Inc., 2009, pp. 29–101. [Online]. Available: http: //dx.doi.org/10.1002/9780470431917.ch2

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GRASP Sampling – a module to build a representative data set for a fitting procedure

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GRASP_sampling performs a stratified sampling of the configurations, described by vectors, of a system to build a representative training set in a fitting procedure. Given a list of candidate configurations, and selected the size (N) of the training set required, the module executes the combinatorial optimization that maximizes the following dissimilarity score (DS) among the elements of the training set:

../../../_images/dissimilarity_score.png

In this formula, the j-th configuration in the sum is the j-th nearest one to the l-th configuration and dij is the Euclidean distance between the l-th and j-th configurations. M is the number of the nearest configurations considered in the score. The exponential weight makes the score near independent from the particular value of M, if it is larger than 4-6.

The combinatorial optimization that maximizes the dissimilarity score is performed using the greedy randomized adaptive search procedure[1]  (GRASP) algorithm. A stratified sampling can be performed without a combinatorial optimization using classical statistical techniques (for example Latin hypercube sampling), the GRASP sampling becomes useful when the selection is restricted to a predeterminated set of configurations, generated or sampled with specific internal constrains. This is the case of the molecular configurations generated in a molecular dynamics simulation.

The complete module documentation, including a link to the source code, can be found in our repository here. 

Motivation and exploitation

The application of the GRASP algorithm to perform a stratified sampling is described in a recent publication [2] by the E-CAM partners at Scuola Normale Superiore (SNS), that we previously reported here.

The motivation behind this software module is the pilot project with industry “Quantum Mechanical Parameterisation of Metal Ions in Proteins” sustained by an E-CAM postdoctoral researcher from SNS.

 

[1] Feo, T. A.; Resende, M. G. Greedy randomized adaptive search procedures. J. Glob. Optim. 1995, 6, 109−133

[2] Francesco Fracchia, Gianluca Del Frate, Giordano Mancini, Walter Rocchia, and Vincenzo Barone, Force Field Parametrization of Metal Ions from Statistical Learning Techniques, J. Chem. Theory Comput. 2018, 14, 255−273

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Geomoltools: A set of software modules to easily manipulate molecular geometries

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Geomoltools is a set of eight pre- and post-treatment Fortran codes that can be used to easily manipulate molecular geometries, allowing to minimize the average energy obtained for a range of internuclear distances for the dimers of each element, and decrease the computational cost of a DFT calculation.

The set of codes are:

  • mol2xyz: converts a .mol file into an ordered .xyz file
  • pastemol: joins two .xyz files
  • movemol: translates and aligns the molecule with some predefined axes
  • stackmol: generates (manually or randomly) different stacking arrangements between two molecules
  • geodiff: compares the internal coordinates of two molecules
  • xyz2zmt_s: converts the cartesian coordinates contained in a .xyz file into Z-matrix (2 possible formats)
  • zmt2xyz_s: converts a Z-matrix (from 2 possible formats) into cartesian coordinates
  • ucubcellgen: calculates the vectors of a unit cell given some atomic coordinates.

Modules source codes can be found here.  For a detailed explanation of the main programs, please have a look to this file. A complete tutorial on how to use the different codes from the package Geomoltools in order to manipulate (rotate, translate, join, pack, convert, etc.) molecular geometries, can be found at this address.

Motivation and exploitation

These modules have been used to study the stacking arrangements of acceptor:donor molecules for organic photovolatics polymers by high-throughput computation with the SIESTA code. This set of codes are available under the GNU General Public License (GPL) version 2.

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Path density for OpenPathSampling

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Module path density implements path density calculations for the OpenPathSampling (OPS) package, including a generic multidimensional sparse histogram, and plotting functions for the two-dimensional case. Path density plots provide a way to visualize kinetic information obtained from path sampling, such as the mechanism of a rare event. In addition, the code in this module can also be used to visualize thermodynamic information such as free energy landscapes.

This module has been incorporated into the core of OPS, an open-source Python package for path sampling that wraps around other classical Molecular Dynamics (MD) codes [1]. An easy-to-read article on the use of path sampling methods to study rare events, and the role of the OPS package to performing these simulations can be found here.

At first glance, a typical path density plot may appear similar to a two-dimensional free energy landscape plot. They are both “heatmap”-type plots, plotting a two-dimensional histogram in some pair of collective variables. However, path density differs from free energy in several important respects:

  • A path density plot is histogrammed according to the number of paths, not the number of configurations. So if a cell is visited more than once during a path, it still only gets counted once.
  • A path density plot may interpolate across cells that the path jumps over. This is because it is assumed that the input must actually be continuous.

These differences can prevent metastable regions from overwhelming the transition regions in the plot. When looking at mechanisms, the path density is a more useful tool than the raw configurational probability.

Module documentation can be found here, including a link to the source code. This and other software modules for studying the thermodynamics and kinetics of rare events where recently documented in deliverable D1.2.: Classical MD E-CAM modules I, available here.

Motivation and exploitation

The path density is one of the most important tools for visualizing mechanisms, and is often one of the first things to analyze in order to draw scientific conclusions about the mechanism from transition path sampling simulations. This module was used to illustrate the differences between dynamics of the wild-type and oncogenic mutant forms of KRas, as part of one student’s master’s thesis and another student’s bachelor’s thesis at the University of Amsterdam. Results from those projects are currently in preparation for publication [2].

 

[1] Jan-Hendrik Prinz, David W.H. Swenson, Peter G. Bolhuis, and John D. Chodera. OpenPathSampling: A Python framework for path sampling simulations. I. Introduction and usage. In prep.
[2] Sander Roet, Ferry Hooft, Peter G. Bolhuis, David W.H. Swenson, and Jocelyne Vreede. Simulating the dynamics of oncogenic and wild-type KRas. In prep.

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Second-Order Differencing Scheme

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This module, SodLib, provides exact wavefunction propagation using the second-order differencing (SOD) integrator scheme to solve the time-dependent Schrödinger equation as described by Leforestier et al, J. Comp Phys, 94, 59-80, 1991. Within this scheme the time interval is determined through dividing hbar by the eigenvalue of the Hamiltonian operator with the largest absolute value. This routine has been implemented and tested as an added functionality within the Quantics software package available through CCPForge.

Quantics is a package to study chemical reactions of molecules whose main developer (G. Worth, University College London) is a member of E-CAM’s WP3 – Quantum Dynamics. It incorporates a variety of quantum dynamical methods joined by the fact that the state system is usually described via wavefunctions (containing the quantum analogue of the information given by positions and velocities for classical atoms). It is increasingly used by the computational chemistry community for scientific applications. Work is on-going in E-CAM to improve its scalability (see E-CAM deliverable D7.2 ) and add new functionalities in view of applications to study materials and light harvesting complexes.

Module documentation can be found here, including a link to the source code.

Practical application and exploitation of the code

The module is currently being used in a Phd thesis and the results of this application will provide benchmarks for a model describing proton-transfer in a condensed phase system.

 

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First GPU version of the DL_MESO_DPD code

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DL_MESO_DPD, is the Dissipative Particle Dynamics (DPD) code from the mesoscopic simulation package DL_MESO [1], developed by Dr. Michael Seaton at Daresbury Laboratory (UK). This open source code is available from Science and Technology Facilities Council (STFC) under both academic (free) and commercial (paid) licenses. E-CAM’s Work-package 4 (WP4), Meso and Multi-scale Modelling, makes use of the DL_MESO_DPD code. See this article on our news feed, for more information on how it is used within E-CAM.

In order to accelerate the DL_MESO_DPD code on the latest and future exascale hardware, a first version for NVidia GPUs has been developed. This is only a starting point, it does not yet cover all the possible cases and it does not yet support multiple GPUs. However, it represents an HPC milestone for the application, complementing the already present parallel versions developed for shared and distributed memory (MPI/OpenMP).

Module documentation including purpose, testing and background information, can be found here. The GPU-version to CPU-version performance analysis can be found in the module documentation and in deliverable D7.2.: E-CAM software porting and benchmarking data I, recently submitted to the EU.

[1] Michael A. Seaton, Richard L. Anderson, SebastianMetz, andWilliamSmith. DL_meso: highly scalable mesoscale simulations. Molecular Simulation, 39(10):796–821, September 2013.

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LibOMM : Orbital Minimization Method Library

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Purpose

The library LibOMM solves the Kohn-Sham equation as a generalized eigenvalue problem for a fixed Hamiltonian. It implements the orbital minimization method (OMM), which works within a density matrix formalism. The basic strategy of the OMM is to find the set of Wannier functions (WFs) describing the occupied subspace by direct unconstrained minimization of an appropriately-constructed functional. The density matrix can then be calculated from the WFs. The solver is usually employed within an outer self-consistency (SCF) cycle. Therefore, the WFs resulting from one SCF iteration can be saved and then re-used as the initial guess for the next iteration.

More information on the module’s documentation can be found here, and the source code is available from the E-CAM Gitlab here. The algorithms and implementation of the library are described in https://arxiv.org/abs/1312.1549v1.

This module is an effort from the Electronic Structure Library Project (ESL), and it was initiated during an E-CAM Extended Software Development Workshop in Zaragoza in June 2016. This and other codes revolved around the broad theme of solvers, were recently reported in Deliverable D2.1.: Electronic structure E-CAM modules I, available for download and consultation here.

Practical application and exploitation of the module

libOMM is one of the libraries supported and enhanced by the Electronic Structure Infrastructure ELSI [1], which in turn is interfaced with the DGDFT, FHI-aims, NWChem, and SIESTA codes.

[1] The electronic structure infrastructure ELSI  provides and enhances scalable, open-source software library solutions for electronic structure calculations in materials science, condensed matter physics, chemistry, molecular biochemistry, and many other fields [https://arxiv.org/abs/1705.11191v1].

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