New article is out: “Force Field Parametrization of Metal Ions from Statistical Learning Techniques”

 

This paper from E-CAM partners working in Scuola Normale Superiore (Pisa, Italy) describes a novel statistical procedure, developed to optimize the parameters of non-bonded force fields of metal ions in soft matter. The paper is open access and can be downloaded directly from ACS’s page at http://pubs.acs.org/doi/10.1021/acs.jctc.7b00779.

This work was performed in the context of the E-CAM pilot project on Quantum Mechanical Parameterisation of Metal Ions in Proteins, which is a collaboration with BiKi Technologies. The list of software modules associated to the pilot project (and this publication) can be found here.

Article

Title: Force Field Parametrization of Metal Ions from Statistical Learning Techniques

Authors: Francesco Fracchia, Gianluca Del Frate, Giordano Mancini, Walter Rocchia, and Vincenzo Barone

Abstract: A novel statistical procedure has been developed to optimize the parameters of non-bonded force fields of metal ions in soft matter. The criterion for the optimization is the minimization of the deviations from ab initio forces and energies calculated for model systems. The method exploits the combination of the linear ridge regression and the cross-validation techniques with the di˙erential evolution algorithm. Wide freedom in the choice of the functional form of the force fields is allowed since both linear and non-linear parameters can be optimized. In order to maximize the information content of the data employed in the fitting procedure, the composition of the training set is entrusted to a combinatorial optimization algorithm which maximizes the dissimilarity of the included instances. The methodology has been validated using the force field parametrization of five metal ions (Zn2+, Ni2+, Mg2+, Ca2+, and Na+) in water as test cases.

 

Share

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

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.

Share

A Conversation on Neural Networks, from Polymorph Recognition to Acceleration of Quantum Simulations

 

With Prof. Christoph Dellago (CD), University of Vienna, and Dr. Donal Mackernan (DM), University College Dublin.

 

Abstract

Recently there has been a dramatic increase in the use of machine learning in physics and chemistry, including its use to accelerate simulations of systems at an ab-initio level of accuracy, as well as for pattern recognition. It is now clear that these developments will significantly increase the impact of simulations on large scale systems requiring a quantum level of treatment, both for ground and excited states. These developments also lend themselves to simulations on massively parallel computing platforms, in many cases using classical simulation engines for quantum systems.

 

Continue reading…

Share

Scoping Workshop: From the Atom to the Molecule

If you are interested in attending this workshop, please visit the CECAM website bellow.

Share

LibOMM : Orbital Minimization Method Library

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].

Share

Solvers for quantum atomic radial equations

SQARE (solvers for quantum atomic radial equations) is a library of utilities intended for dealing with functions discretized on radial meshes, wave-equations with spherical symmetry and their corresponding quantum states. The utilities are segregated into three levels: radial grids and functions, ODE solvers, and states.

For more information see modules SQARE radial grids and functions, SQARE ODE and SQARE states documentations.

Share

State of the art workshop: Electronic Structure

This is the third state of the art workshop for 2016. It is organised by the CECAM-UK-HARTREE node and will focus on electronic structure. Scoping workshops provide a forum to survey new methods and developments in simulation. These workshops inform the software that will be developed for the E-CAM library.

Share

Extended Software Development Workshop: Wannier90

The aim of the workshop is to share recent developments related to the generation and use of maximally-localised Wannier functions and to either implement these developments in, or interface them to, theWannier90 code. It will also be an opportunity to improve and update existing interfaces to other codes and write new ones. The format will be deliberately open, with the majority of the time allocated for coding and discussion.

Share

Electronic Structure Library Coding Workshop

This is the first E-CAM Extended Software Development Workshop, taking place in Zaragoza in Spain. The Electronic Structure Library  is a new project to build a community-maintained library of software of use for electronic structure simulations. The goal is to create an extended library that can be employed by everyone for building their own packages and projects.

Share