List of innovations by the CoEs, spotted by the EU innovation radar

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The EU Innovation Radar aims to identify high-potential innovations and innovators. It is an important source of actionable intelligence on innovations emerging from research and innovation projects funded through European Union programmes. 
 
These are the innovations from the HPC Centres of Excellence as spotted by the EU innovation radar:
 
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Title: GROMACS, a versatile package to perform molecular dynamics
Market maturity: Exploring
Project: BioExcel
Innovation Topic: Excellent Science
KUNGLIGA TEKNISKA HOEGSKOLAN - SWEDEN

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Title: Urgent Computing services for the impact assessment in the immediate aftermath of an earthquake
Market maturity: Tech Ready
Market creation potential: High
Project: ChEESE
Innovation Topic: Excellent Science
EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH - SWITZERLAND
BULL SAS - FRANCE

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Table: New coupled earth system model
Market maturity: Tech Ready
Project: ESiWACE
Innovation Topic: Excellent Science
BULL SAS - FRANCE
MET OFFICE - UNITED KINGDOM
EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS - UNITED KINGDOM
 

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Title: In-Situ Analysis of CFD Simulations
Market maturity: Tech Ready
Market creation potential: High
Project: Excellerat
Innovation Topic: Excellent Science
KUNGLIGA TEKNISKA HOEGSKOLAN - SWEDEN
FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V. - GERMAN

Title: Interactive in situ visualization in VR
Market maturity: Tech Ready
Market creation potential: High
Project: Excellerat
Innovation Topic: Excellent Science
UNIVERSITY OF STUTTGART - GERMANY

Title: Machine Learning Methods for Computational Fluid Dynamics (CFD) Data
Market maturity: Tech Ready
Market creation potential: Noteworthy
Project: Excellerat
Innovation Topic: Excellent Science
KUNGLIGA TEKNISKA HOEGSKOLAN - SWEDEN
FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V. - GERMAN

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Title: Quantum Simulation as a Service
Market maturity: Exploring
Market creation potential: Noteworthy
Project: MaX
Innovation Topic: Excellent Science
EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH - SWITZERLAND
CINECA CONSORZIO INTERUNIVERSITARIO - ITALY

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E-CAM: Industry training at the MESOSCALE

To further expand the portfolio of activities targeted at industrialists, E-CAM has established a series of new events targeted at training interested industrial researchers on the simulation and modelling techniques implemented in specific codes and in the direct use of this software for their industrial applications.

The first event of this series will focus on the area of meso- and multiscale simulations and on the flagship code DL_MESO:

Industry Training at the MESOSCALE

22nd – 25th March 2021
Online / UKRI STFC Daresbury Laboratory
Website: https://www.cecam.org/workshop-details/1074

In this workshop we will introduce DL_MESO: a software package for mesoscale simulations. Usage of the software will be gradually presented, starting with tutorials based on theoretical background and following up with hands-on sessions. We will focus on the Dissipative Particle Dynamics (DPD) methodology, exploring the different capabilities of DL_MESO_DPD via practical examples that reflect daily industrial challenges. 

DL_MESO has been used for a wide range of problems of both scientific and industrial interest. The code is used, for example, in projects with Unilever, Syngenta and Infineum – to develop DPD parameterisation strategies and simulation protocols to predict important properties of newly-devised surfactant-based formulations; with IBM Research Europe – to model nanofluidic multiphase. The code developers themselves will provide the training. The event is co-organized by Formeric, a company that helps industrial users to study their own formulated projects, primarily by developing a software platform to make it easier for them to access DPD simulations and modelling tools.

As part of the event, UKRI STFC offers a 6-month one seat free licence of DL_MESO 2.7 to be used soon after the end of the event, which will help testing the software.


Don’t miss this opportunity to be trained by the experts on the methods and on the codes themselves! Register for event at

www.cecam.org/workshop-details/1074/

Download event flyer

January Module of the Month: MaZe, Mass-Zero Constrained Dynamics for Orbital Free Density Functional Theory

 

Description

The program performs Orbital-Free Density Functional Theory Molecular Dynamics (OF-DFT-MD) using the Mass-Zero (MaZe) constrained molecular dynamics approach described in [1].

This method enforces, at each time step, the Born-Oppenheimer condition that the system relaxes instantaneously to the ground state through the formalism of massless constraints. The adiabatic separation between the degrees of freedom is enforced rigorously, and the algorithm is symplectic and time-reversible in both physical and additional set of degrees of freedom.

The computation of the electronic density is carried out in reciprocal space through a plane-waves expansion so that the mass-zero degrees of freedom are associated to the Fourier coefficients of the electronic density field. The evolution of the ions is performed using Velocity-Verlet algorithm, while the SHAKE algorithm is used for computation of the additional degrees of freedom. The code can sample the NVE and the NVT ensemble, the latter through a Langevin thermostat.

The code was optimised to run on HPC machines, as explained in the software documentation. The proposed optimisations allow a reduction of the execution time by roughly 50% compared to the original version of the code.

Caption: MaZe optimisation of the electronic density at each nuclear step along an orbital-free DFT Born–Oppenheimer trajectory. Very high speed of convergence is achieved by interpreting the optimisation as a constraint solved via an original implementation of the SHAKE algorithm.  The number of iterations needed to converge the electronic density and the time per time step for MaZe (red) and standard conjugate gradients (blue) are compared for the indicated kinetic energy functionals (G_c is the energy cut-off).

Practical application

The code is intended for condensed matter physicists and for material scientists and it can be used for various purposes related to the subject. Even though some analysis tools are included in the package, the main goal of the software is to produce particles trajectories to be analysed in post-production by means of external software.

MaZe implements the orbital-free formulation of density functional theory, in which the optimisation of the energy functional is performed directly in terms of the electronic density without use of Kohn-Sham orbitals. This feature avoids the need for satisfying the orthonormality constraint among orbitals and allows the computational complexity of the code to scale linearly with the dimensionality of the system. The accuracy of the simulation relies on the choice of the kinetic energy functional, which has to be provided in terms of the electronic density alone.

Documentation and source code

The complete documentation is at this location. The source code is available from the E-CAM Gitlab under the MaZe project (software is under embargo until publication leveraging the developments is achieved. Contact code developers or info@e-cam2020.eu for more information.)

References

[1] Sara Bonella, Alessandro Coretti, Rodolphe Vuilleumier, Giovanni Ciccotti, “Adiabatic motion and statistical mechanics via mass-zero constrained dynamics”, Phys. Chem. Chem. Phys. 2020, 22, 10775-10785 DOI: 10.1039/D0CP00163E
Pre-print version (open access): https://arxiv.org/abs/2001.03556

Mesoscale simulation of billion atom complex systems using thousands of GPGPUS's

 A Use Case by

Short description

In collaboration with the UKRI STFC Daresbury Laboratory, E-CAM has developed a highly effi-cient version of DL_MESO, a software package for mesoscale simulations developed at the UKRI STFC [1]. This distributed GPU acceleration de-velopment is an extension of the DL_MESO pack-age to MPI+CUDA that exploits the computational power of the latest NVIDIA cards on hybrid CPU–GPU architectures. The need to port DL_MESO to massively parallel computing platforms arose be-cause often real systems are made of millions of particles and small clusters are usually not suffi-cient to obtain results in brief time. Moreover, with the advent of hybrid architectures, updating the code is becoming an important software engineer-ing step to allow scientist to continue their work on such systems.

Results & Achievements

The current multi-GPU version of DL_MESO scales with an 85% parallel efficiency up to 4096 GPUs (equivalent to almost 20 petaflops of raw double precisions performance) (see Fig. 1) [2]. This allows the simulation of very large systems like a phase mixture with 1.8 billion particles (Fig. 2).

For improved load balancing, E-CAM’s load balancing ALL[3] developed at the Juelich Supercomputing Centre has been implemented in the multi-GPU version of DL_MESO (DPD). The intention is to allow for better performance when modelling complex systems, like large proteins or lipid bi-layers, redistributing the workload across the GPUs. The library Kokkos [4] is also being incor-porated in DL_MESO (DPD), enabling the execu-tion of DL_MESO_DPD on NVidia GPUs as well as on other GPUs or architectures (many-core hardwarelike KNL), allowing performance porta-bility as well as separation of concern between computational science and HPC.

Objectives

The rewrite of the DL_MESO code allows the sim-ulation of billion of atoms complex systems on thousands of GPGPUs. This is necessary to simu-late surfactants, key ingredients in personal care products, dish soaps, laundry detergents, etc. At the microscopic level, surfactants are very long chains of molecules, known as polymers. Realistic poly-mers are typically very large macromolecules, and their modelling in industrial manufacturing processes (e.g. to predict complex material properties), is a very challenging task.

E-CAM Newsletter December 2020

5. January 2021
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(c) Fusion Medical Animation on Unsplash

How EU projects work on supercomputing applications to help contain the corona virus pandemic

The Centres of Excellence in high-performance computing are working to improve supercomputing applications in many different areas: from life sciences and medicine to materials design, from weather and climate research to global system science. A hot topic that affects many of the above-mentioned areas is, of course, the fight against the corona virus pandemic.

There are rather obvious challenges for those EU projects that are developing HPC applications for simulations in medicine or in the life sciences, like CompBioMed (Biomedicine) BioExcel (Biomolecular Research), and PerMedCoE (Personalized Medicine). But also other projects from scientific areas, that you would, at first sight, not directly relate to research on the pandemic, are developing and using appropriate applications to model the virus and its spread, and support policy makers with computing-heavy simulations. For example, did you know that researchers can simulate the possible spread of the virus on a local level, taking into account measures like closing shops or quarantining residents?

This article gives an overview over the various ways in which EU projects are using supercomputing applications to tackle and support the global challenge of containing the pandemic.

Simulations for better and faster drug development

CompBioMed is an EU-funded project working on applications for computational biomedicine. It is part of a vast international consortium across Europe and USA working on urgent coronavirus research. “Modelling and simulation is being used in all aspects of medical and strategic actions in our fight against coronavirus. In our case, it is being harnessed to narrow down drug targets from billions of candidate molecules to a handful that can be clinically trialled”, says Peter Coveney from University College London (UCL) who is heading CompBioMed’s efforts in this collaboration. The goal is to accelerate the development of antiviral drugs by modelling proteins that play critical roles in the virus life cycle in order to identify promising drug targets.

Secondly, for drug candidates already being used and trialled, the CompBioMed scientists are modelling and analysing the toxic effects that these drugs may have on the heart, using supercomputing resources required to run simulations on such scales.  The goal is to assess the drug dosage and potential interactions between drugs to provide guidance for their use in the clinic.

Finally, the project partners analysed a model used to inform the UK Government’s response to the pandemic. It has been found to contain a large degree of uncertainty in its predictions, leading it to seriously underestimate the first wave. “Epidemiological modelling has been and continues to be used for policy-making by governments to determine healthcare interventions”, says Coveney. “We have investigated the reliability of such models using HPC methods required to truly understand the uncertainty and sensitivity of these models.” As a conclusion, a better public understanding of the inherent uncertainty of models predicting COVID-19 mortality rates is necessary, saying they should be regarded as “probabilistic” rather than being relied upon to produce a particular and specific outcome.

Image of SuperMUC-NG, supercomputer at Leibniz Supercomputing Centre of the Bavarian Academy of Sciences. (c)MMM/LRZ
Image of SuperMUC-NG, supercomputer at Leibniz Supercomputing Centre of the Bavarian Academy of Sciences, consortium member in the CompBioMed project. (c) MMM/LRZ

BioExcel is an EU-funded project developing some of the most popular applications for modelling and simulations of biomolecular systems. Along with code development, the project builds training programmes to address competence gaps in extreme-scale scientific computing for beginners, advanced users and system maintainers.

When COVID-19 struck, BioExcel launched a series of actions to support the community on SARS-CoV-2 research, with an extensive focus on facilitating collaborations, user support, and providing access to HPC resources at partner centers. BioExcel partnered with Molecular Sciences Software Institute to establish the COVID-19 Molecular Structure and Therapeutics Hub to allow researchers to deposit their data and review other group’s submissions as well.

During this period, there was an urgent demand for diagnostics and sharing of data for COVID-19 applications had become vital more than ever. A dedicated BioExcel-CV19 web-server interface was launched to provide access to study molecules involved in the COVID-19 disease. This allowed the project to be a part of open access initiative promoted by the scientific community to make research accessible.

Recently, BioExcel endorsed the EU manifesto for COVID-19 Research launched by European Commission as part of their response to the coronavirus outbreak.

Modelling the electronic structure of the protease

MaX (MAterials design at the eXascale) is a European Centre of Excellence aiming at materials modelling, simulations, discovery and design on the exascale supercomputing architectures.

Though the main interest of the MaX flagship codes is then centered on materials science, the CoE is participating in the fight against SARS-CoV-2. Given the critical pandemic situation that the world is currently facing, an unprecedented effort is being devoted to the study of SARS-CoV-2 by researchers from different scientific communities and groups worldwide. From the biomolecular standpoint, particular focus is being devoted to the main protease, as well as to the spike protein. As such, it is an important potential antiviral drug target: if its function is inhibited, the virus remains immature and non-infectious. Using fragment-based screening, researchers have identified a number of small compounds that bind to the active site of the protease and can be used as a starting point for the development of protease inhibitors.

Sars-Cov-2 main protease monomer, in green, with the N3 3-mer peptide inhibitor bound in the enzyme’s active site.(from PDB crystal structure 6lu7). Structure like this ones can be simulated with a full DFT calculation and automatically decomposed into fragments whose interaction network can be characterized and analyzed.

Among other quantities, MaX researchers now have the possibility to model the electronic structure of the protease in contact with a potential docked inhibitor, and provide new insights on the interactions between them by selecting specific amino-acids that are involved in the interaction and characterizing their polarities. This new approach proposed by the MaX scientists is complementary to the docking methods used up to now and based on in-silico research of the inhibitor. Biological systems are naturally composed of fragments such as amino-acids in proteins or nitrogenous bases in DNA.

With this approach, it is possible to evaluate whether the amino acid-based fragmentation is consistent with the electronic structure resulting from the QM computation. This is an important indicator for the end-user, as it enables to evaluate the quality of the information associated with a given fragment. Then, QM observables on the system’s fragments can be obtained, which are based on a population analysis of electronic density of the system, projected on the amino-acid.

A novelty that this approach enables is the possibility of quantifying the strength of the chemical interaction between the different fragments. It is possible to select a target region and identify which fragments of the systems interact with this region by sharing electrons with it.

“We can reconstruct the fragmentation of the system in such a way as to focus on an active site in a specific portion of the protein”, says Luigi Genovese from CEA (Commissariat à l’énergie atomique et aux énergies alternatives) who is heading Max’s efforts on this topic. “We think this modelling approach could inform efforts in protein design by granting access to variables otherwise impervious to observation.”

This illustration, created at the Centers for Disease Control and Prevention (CDC), reveals ultrastructural morphology exhibited by coronaviruses. Note the spikes that adorn the outer surface of the virus, which impart the look of a corona surrounding the virion, when viewed electron microscopically. A novel coronavirus, named Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), was identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China in 2019. The illness caused by this virus has been named coronavirus disease 2019 (COVID-19).
Various EU projects are using supercomputing applications to tackle and support the global challenge of containing the pandemic (c)CDC on Unsplash

Improving drug design and biosensors

The project E-CAM supports HPC simulations in industry and academia through software development, training and discussion in simulation and modeling. Project members are currently following two approaches to add to the research on the corona virus.

Firstly, the SARS-CoV-2 virus that causes COVID-19 uses a main protease to be functional. One of the drug targets currently under investigation is an inhibitor for this protease. While efforts on simulations of binding stability and dynamics are being conducted, not much is known of the dynamical transitions of the binding-unbinding reaction. Yet, this knowledge is crucial for improved drug design. E-CAM aims to shed light on these transitions, using a software package developed by project teams at the University of Amsterdam and the Ecole Normale Superieure in Lyon.

Secondly, E-CAM contributes to the development of the software required to design a protein-based sensor for the quick detection of COVID-19. The sensor, developed at the partner University College Dublin with the initial purpose to target influenza, is now being adapted to SARS-CoV-2. This adaptation needs DNA sequences as an input for suitable antibody-epitope pairs. High-performance computing is required to identify these DNA sequences to design and simulate the sensors prior to their expression in cell lines, purification and validation.

Studying COVID-19 infections on the cell level

The project PerMedCoE aims to optimise codes for cell-level simulations in high-performance computing, and to bridge the gap between organ and molecular simulations. The project started in October 2020.

“Multiscale modelling frameworks prove useful in integrating mechanisms that have very different time and space scales, as in the study of viral infection, human host cell demise and immune cells response. Our goal is to provide such a multiscale modelling framework that includes infection mechanisms, virus propagation and detailed signalling pathways,” says Alfonso Valencia, PerMedCoE project coordinator at the Barcelona Supercomputing Center.

The project team has developed a use case that focusses on studying COVID-19 infections using single-cell data. The work was presented to the research community at a specialized virtual conference in November, the Disease Map Community Meeting. “This use case is a priority in the first months of the project”, says Valencia.

On the technical level, disease maps networks will be converted to models of COVID-19 and human cells from the lung epithelium and the immune system. Then, the team will use omics data to personalise models of different patients’ groups, differentiated for example by age or gender. These data-tailored models will then be incorporated into a COVID-focussed version of the open source cell-level simulator PhysiCell.

Supporting policy makers and governments

The HiDALGO project focusses on modelling and simulating the complex processes which arise in connection with major global challenges. The researchers have developed the Flu and Coronavirus Simulator (FACS) with the objective to support decision makers to provide an appropriate response to the current pandemic situation taking into account health and care capabilities.

FACS is guided by the outcomes of SEIR (Susceptible-Exposed-Infectious-Recovered) models operating at national level. It uses geospatial data sources from Openstreet Map to approximate the viral spread in crowded places, while trading the potential routes to reach them.

In this way, the simulator can model the COVID-19 spread at local level to provide estimations of infections and hospital arrivals, given a range of public health interventions, going from no interventions to lockdowns. Public authorities can use the results of the simulations to identify peaks of contagion, set appropriate measures to reduce spread and provide necessary means to hospitals to prevent collapses. “FACS has enabled us to forecast the spread of COVID-19 in regions such as the London Borough of Brent. These forecasts have helped local National Health Service Trusts to more effectively plan out health and care services in response to the pandemic.” says Derek Groen from the HiDALGO project partner Brunel University London.

Scientists from the HiDALGO project use simulations to predict the spread of the Corona virus in certain areas of London. (c)HiDALGO
Scientists from the HiDALGO project use simulations to predict the spread of the Corona virus in certain areas of London. (c)HiDALGO

EXCELLERAT is a project that is usually focussing on supercomputing applications in the area of engineering. Nevertheless, a group of researchers from EXCELLERAT’s consortium partner SSC-Services GmbH, an IT service provider in Böblingen, Germany and the High-Performance Computing Center Stuttgart (HLRS) are also providing measures to contain the pandemic by supporting the German Federal Institute for Population Research (Bundesinstitut für Bevölkerungsforschung, BiB).

The scientists have developed an intelligent data transfer platform, which enables the BiB to upload data, perform computing-heavy simulations on the HLRS’ supercomputer Hawk, and download the results. The platform supports the work of BiB researchers in predicting the demand for intensive care units during the COVID-19 pandemic. “Nowadays, organisations face various issues while dealing with HPC calculations, HPC in general or even the access to HPC resources,” said Janik Schüssler, project manager at SSC Services. “In many cases, calculations are too complex and users do not have the required know-how with HPC technologies. This is the challenge that we have taken on. The BiB’s researchers had to access HLRS’s Hawk in a very complex way. With the help of our new platform, they can easily access Hawk from anywhere and run their simulations remotely.”

“This platform is part of EXCELLERAT’s overall strategy and tools development, which not only addresses the simulation part of engineering workflows, but provides users the necessary means to optimise their work”, said Bastian Koller, Project Coordinator of EXCELLERAT and HLRS’s Managing Director. “Extending the applicability of this platform to further use cases outside of the engineering domain is a huge benefit and increases the impact of the work performed in EXCELLERAT.”

LearnHPC: dynamic creation of HPC infrastructure for educational purposes

 

Abstract

In a newly successful PRACE-ICEI proposal, E-CAM, FocusCoE, HPC Carpentry and EESSI join forces to bring HPC resources to the classroom in a simple, secure and scalable way. Our plan is to reproduce the model developed by the Canadian open-source software project Magic Castle. The proposed solution creates virtual HPC infrastructure(s) in a public cloud, in this case on the Fenix Research Infrastructure, and generates temporary event-specific HPC clusters for training purposes, including a complete scientific software stack. The scientific software stack is fully optimised for the available hardware and will be provided by the European Environment for Scientific Software Installations (EESSI). 

Description 

EU-wide requirements for HPC training are exploding as the adoption of HPC in the wider scientific community gathers pace. However, the number of topics that can be thoroughly addressed without providing access to actual HPC resources is very limited, even at the introductory level. In cases where such access is available, security concerns and the overhead of the process of provisioning accounts make the scalability of this approach questionable.

EU-wide access to HPC resources on the scale required to meet the training needs of all countries is an objective that we attempt to address with this project. The proposed solution essentially provisions virtual HPC system(s) in a public cloud, in this case on the Fenix Research Infrastructure. The infrastructure will dynamically create temporary event-specific HPC clusters for training purposes, including a scientific software stack. The scientific software stack will be provided by the European Environment for Scientific Software Installations (EESSI) which uses a software distribution system developed at CERN, CernVM-FS, and makes a research-grade scalable software stack available for a wide set of HPC systems, as well as servers, desktops and laptops (including MacOS and Windows!). 

The concept is built upon the solution of Compute Canada, Magic Castle, which aims to recreate the Compute Canada user experience in public clouds (there is even a presentation where the main developer creates a cluster just by talking to his phone!). Magic Castle uses the open-source software Terraform and HashiCorp Language (HCL) to define the virtual machines, volumes, and networks that are required to replicate a virtual HPC infrastructure. 

In addition to providing a dynamically provisioned HPC resource, the project will also offer a scientific software stack provided by EESSI. This model is also based on a Compute Canada approach and enables replication of the EESSI software environment outside of any directly related physical infrastructure. 

Our adaption of Magic Castle aims to recreate the EESSI HPC user experience, for training purposes, on the Fenix Research Infrastructure.  After deployment, the user is provided with a complete HPC cluster software environment including a Slurm scheduler, a Globus Endpoint, JupyterHub, LDAP, DNS, and a wide selection of research software applications compiled by experts with EasyBuild.

The architecture of the solution is best represented by the graphic below (taken from the Compute Canada documentation at https://github.com/ComputeCanada/magic_castle/tree/master/docs):

Cloud Cluster Architecture Overview ©Magic Castle (https://github.com/ComputeCanada/magic_castle)

With the resources made available to the project, we plan to run 6 HPC training events from January to July 2021. These training events are connected to the Centres of Excellence E-CAM and FocusCoE and with HPC Carpentry.

November Module of the Month: PerGauss, Periodic Boundary Conditions for gaussian bases


 Description

The module PerGauss (Per iodic Gauss ians) consists on an implementation of periodic boundary conditions for gaussian bases for the Quantics program package.

In quantum dynamics, the choice of coordinates is crucial to obtain meaningful results. While xyz or normal mode coordinates are linear and do not need a periodical treatment, particular angles, such as dihedrals, must be included to describe accurately the (photo-)chemistry of the system under consideration. In these cases, periodicity can be taken into account, since the value of the wave function and hamiltonian repeats itself after certain intervals.

Practical application

The module is expected to provide the quantum dynamics community with a more efficient way of treating large systems whose excited state driving forces involve periodic coordinates. When used on precomputed potentials (in G-MCTDH and vMCG), the model can improve the convergence since smaller grid sizes are needed. Used on-the-fly, it reduces considerably the amount of electronic structure computations needed compared to cartesian coordinates, since conformations that seemed far in the spanned space may be closer after applying a periodic transformation.

Source code

Currently PerGauss resides within the Quantics software package available upon request through gitlab. For more information see the PerGauss documentation here.

 

Source: This text was first published on the E-CAM website here: https://www.e-cam2020.eu/pergauss-periodic-boundary-conditions-for-gaussian-bases/

E-CAM article on the EU Research Magazine

An article about E-CAM has just been released with the Autumn edition of the EU Research Magazine. The EU research magazine is Europe’s leader in research dissemination.

The piece consists on an interview to Prof. Ignacio Pagonabarraga, E-CAM technical manager, Dr. Sara Bonella, leader of our work-package focused on quantum dynamics and also of the work-package that deals with the interactions with industry; Dr. Donal Mackernan, leader of our dissemination work-package and Dr. Jony Castagna, programmer in E-CAM.

The interview describes E-CAM’s work in

(1) developing software targeted at the needs of both academic and industrial end-users, with applications from drug development to the design of new materials ;

(2) tuning those codes to run on HPC machines, through application co-design and the provision of HPC oriented libraries and services;

(3) training scientists from industry and academia ; and

(4) supporting industrial end-users in their use of simulation and modelling, via workshops and direct discussions with experts in the CECAM community.

Autumns edition of the EU Research Magazine is available online at  http://www.euresearcher.com/14/eu-research-live.

 

ETP4HPC handbook 2020 released

6. November 2020

The 2020 edition of the ETP4HPC Handbook of HPC projects is available. It offers a comprehensive overview over the European HPC landscape that currently consists of around 50 active projects and initiatives. Amongst these are the 14 Centres of Excellence and FocusCoE, that are also represented in this edition of the handbook.

>> Read here