April 2021

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Implementation of High-Dimensional Neural Network Potentials

In this conversation with Andreas Singraber, post-doc in E-CAM until last month, we will discover the ensemble of his work to expand the Neural Network Potential (NNP) Package n2p2 and to improve user accessibility to the code via the LAMMPS package. Andreas will talk about new tools that he developed during his E-CAM pilot project, that can provide valuable input for future developments of NNP based coarse-grained models. He will describe how E-CAM has impacted his career and led him to recently integrate a software company as a scientific software engineer.

Challenges to Industry of drug substance development

APC was created in 2011 by Dr. Mark Barrett and Prof. Brian Glennon of the University College Dublin School of Chemical and Bioprocess Engineering with a mission to harness state-of-the-art research methods & know-how to accelerate drug process development. Since then it has grown organically partnering with companies across the world, large and small, to bring medicines to market at unprecedented speed. Computation-based methods play a growing role in all stages of its medicine pipeline as explained by Dr. Jacek Zeglinski in this E-CAM interview on challenges to Industry of drug substance development.

Development of an HTC-based, scalable committor analysis tool in OpenPathSampling opens avenues to investigate enzymatic mechanisms linked to Covid-19

Development of an HTC-based, scalable committor analysis tool in OpenPathSampling opens avenues to investigate enzymatic mechanisms linked to Covid-19
The E-CAM HPC Centre of Excellence and a PRACE team in Wroclaw have teamed up to develop High Throughput Computing (HTC)-based tools to enable the computational investigation of reaction mechanisms in complex systems. Committor analysis is a computationally expensive tool, but allows for powerful simulations. In this project, the main goal was to integrate the committor analysis methodology with the existing software application OpenPathSampling (OPS), which is performance portable across a range of HPC hardware and hosting sites. Integrating OPS and the HTC library resulted in an unprecedented parallelised committor simulation capability. These tools are now being implemented for a committor simulation of the SARS-CoV-2 main protease. The data will provide insight into the dynamics of the protease loop region and the mechanism of its gate-like activity.

Proof of concept : recognition as a disruptive technology

The transformation of a beautiful idea born via simulation into a commercial opportunity is recognised as a disruptive technology. At the heart of this ongoing story is advanced simulation using massively parallel computation, rare-event methods, genetic engineering, and a molecular switch developed during an E-CAM pilot project, with an initial application as a point-of-care medical diagnostic for COVID-19 and influenza. This work has recently received an award by NovaUCD.

Featured Software Modules


ALL library implementation in HemeLB, a CoE collaboration

This module describes the work done in E-CAM in cooperation with the CompBioMed Centre of Excellence, on the integration of the load balancing library ALL on the HemeLB code.

n2p2 - Improved link to HPC MD software

This module improves the connection of n2p2 to HPC software, in particular to LAMMPS, by creating a pull request to the official LAMMPS repository. Furthermore, the build process for the n2p2 interface library is enhanced to allow for a selective activation of different interfaces.

DL_MESO (DPD) on Kokkos for enhanced performance portability

This work relates to the implementation of a performance portable version of DL_MESO (DPD) using the Kokkos library. This allows to run DL_MESO on NVidia GPUs as well as on other GPUs or architectures (many-core hardware like KNL), allowing performance portability as well as separation of concern between computational science and HPC.

MaZe, Mass-Zero Constrained Dynamics for Orbital Free Density Functional Theory

This program performs Orbital-Free Density Functional Theory Molecular Dynamics using the Mass-Zero (MaZe) constrained molecular dynamics approach described in Phys. Chem. Chem. Phys., 2020, 22, 10775-10785.

Recent publication

Transition Path Sampling as Markov Chain Monte Carlo of Trajectories: Recent Algorithms, Software, Applications, and Future Outlook

Peter G. Bolhuis and David W. H. Swenson, Adv. Theory Simul. 2021, 2000237.
Synopsis: The development of enhanced sampling methods to investigate rare but important events has always been a focal point in the molecular simulation field. Such methods often rely on prior knowledge of the reaction coordinate. However, the search for this reaction coordinate is at the heart of the rare event problem. Transition path sampling (TPS) circumvents this problem by generating an ensemble of dynamical trajectories undergoing the activated event.

Upcoming Extended Software Development Workshops

Improving bundle libraries

Emilio Artacho, Volker Blum, Fabiano Corsetti, Micael Oliveira, Nick Papior,Yann Pouillon
Dates:Oct 11-22, 2021
Info and registration:

HPC for simulation of complex phenomena

Andrea Cavalli, Sergio Decherchi, Marco Ferrarotti, Walter Rocchia
Dates: Oct.11-15, 2021
Info and registration :

Submitted deliverables

D1.6: Classical MD E-CAM Modules V*
Nine software modules delivered to the E-CAM repository in the area of Classical Molecular Dynamics
D2.6: Electronic structure E-CAM modules V*
Nine software modules delivered to the E-CAM repository in the area of Electronic Structure
D3.6: Quantum dynamics E-CAM modules V*
Six software modules delivered to the E-CAM repository in the area of Quantum Dynamics
D4.6: Meso- and multi-scale modelling E-CAM modules V*
Nine software modules delivered to the E-CAM repository in the area of Meso- and Multi-scale modelling

*This is a draft document delivered to the European Commission but not yet approved
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 676531