E-CAM Case Study: The implementation of a hierarchical equilibration strategy for polymer melts, to help studying the rheological properties of new composite materials

Dr. Hideki Kobayashi, Max-Planck-Institut für Polymerforschung, Germany

Abstract

The ability to accurately determine and predict properties of newly developed polymer materials is highly important to researchers and industry, but at the same time represents a significant theoretical and computational challenge. We have developed a novel multiscale simulation method based on the hierarchical equilibration strategy, which significantly decreases the equilibrium properties calculation time while satisfying the thermodynamic consistency. A number of E-CAM modules was developed and implemented in he ESPResSo++ software package.

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The simulation of metal ions in protein-water systems using machine learning: An E-CAM case study and conversation

 

With Dr. Francesco Fracchia, Scuola Normale Superiore di Pisa

Interviewer: Dr. Donal Mackernan, University College Dublin

 

Abstract

One quarter to one third of all proteins require metals to function but the description of metal ions in standard force fields is still quite primitive. In this case study and interview an E-CAM project to develop a suitable parameterisation using machine learning is described.  The training scheme combines classical simulation with electronic structure calculations to produce a force field comprising standard classical force fields with additional terms for the metal ion-water and metal ion-protein interactions. The approach allows simulations to run as fast as standard molecular dynamics codes, and is suitable for efficient massive parallelism scale-up.

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From Rational Design of Molecular Biosensors to Patent and potential Start-up

 

Dr. Donal Mackernan, University College Dublin

Abstract

The power of advanced simulation combined with statistical theory , experimental know-how and high performance computing is used to design a protein based molecular switch sensor with remarkable sensitivity and significant industry potential. The sensor technology has applications across commercial markets including diagnostics, immuno-chemistry, and therapeutics.

 

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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.

 

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Rare events, path sampling and the OpenPathSampling package

 

In the last few years, modelling of rare events has made tremendous progress and several computational methods have been put forward to study these events. Despite this effort, new approaches have not yet been included, with adequate efficiency and scalability, in common simulation packages. One objective of the Classical Dynamics Work Package of the project E-CAM is to close this gap. The present text is an easy-to-read article on the use of path sampling methods to study rare events, and the role of the OpenPathSampling package to performing these simulations. Practical applications of rare events sampling and scalabilities opportunities in OpenPathSampling are also discussed.

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Does our simulation community need EXASCALE ?

By Prof. David Ceperley, University of Illinois Urbana-Champaign

The computer simulation of electrons, atoms, molecules, and their assemblies in soft and hard matter is foundational for many scientific disciplines and important commercially. Exascale computing is coming and our community should take part as are our colleagues in lattice gauge theory, climate modeling, cosmology, genomics and other disciplines. Continue reading…

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Technology transfer from the academic world to industry is a difficult process in all scientific fields

By Prof. Mike Payne, University of Cambridge

In many countries there is increasing demand for measurable socio-economic impact from academic research. Perhaps the UK is furthest down this path with a significant fraction of the funding for Universities dependent on the ‘Impact’ (defined as impact outside of academia) of the research performed [1]. However much we might wish to ignore this trend, I am convinced that it will only increase, at least over the short to medium term. I also believe that, as a community, Continue reading…

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High Performance Computing, Computer Simulation, and Theoretical Physics: Evolution or Revolution?

By Prof. Giovanni Ciccotti, University of Rome La Sapienza

Numerical physics, i.e. numerical calculations serving the needs of traditional theoretical physics, exists at least since the times of Galileo, and probably long before. As Computer Simulation (started with solving problems in Statistical Mechanics), it exists only since the end of the second World War. It is based on the possibility of having computation speeds largely beyond human capabilities, even including speeds reachable by exploiting team work. Continue reading…

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