Mesoscale simulation of billion atom complex systems using thousands of GPGPU’s, an industry success story

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Dr. Jony Castagna, Science and Technology Facilities Council, United Kingdom


Abstract

Jony Castagna recounts his transition from industry scientist to research software developer at the STFC, his E-CAM rewrite of  DL_MESO allowing the simulation of billion atom systems on thousands of GPGPUs, and his latest role as Nvidia ambassador focused on machine learning.

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Software vendor SMEs as a boost for technology transfer in industrial simulative pipelines

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The E-CAM Scoping Workshop “Building the bridge between theories and software: SME as a boost for technology transfer in industrial simulative pipelines”, organised in May 2018 at the Fondazione Istituto Italiano di Tecnologia (IIT), Genoa, brought together top-level scientists of the E-CAM community with expertise in statistical mechanics, multi-scale modeling and electronic structure, and representatives of pharmaceutical and material industries, with the objective to identify the major gaps which still hamper a systematic exploitation of accurate computer simulations in industrial R&D. Special attention was given to the role of SMEs devoted to simulative software development, and several software vendor SMEs were present at the meeting.

The meeting highlighted the role of software vendor SMEs as a key link for the uptake of modelling in industry. They can play an increasingly important role not only in translating the science developed in academia into a proper technological transfer process, but also in building a scientific bridge between the industry requirements in terms of automation and the new theories and algorithms developed at an academic level. There was also a consensus that EU funded Centers of Excellence for Computing Applications, such as E-CAM, can provide an opportunity to enhance the expertise and scope of software vendors SMEs.

Read the full report here.

Read the workshop scientific report
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E-CAM Case Study: Mesoscale models for polarisable solvents: application to oil-water interfaces

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Dr. Silvia Chiacchiera, Science and Technology Facilities Council, United Kingdom

Abstract

Water is a polar liquid and has a dielectric permittivity much higher than typical apolar liquids, such as light oils. This strong dielectric contrast at water-oil interfaces affects electrostatics and is important, for example, to include these effects to describe biomolecular processes and water-oil mixtures involving surfactants, as detergents. In this pilot project, developed in collaboration with Unilever and Manchester University, we have proposed and analysed a class of polarisable solvent models to be used in Dissipative Particle Dynamics (DPD), a coarse-grained particle-based simulation method commonly used in various industrial sectors. Related software modules for the DL_MESO package have also been developed.

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The Curse of Dimensionality in Data-Intensive Modeling in Medicine, Biology, and Diagnostics

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With Prof. Tim Conrad (TC), Free University of Berlin, and Dr. Donal Mackernan (DM), University College Dublin.

Abstract

Until recently the idea that methods rooted in statistical physics could be used to elucidate phenomena and underlying mechanisms in biology and medicine was widely considered to be a distant dream.  Elements of that dream are beginning to be realized, aided very considerably by machine learning and advances in measurement, exemplified by the development of large-scale biomedical data analysis for next-generation diagnostics. In this E-CAM interview of Tim Conrad,  the growing importance of diagnostics in medicine and biology is discussed. One difficulty faced by such developments and shared with particle-based simulation is the “curse of dimensionality”. It is manifest in problems such as: (a) the use of a very large number of order parameters when trying to identify reaction mechanisms, nucleation pathways, metastable states, reaction rates; polymorph recognition (b) machine learning  applied to electronic structure  problems – such as neural network based potentials need very high dimensional basis sets; (c) systematic coarse-graining would ideally start with a very high dimensional space and systematically reduce the dimension.  The opportunities and challenges for scientists engaging with industry are also discussed. Tim Conrad is Professor of “Medical Bioinformatics” at the Institute of Mathematics of the Free University of Berlin and head of MedLab, one of the four laboratories of the Modal research campus. MODAL is a public-private partnership project which conducts mathematical research on data-intensive modeling, simulation, and optimization of complex processes in the fields of energy, health, mobility, and communication.  Tim Conrad is also the founder of three successful start-up companies.

In this E-CAM interview with Prof. Tim Conrad, the growing importance of diagnostics in medicine and biology is discussed, including concepts rooted in signal analysis relevant to systematic dimensional reduction, and pattern recognition, and the possibilities of their application to systematic coarse-graining. The opportunities and challenges for scientists of start-up companies are also discussed based on experience.

 

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E-CAM Case Study: Designing control pulses for superconducting qubit systems with local control theory

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Dr. Momir Mališ, École Polytechnique Fédérale de Lausanne, Switzerland

 

Abstract

A quantum logic gate is one of the key components of the quantum computer, and designing an effective quantum universal gate is one of the major goals in the development of quantum computers. We have developed a software based on local control theory to design efficient state preparation control pulses for universal quantum gates which drive full population transfer between qubit states.

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

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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

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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

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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

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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

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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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