State-of-the art workshop: Challenges in Multiphase Flows

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If you are interested in attending this event, please visit the CECAM website here.

Workshop Description

The general topic of the event is computational methods to study multiphase flows [1,2]. Such methods are applied in very different disciplines, such as statistical physics, materials science, applied mathematics, and engineering, with applications ranging from geophysical to micro scales. Examples include volcano eruptions, oil recovery, and the dynamics of droplets on structured surfaces (“lotus effect”). The computational approaches to tackle these problems are as disparate as the phenomena themselves and the corresponding scientific communities, which rarely communicate amongst each other. The purpose of this school and workshop is to bring these various practitioners together for a fruitful exchange with the aim of improving the methodological toolbox which is still facing significant problems.

From the computational point of view, three major approaches (which shall all be covered) are commonly used: (i) sharp interface methods that keep track of the interface position [3]; (ii) smeared interface methods, which again may be subdivided into level set approaches [4-6] and methods based upon a Cahn-Hilliard free energy, or similar (to be discussed in the next paragraph) and finally (iii) methods which average over several phases being present in one volume element [7-9].

Concerning Cahn-Hilliard based approaches and similar, a whole plethora of methods has been developed. In metallurgy and other branches of materials science, phase-field models are fairly popular and have been particularly successful in the prediction of solid structures and their dynamic formation [10-15]. For fluid systems, the usual approach has been standard Computational Fluid Dynamics, based upon Finite Elements / Finite Differences / Finite Volume discretizations. These have recently been generalized to also include thermal fluctuations [16], which are typically needed for modeling phenomenena in the soft-matter domain, i.e. the micro- and nanoscale. Instead of using an Eulerian grid, an alternative discretization of the Navier-Stokes equations is also possible in terms of Lagrangian particles; this is the so-called Smoothed Particle Hydrodynamics (SPH) method, which has been used for macroscale multiphase flows for quite a while [17,18]. An exciting recent development has generalized SPH to also include thermal fluctuations [20,21], which was subsequently combined with the multiphase methodology [22,23].

A substantial body of work is based on the Lattice Boltzmann method [24]. While the original version was for an ideal gas on the macroscale, it has been generalized to include thermal fluctuations [25] and also multiphase flows, where typically the Shan-Chen model [26], the Swift-Yeomans model [27,28], or variants thereof [29,30] are being used. Thermal fluctuations have been included as well [31]. Quite successful applications include spinodal decomposition [32], Pickering emulsions [33-35], and flow of droplets past structured surfaces [36]. The Lattice Boltzmann method is particularly well-suited for modern parallel computer architectures and hence considerations of computational efficiency have played an important role in the literature [37,38].

A problem that has so far not been solved fully satisfactorily is the appearance of so-called “spurious currents” at an interface, which are a mere discretization artifact. Though also present in standard grid-based CFD calculations [39], they seem to have mainly been discussed in the Lattice Boltzmann literature [40-42]. An important goal of the event will be to critically discuss such artifacts, as well as issues of thermodynamic consistency. This will be targeted at (i) avenues toward systematic understanding, reduction and ultimate elimination of such undesired effects, but also at (ii) the more pragmatic question of how far these issues matter in practical applications.

 

References

[1] Prosperetti, A. & Tryggvason, G., ed. (2009), Computational Methods for Multiphase Flow, Cambridge University Press, Cambridge; New York.

[2] Tryggvason, G.; Scardovelli, R. & Zaleski, S. (2011), Direct Numerical Simulations of Gas-Liquid Multiphase Flows, Cambridge University Press, Cambridge; New York.

[3] Tryggvason, G.; Bunner, B.; Esmaeeli, A.; Juric, D.; Al-Rawahi, N.; Tauber, W.; Han, J.; Nas, S. & Jan, Y. J. (2001), A Front-Tracking Method for the Computations of Multiphase Flow, Journal of Computational Physics 169(2), 708–759.

[4] Olsson, E. & Kreiss, G. (2005), A conservative level set method for two phase flow, Journal of Computational Physics 210(1), 225–246.

[5] Olsson, E.; Kreiss, G. & Zahedi, S. (2007), A conservative level set method for two phase flow II, Journal of Computational Physics 225(1), 785–807.

[6] Zahedi, S.; Gustavsson, K. & Kreiss, G. (2009), A conservative level set method for contact line dynamics, Journal of Computational Physics 228(17), 6361–6375.

[7] Hassanizadeh, M. & Gray, W. G. (1979), General conservation equations for multi-phase systems: 1. Averaging procedure, Advances in Water Resources 2, 131–144.

[8] Hassanizadeh, M. & Gray, W. G. (1979), General conservation equations for multi-phase systems: 2. Mass, momenta, energy, and entropy equations, Advances in Water Resources 2, 191–203.

[9] Hassanizadeh, M. & Gray, W. G. (1980), General conservation equations for multi-phase systems: 3. Constitutive theory for porous media flow, Advances in Water Resources 3(1), 25–40.

[10] Echebarria, B.; Folch, R.; Karma, A. & Plapp, M. (2004), Quantitative phase-field model of alloy solidification, Physical Review E 70(6), 061604.

[11] Folch, R. & Plapp, M. (2005), Quantitative phase-field modeling of two-phase growth, Physical Review E 72(1), 011602.

[12] Plapp, M. (2011), Unified derivation of phase-field models for alloy solidification from a grand-potential functional, Physical Review E 84(3), 031601.

[13] Steinbach, I.; Pezzolla, F.; Nestler, B.; Seeºselberg, M.; Prieler, R.; Schmitz, G. J. & Rezende, J. L. L. (1996), A phase field concept for multiphase systems, Physica D: Nonlinear Phenomena 94(3), 135–147.

[14] Nestler, B.; Garcke, H. & Stinner, B. (2005), Multicomponent alloy solidification: Phase-field modeling and simulations, Physical Review E 71(4), 041609.

[15] Janssens, K. G. F. (2007), Computational Materials Engineering: An Introduction to Microstructure Evolution, Academic Press, Amsterdam; Boston.

[16] Chaudhri, A.; Bell, J. B.; Garcia, A. L. & Donev, A. (2014), Modeling multiphase flow using fluctuating hydrodynamics, Physical Review E 90(3), 033014.

[17] Monaghan, J. J. & Kocharyan, A. (1995), SPH simulation of multi-phase flow, Computer Physics Communications 87(1), 225–235.

[18] Monaghan, J. J. & Rafiee, A. (2012), A simple SPH algorithm for multi-fluid flow with high density ratios, International Journal for Numerical Methods in Fluids 71(5), 537–561.

[19] Morris, J. P. (2000), Simulating surface tension with smoothed particle hydrodynamics, International Journal for Numerical Methods in Fluids 33(3), 333–353.

[20] Espanol, P. & Revenga, M. (2003), Smoothed dissipative particle dynamics, Physical Review E 67(2), 026705.

[21] Vazquez-Quesada, A.; Ellero, M. & Espanol, P. (2009), Consistent scaling of thermal fluctuations in smoothed dissipative particle dynamics, The Journal of Chemical Physics 130(3), 034901.

[22] Hu, X. Y. & Adams, N. A. (2006), A multi-phase SPH method for macroscopic and mesoscopic flows, Journal of Computational Physics 213(2), 844–861.

[23] Hu, X. Y. & Adams, N. A. (2007), An incompressible multi-phase SPH method, Journal of Computational Physics 227(1), 264–278.

[24] Krueger, T.; Kusumaatmaja, H.; Kuzmin, A.; Shardt, O.; Silva, G. & Viggen, E. M. (2017), The Lattice Boltzmann Method: Principles and Practice, Springer International Publishing.

[25] Duenweg, B. & Ladd, A. J. C. (2009), Lattice Boltzmann Simulations of Soft Matter Systems, in Advanced Computer Simulation Approaches for Soft Matter Sciences III, Springer, Berlin, Heidelberg, , pp. 89–166.

[26] Shan, X. & Chen, H. (1994), Simulation of nonideal gases and liquid-gas phase transitions by the lattice Boltzmann equation, Physical Review E 49(4), 2941–2948.

[27] Swift, M. R.; Osborn, W. R. & Yeomans, J. M. (1995), Lattice Boltzmann Simulation of Nonideal Fluids, Physical Review Letters 75(5), 830–833.

[28] Swift, M. R.; Orlandini, E.; Osborn, W. R. & Yeomans, J. M. (1996), Lattice Boltzmann simulations of liquid-gas and binary fluid systems, Physical Review E 54(5), 5041–5052.

[29] Sbragaglia, M.; Benzi, R.; Biferale, L.; Succi, S.; Sugiyama, K. & Toschi, F. (2007), Generalized lattice Boltzmann method with multirange pseudopotential, Physical Review E 75(2), 026702.

[30] Krueger, T.; Frijters, S.; Guenther, F.; Kaoui, B. & Harting, J. (2013), Numerical simulations of complex fluid-fluid interface dynamics, The European Physical Journal Special Topics 222(1), 177–198.

[31] Thampi, S. P.; Pagonabarraga, I. & Adhikari, R. (2011), Lattice-Boltzmann-Langevin simulations of binary mixtures, Physical Review E 84(4), 046709.

[32] Kendon, V. M.; Cates, M. E.; Pagonabarraga, I.; Desplat, J.-C. & Bladon, P. (2001), Inertial effects in three-dimensional spinodal decomposition of a symmetric binary fluid mixture: a lattice Boltzmann study, Journal of Fluid Mechanics 440, 147–203.

[33] Stratford, K.; Adhikari, R.; Pagonabarraga, I.; Desplat, J.-C. & Cates, M. E. (2005), Colloidal Jamming at Interfaces: A Route to Fluid-Bicontinuous Gels, Science 309(5744), 2198–2201.

[34] Jansen, F. & Harting, J. (2011), From bijels to Pickering emulsions: A lattice Boltzmann study, Physical Review E 83(4), 046707.

[35] Michele, L. D.; Fiocco, D.; Varrato, F.; Sastry, S.; Eiser, E. & Foffi, G. (2014), Aggregation dynamics, structure, and mechanical properties of bigels, Soft Matter 10(20), 3633–3648.

[36] Asmolov, E. S.; Schmieschek, S.; Harting, J. & Vinogradova, O. I. (2013), Flow past superhydrophobic surfaces with cosine variation in local slip length, Physical Review E 87(2), 023005.

[37] Cates, M. E.; Desplat, J.-C.; Stansell, P.; Wagner, A. J.; Stratford, K.; Adhikari, R. & Pagonabarraga, I. (2005), Physical and computational scaling issues in lattice Boltzmann simulations of binary fluid mixtures, Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 363(1833), 1917–1935.

[38] Schmieschek, S.; Shamardin, L.; Frijters, S.; Krueger, T.; Schiller, U. D.; Harting, J. & Coveney, P. V. (2017), LB3D: A parallel implementation of the Lattice-Boltzmann method for simulation of interacting amphiphilic fluids, Computer Physics Communications 217, 149–161.

[39] Zahedi, S.; Kronbichler, M. & Kreiss, G. (2011), Spurious currents in finite element based level set methods for two-phase flow, International Journal for Numerical Methods in Fluids 69(9), 1433–1456.

[40] Shan, X. (2006), Analysis and reduction of the spurious current in a class of multiphase lattice Boltzmann models, Physical Review E 73(4), 047701.

[41] Lee, T. & Fischer, P. F. (2006), Eliminating parasitic currents in the lattice Boltzmann equation method for nonideal gases, Physical Review E 74(4), 046709.

[42] Pooley, C. M. & Furtado, K. (2008), Eliminating spurious velocities in the free-energy lattice Boltzmann method, Physical Review E 77(4), 046702.

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E-CAM State of the Art Workshop: CHALLENGES IN MULTIPHASE FLOWS

We would like to draw your attention to a school cum workshop on

CHALLENGES IN MULTIPHASE FLOWS

that will run on Dec 9-12, 2019, at the Monash University Prato Center,
see http://monash.it/, in Tuscany. The event is an E-CAM state-of-the-art
workshop, and its aim is to focus on computer
simulation methods for multiphase systems and their dynamics, and
their strengths and shortcomings. This is a topic that is relevant in
physics, mathematics, chemistry, and engineering, and we are trying to
bring these communities together for a fruitful exchange. At the same
time, a set of advanced lectures at the school is intended to provide
a solid foundation of background knowledge. For more information (in
particular, the list of Invited Speakers), see the

Main web site for the event

Registration is now open. Regular participants need to pay a fee of
500 Australian Dollars (roughly 300 Euros) for meals etc.; however the
first 25 students (with proven status) who register may attend for free.

DEADLINE for registration and abstract submission is September 22.

Please do not hesitate to contact the organisers (contact information on the main website for the event) if you feel you need more information beyond what is provided on the web.

The Organisers

Burkhard Duenweg, Mainz
Ravi Prakash Jagadeeshan, Melbourne
Ignacio Pagonabarraga, Lausanne

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Recent developments in quantum dynamics, an E-CAM state-of-the-art workshop

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If you are interested in attending this event, please visit the CECAM website here.

Workshop Description

The proposed workshop will gather a broad community of researchers in the field of quantum dynamics, who are actively investigating the interplay of electronic and nuclear correlation in problems spanning multiple length and time scales, and who are seeking to develop and apply state-of-the-art (SOA) methodologies to systems of increasing complexity.

Continuing in the spirit of the first E-CAM SOA workshop, held in 2016 in Lausanne, a broad overview of the field of quantum dynamics will be presented. Current and emergent quantum dynamics methodologies will be critically discussed from their basic assumptions to their most recent extensions, including their pitfalls and possible improvements, in the hope that the ideas exchanged will promote exciting new developments. Participants will also be asked to address, in particular, aspects related to the software tools that implement the different methods, evaluating development schemes (community efforts, in-house coding), HPC-readiness (e.g. portability, scalability, benchmarking), and ease of use. An assessment of the “readiness for experiments and industry” will also be pursued, identifying new problems of experimental and industrial interest where quantum-dynamical effects are relevant, presenting success stories, and – crucially – evaluating critically the gap between available methods and codes and the needs of non-professional users to suggest means to reduce it.

The format of the workshop will conform to the Tentative Timetable included in this proposal. This format is based on positive feedback following the CECAM Quantum Dynamics meetings that took place in Paris (2016) and Lausanne (2017). Ample time for discussions is set aside, in agreement with CECAM and E-CAM recommendations. We will organize the topics into five sessions:

I. Theoretical Foundations of Quantum Dynamics in Molecular and Condensed Phase Systems
II. Real-time Path Integral and Quantum Master Equation Techniques
III. Trajectory-Based Quantum Molecular Dynamics: Methods and Applications
IV. Nuclear Quantum Effects, Path Integral Molecular Dynamics, and Vibrational Spectroscopy
V. Numerically Exact Methods

We will also invite chairpeople that will be asked to actively encourage exchanges and cross-fertilization in the discussion sessions. Speakers and participants will also be asked to highlight formal and algorithmic connections between different methods and to mention, or propose sets of benchmarks to assess relative performances. In this SOA workshop, we have chosen not to allot time for contributed talks. All participants are, however, expected to contribute to the discussions and will be given a chance to present their work at the poster session or, informally, as has become customary in the CECAM environment, during the long coffee breaks.

The connection to E-CAM will be highlighted through a special discussion session (VI: Software development in Quantum Dynamics) and presentation of the most recent software modules developed during the extended software development workshops, which runs in parallel to this workshop series. Experts from E-CAM and from other experiences of systematic software development in the area (e.g. MolSSI, GPU based codes, i-PI) will discuss their experience with the goal to share good practices, identify new synergies, provide all participants with an opportunity to know and contribute (if interested) to community based codes or to initiate new coordinated activities in the area.

 

References

[1] D. Schapers, B. Zhao, U. Manthe, Chemical Physics 509, 37-44, (2018).
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[19] TE Markland, M Ceriotti – Nature Reviews Chemistry, 2018.
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[21] Ceriotti, M.; Bussi, G.; Parrinello, M. Phys. Rev. Lett. 2009, 103, 030603.
[22] Javier Hernández-Rojas, Florent Calvo, and Eva Gonzalez Noya, J. Chem. Theo. Comp. 2015 11 (3), 861-870.
[23] N. Ananth, J. Chem. Phys. 139, 124102 (2013); J.O. Richardson and M. Thoss ibid., 139, 031102, 2013.
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State-of-the-Art Workshop: Large scale activated event simulations

If you are interested in attending this event, please visit the CECAM website here.

Workshop Description

Running on powerful computers, large-scale molecular dynamics (MD) simulations are used routinely to simulate systems of millions of atoms providing crucial insights on the atomistic level of a variety of processes of interest in physics, materials science, chemistry and biology. For instance, MD simulations are extensively used to study the dynamics and interactions of proteins, understand the properties of solutions or investigate transport in and on solids. From a technological point of view, molecular dynamics simulations play an important role in many fields such as drug development, the discovery of new materials, oil extraction or energy production. Indeed, enormous amounts of data are produced every day by molecular dynamics simulations running on high performance computers around the world and one of the big challenges related to such simulations is to make sense of the data and obtain mechanistic understanding in terms of low-dimensional models that capture the crucial features of the processes under study. Another central challenge is related to the time scale problem often affecting molecular dynamics simulations. More specifically, despite the exponential increase in computing power witnessed during the last decades and the development of efficient molecular dynamics algorithms, many processes are characterized by typical time scales that are still far beyond the reach of current computational capabilities. Addressing such time scale problems and developing scientific software able to overcome them is one of the central goals of Work Package 1 (WP1-Classical Molecular Dynamics) of the E-CAM Project.

Three fundamental problems are intimately tied to the time scale problem of classical molecular dynamics simulation:

1) The calculation of the populations of metastable states of an equilibrium system. Such populations can be expressed in terms of free energies and hence this problem boils down to the efficient calculation of free energies.

2) The sampling of transition pathways between long-lived (meta)stable states and the calculation of reaction rate constants.

3) The extraction of useful mechanistic information from the simulation data and the construction of low-dimensional models that capture the essential features of the process under study. Such models serve as the basis for the definition of reaction coordinates that enable in-depth studies of the process at hand, e.g. by computing the free energy and kinetics.

The central goal of this workshop is to review new algorithmic developments that address the computational challenges mentioned above with a particular emphasis on implications for industrial applications. In particular, the workshop aims at identifying software modules that should be developed to make efficient and scalable algorithms available to the academic and industrial community. Another goal of the workshop is to identify specific collaboration projects with industrial partners. A dedicated half-day session will be organized specifically for this purpose. To establish the needs of the community and lay out possible directions for development, we will bring together a diverse group of people including software developers, users of HPC infrastructure and industrial researchers.

The proposed workshop is a follow-up of the first ECAM State-of-the-art Workshop of WP1, which took place in the summer of 2016 at the Lorentz Center in Leiden, The Netherlands. At this workshop, participants reviewed current rare event methods including path sampling, milestoning, metadynamics, Markov state modeling, diffusion maps, dimension reduction, reaction coordinate optimization, machine learning, and unsupervised cluster methods, and explored ways to improve these methods. Particular attention was devoted to the integration of popular MD packages such as Gromacs, NAMD, Charmm, Amber, ACEMD, MOIL, LAMMPS with enhanced analysis and advanced sampling tools including Plumed (a package for enhanced sampling and collective variable analysis), pyEmma, and MSMBuilder (packages for Markov sate model analysis).

Notwithstanding the great capabilities of existing methods and software, several challenges remain and will be discussed at the proposed workshop in Vienna:

– Extracting order parameters from molecular simulations to construct low dimensional models. This point is important because there is no straightforward recipe to reduce the dimensions to meaningful variables and progress in this area is urgently needed.

– Efficient Methods for sampling rare pathways. Here the goal is to create the molecular trajectory data using advanced sampling algorithms.

– Machine learning algorithms. Automatic analysis methods may offer new ways to guide simulations and construct reaction coordinates from molecular trajectories.

– Better ways to integrate simulations and experiments. It is important to connect the proposed computational methods to experimental probes and integrate experimental information into the analysis of computer simulation data.

More specifically, questions that will be addressed at the proposed workshop include:

1. How to obtain the best low dimension model for the process of interest?

2. How can we use machine learning to find collective variables and reaction coordinates?

3. When can reaction coordinates, which often constitute the slow variables of a process, be used to coarse-grain the dynamics? When not?

4. What if multiple transitions are important? Do we resort to kinetic networks or use multiple reaction coordinates? Should one identify a single (possibly complicated) reaction coordinate, or try to construct a Markov state model (MSM) using many metastable states?

5. When is it possible to reduce a complex problem to diffusion on a one dimensional free energy landscape, and when do we need a network Markov model?

6. How can experiments test reaction coordinate predictions? How do we connect to experiments?

7. How can extreme-scale computational resources be used efficiently to address these questions?

8. How can progress in these questions help to address problems of industrial interest?

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State-of-the-Art Workshop: Improving the accuracy of ab-initio predictions for materials

If you are interested in attending this event, please visit the CECAM website here.

Workshop Description

Ab-initio simulation methods are the major tool to perform research in condensed matter physics, materials science, quantum and molecular chemistry. They can be classified in terms of their accuracy and efficiency, but typically more accurate means less efficient and vice-versa. The accuracy depends mainly on how accurate one can solve the electronic problem. The most accurate algorithms are the wave-function based methods, such as Full CI, Coupled Cluster (CC), and Quantum Monte Carlo (QMC) followed by the Density Functional Theory-(DFT)-based methods and finally more approximate methods such as Tight-Binding. Another impor- tant consideration is how the accuracy of a given method scales with the size of the system under consideration. Among the wave-function based methods, the accuracy of traditional quantum chemistry methods can be sys- tematically improved but their scaling with system size limits their applicability to small molecules. On the other hand, QMC methods have a much more tractable scaling and have, in spite of the “fermion sign problem” and the commonly used fixed-node approximation, because the energies are variational upper bounds, a way of systematically improving the accuracy. Recently there has been much progress in the use of pseudopotentials and the systematic improvement of nodal surfaces using backflow, and multiple determinants. [1, 2, 3]
Conversely DFT based methods are based on a plethora of different self-consistent mean field approxima- tions, each one tuned to best represent a class of systems but with limited transferability. Despite progress in developing more general functionals [4, 5, 6], DFT is missing an “internal” accuracy scale; its accuracy is gen- erally established against more fundamental theories (like CC or QMC) or against experiments. DFT methods are very popular because their favorable scaling with system size, the same as for QMC, but with a smaller prefactor.
In a number of recent applications [7, 8] it was found that inclusion of nuclear quantum effects (NQE) worsen considerably the agreement between DFT predictions and experiments. This is ascribed to the inac- curacies of DFT. This illustrates the importance of not using experimental data alone to improve the DFT functional but instead calculations using more fundamental methods. There has been a recent effort to establish the accuracy of DFT approximations by benchmarking with QMC calculations not only for equilibrium geome- tries but also for thermal configurations. This benchmarking can be customized for the individual molecules at a given temperature and pressure and geometry [9, 10, 11, 12].
Another important aspect concerns finite size effects in modelling extended systems. Although corrections can be developed for homogenous systems, for more complex situations with several characteristic length scales one needs to consider systems sizes that cannot be tackled by ab-initio methods. In these applications one needs to use an effective interaction energy. A recent development is the use of Machine Learning (ML) techniques to obtain energy functions with ab-initio accuracy [13, 14, 15]. Their transferability and accuracy assessment is still unsolved to some extent but progress is rapid. A related development is to use ML methods to by-passing the Kohn-Sham paradigm of DFT and directly address potential-density map [16, 17, 18]

The following is a list of topics that will be discussed during the meeting:
• Benchmarking existing DFT functionals with QMC. DFT has the potential to be accurate, but the main problem with its predictive power is that its accuracy can be system dependent. QMC was instrumental in developing the first exchange-correlation approximations (e. g. LDA), and we envisage that it can play a substantial role to help the discovery and tuning of new functionals. In particular, the tuning of dispersion interactions appears to be a crucial elements still not fully controlled in modern DFT approximations while it plays a crucial role in many systems like hydrogen and hydrogen based materials such as water.
• ML approaches with QMC accuracy. Machine Learning (ML) has attracted significant interest recently, mainly because of its potential to study real life systems, and also to explore the phase space at a scale that is not available to ab-initio methods. However, crucial for the ML method is the quality of the training set. It is often possible to train a ML potential on small systems, where accurate energies and forces can be obtained by quantum chemistry methods. However, training sets including larger systems are needed. QMC has the potential to provide them especially going forward with exascale computing.
• opportunity for new exascale applications of QMC to impact simulation for larger systems and longer time scale. QMC is capable of exploiting parallelism very efficiently, and is probably one of the few methods already capable of running at the exascale level. ML methods on large data set are also inherently parallel and directly usable on exascale machines.
• We will address the problem of using and testing the force field derived for a small systems to those of a much larger size.
• We will discuss the use of ML methods to derive new classes of wave functions for QMC calculations of complex systems.

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Extreme-Scale State-of-the-Art Workshop

Goals of the Workshop:

The central goal of the 1st E-CAM Extreme-Scale State-of-the-art Workshop is to provide a forum for fellow E-CAM application end users and developers to:

  1. Identify emerging extreme-scale computing requirements across the centre, including from both academia and industry partners
  2. Increase the centre’s awareness of current and emerging HPC hardware and software technologies on the road to exascale computing
  3. Increase the centre’s awareness of PRACE services (Advanced Training, software enablement, and industry interactions)
  4. Interface with other members of the European HPC community
  5. Identify themes of future interest for the centre on the road to exascale computing

If you wish to apply for this workshop please do so through the CECAM website here.

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State of the Art Workshop: Meso and Multiscale Modelling

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

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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.

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Reaction Coordinates from Molecular Trajectories

This is the second E-CAM’s state of the art workshops, providing a discussion of developments in the field and assessing the impact of developed software on the academic community.

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Quantum Mechanics and Electronic Structure

This is the second of E-CAM’s Extended Software Development Workshops ESDW), which will take place in Paris. ESDW’s are training events  that include coding sessions and training lectures on computer hardware and advances in new architecture, parallel programming techniques and more.

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