Calendar

Organizers

• Mikko Alava
  Aalto University, Finland
• Brian Tighe
  TU Delft, The Netherlands
• Jan Astrom
  CSC It center for science, Finland
• Antti Puisto
  Aalto University, Finland

Description

In Discrete Element Methods the equation of motion of large number of particles is numerically integrated to obtain the trajectory of each particle [1]. The collective movement of the particles very often provides the system with unpredictable complex dynamics inaccessible via any mean field approach. Such phenomenology is present for instance in a seemingly simple systems such as the hopper/silo, where intermittent flow accompanied with random clogging occurs [2]. With the development of computing power alongside that of the numerical algorithms it has become possible to simulate such scenarios involving the trajectories of millions of spherical particles for a limited simulation time. Incorporating more complex particle shapes [3] or the influence of the interstitial medium [4] rapidly decrease the accessible range of the number of particles.

Another class of computer simulations having a huge popularity among the science and engineering community is the Computational Fluid Dynamics (CFD). A tractable method for performing such simulations is the family of Lattice Boltzmann Methods (LBMs) [5]. There, instead of directly solving the strongly non-linear Navier-Stokes equations, the discrete Boltzmann equation is solved to simulate the flow of Newtonian or non-Newtonian fluids with the appropriate collision models [6,7]. The method resembles a lot the DEMs as it simulates the the streaming and collision processes across a limited number of intrinsic particles, which evince viscous flow applicable across the greater mass.

As both of the methods have gained popularity in solving engineering problems, and scientists have become more aware of finite size effects, the size and time requirements to simulate practically relevant systems using these methods have escaped beyond the capabilities of even the most modern CPUs [8,9]. Massive parallelization is thus becoming a necessity. This is naturally offered by graphics processing units (GPUs) making them an attractive alternative for running these simulations, which consist of a large number of relatively simple mathematical operations readily implemented in a GPU [8,9].

This workshop is organised in collaboration with the CSC IT Center for Science (which is also a PRACE Advanced Training Center PATC), and will mix three different ingredients: (1) workshop on state-of-the-art challenges in computational science and software, (2) CSC -run school, and (3) coding sessions with the aid of CSC facilities and expertise. We plan to involve and contact a number of groups in Europe interested in topics where the usage of this methodology is essential and/or current code development / usage is actively going on.

 

 

References

[1] P.A. Cundall and O.D.L. Strack, Geotechnique 29, 47–65 (1979).
[2] H. G. Sheldon and D. J. Durian, Granular Matter 6, 579-585 (2010).
[3] A. Khazeni, Z. Mansourpour Powder Tech. 332, 265-278 (2018).
[4] J. Koivisto, M. Korhonen, M. J. Alava, C. P. Ortiz, D. J. Durian, A. Puisto, Soft Matter 13 7657-7664 (2017).
[5] S. Succi,The lattice Boltzmann equation: for fluid dynamics and beyond. Oxford university press, (2001).
[6] L. S. Luo, W. Liao, X. Chen, Y. Peng, W. Zhang, Phys. Rev. E, 83, 056710 (2011).
[7] S. Gabbanelli, G.Drazer, J. Koplik, Phys. Rev. E, 72, 046312 (2005).
[8] N Govender, R. K. Rajamani, S. Kok, D. N. Wilke, Minerals Engin. 79, 152-168 (2015).
[9] P.R. Rinaldi, E. A. Dari, M. J. Vénere, A. Clausse, Simulation Modelling Practice and Theory, 25, 163-171 (2012).

Organizers

• Donal MacKernan
  University College Dublin, Ireland
• Vladimir Lobaskin
  University College Dublin, Ireland

Description

The overarching theme of this proposed E-CAM Transverse Extended Software Development Workshop is the design and control of molecular machines including sensors, enzymes, therapeutics, and transporters built as fusion proteins or nanocarrier-protein complexes, and in particular, the software development and interfacing that this entails. Several immuno-diagnostic companies and molecular biology experimental groups have expressed a strong interest in the projects at the core of this proposal. The proposed ESDW is transverse as it entails the use of methodologies from two E-CAM Scientific Workpackages: WP1 (Advanced MD/rare-events methods) and WP4 (Mesoscale/Multiscale simulation).

Fusion proteins are sets of two or more protein modules linked together where the underlying genetic codes of each module and the fusion protein itself are known or can be easily inferred. The fusion protein typically retains the functions of its components, and in some cases gains additional functions. They occur in nature, but also can be made artificially using genetic engineering and biotechnology- and used for a wide variety of settings ranging from unimolecular FRET sensors, novel immuno-based cancer drugs, enzymes [1,2] and energy conversion (for example efficient generation of alcohol from cellulose) [3,4]. Fusion proteins can be expressed using genetic engineering in cell lines, and purified for in-vitro use using biotechnology. Much of the design work is focused on how different modules are optimally linked or fused together via suitable peptides, rather than on internal changes of modules. Optimizing such designs experimentally can be done through for example random mutations, but a more controlled approach based on underlying molecular mechanisms is desirable, for which a pragmatic multiscale approach is ideally suited combining bioinformatics and homology, coarse-graining, detailed MD and rare-event based methods, and machine learning. The figure on the front of this proposal is a representative example of a fusion protein sensor designed to bind to a specific RNA nucleic acid sub-sequence, which causes an optimized hinge-like protein to close and in the process bring two fluorescence proteins together allowing the binding event to be observed optically through FRET microscopy.

Nanocarriers (NC) are promising tools for cancer immunotherapy and other diagnostic and therapeutic applications. NCs can be decorated on their surface with molecules that facilitate target-specific antigen delivery to certain antigen-presenting cell types or tumor cells. However, the target cell-specific uptake of nano-vaccines is highly dependent on the modifications of the NC itself. One of these is the formation of a protein corona [5] around NC after in vivo administration. Appropriate targeting of NC can be affected by unintended interactions of the NC surface with components of blood plasma and/or with cell surface structures that are unrelated to the specific targeting structure. The protein corona around NC may affect their organ-specific or cell type-specific trafficking as well as endocytosis and/or functional properties of the NC. Most importantly, the protein corona has been shown to interfere with targeting moieties used to induce receptor-mediated uptake of the NC, both inhibiting and enhancing internalization by specific cell types [5]. Moreover, the protein corona is taken up by the target cell, which may alter their function. Therefore, tailoring the surface properties of the NC to facilitate the adsorption of specific proteins and control the structure of the corona can help to significantly improve their performance. Modification of surface properties, e.g. via grafting olygomers, is also known to affect the preferred orientation of adsorbed proteins and, therefore, their functionality [6]. The molecular design would include the selection of appropriate NC coating and the type of antibody to optimize the NC uptake.

Mesoscale simulation is required to understand the thermodynamics and kinetics of protein adsorption on the NCs with engineered surfaces [7] and to achieve the desired structure with preferred adsorption of the selected antigen. However, the aforementioned issues often require biological and chemical accuracy that typical mesoscale models cannot achieve unless buttressed by accurate simulations at an atomistic/molecular level, rare-event methods and machine learning.

A pragmatic approach towards the enhancement of fusion proteins and NC’s is as follows.

(i) Molecular designs are initially developed and optimized as simple CG models and include the use of information theory and machine learning.

(ii) The solution of the inverse problem of building the fusion protein or the NC-protein complex to match the design requires a multiscale approach combining mesoscale modeling, molecular dynamics, rare-event methods, machine learning, homology, mutation, solvent conditions.

(iii) Iterate steps (i) and (ii) to optimize the design, and in the process collect data for machine learning driven design.

(iv) Final validation using detailed MD, rare-event methods and HPC

The ESDW we plan will over the course of two 5 day meetings with several intervening months produce multiple software modules including the following.
(a) C/C++/Modern Fortran or python based codes to build and optimize simple CG models of fusion proteins or NC-protein complexes using information theory and machine learning.

(b) Semi-automated pipelines to solve the inverse problem of building the fusion protein or the NC to match the design. This will involve interfacing with md/ mesoscale engines such as LAMMPS, Gromacs, OPENMM, EXpresso, rare-event based methods such as PLUMED, and bioinformatics code such as I-TASSER, INTFOLD.

(c) Particle insertion/deletion methods for alchemistry – mutation of amino acids, changes in the solvent and associated changes in free energy properties.

(d) Codes to add corrections to coarse-grained models (bead models/martini) using detailed atomistic data (e.g. potential of mean force for key order parameters, structure factors etc) or experimental data where available.

While this is an ambitious plan, it is worth pointing out that a similar integrated approach to protein development was already made by the lab of John Chodera [8]. While it did not include the focus on fusion proteins or NC-protein complexes or incorporate systematically coarse-graining, it demonstrates both the feasibility of what we propose here and how to achieve practical solutions. Other ideas of a systematic approach to molecular design using MD simulation have been also proposed recently [9,10].

 

References

[1] H. Yang et al, The promises and challenges of fusion constructs in protein biochemistry and enzymology, Appl Microbiol Biotechnol (2016)
[2] Bochicchio, Anna et al, Designing the Sniper: Improving Targeted Human Cytolytic Fusion Proteins for Anti-Cancer Therapy via Molecular Simulation, Biomedicines, 5(1),9 (2017)
[3] Y. Fujita et al, Direct and Efficient Production of Ethanol from Cellulosic Material with a Yeast Strain Displaying Cellulolytic Enzymes, Appl Environ Microbiol. 68(10): 5136–5141 (2002)
[4] M. Gunnoo et al, Nanoscale Engineering of Designer Cellulosomes, dv Mater. 28(27):5619-4 (2016)
[5] M. Bros et al. The Protein Corona as a Confounding Variable of Nanoparticle-Mediated Targeted Vaccine Delivery, Front. Immunol. 9, 1760 (2018).
[6] I. Lieberwirth et al. The Role of the Protein Corona in the Uptake Process of Nanoparticles, 24, Supplement S1, Proceedings of Microscopy & Microanalysis (2018)
[7] H Lopez et al. Multiscale Modelling of Bionano Interface, Adv. Exp. Med. Biol. 947, 173-206 (2017)
[8] DL. Parton et al Ensembler: Enabling High-Throughput Molecular Simulations at the Superfamily Scale. PLoS Comput Biol 12(6): e1004728, (2016)
[9] PV. Komarov et al. A new concept for molecular engineering of artificial enzymes: a multiscale simulation, Soft Matter 12, 689-704 (2016)
[10] BA. Thurston et al. Machine learning and molecular design of self-assembling -conjugated oligopeptides, Mol. Sim. 44, 930-945 (2018)
[11] D. Carroll. Genome Engineering with Targetable Nucleases, Annu. Rev. Biochem. 83:409–39 (2014)

Organizers

• Burkhard Duenweg
  Max Planck Institute for Polymer Research, Mainz, Germany
• Ignacio Pagonabarraga
  Swiss Federal Institute of Technology, Switzerland
• Ravi Prakash Jagadeeshan
  Monash University, Melbourne, Australia

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.

Organisers

• Nick R. Papior
  Technical University of Denmark, Denmark
• Micael Oliveira
  Max Planck Institute for the Structure and Dynamics of Matter, Hamburg, Germany
• Yann Pouillon
  Universidad de Cantabria, Spain
• Volker Blum
  Duke University, Durham, NC, USA, USA
• Fabiano Corsetti
  Synopsys QuantumWise, Denmark
• Emilio Artacho
  University of Basque Country, United Kingdom

Description

The evolutionary pressure on electronic structure software development is greatly increasing, due to the emergence of new paradigms, new kinds of users, new processes, and new tools. Electronic structure software complexity is consequently also increasing, requiring a larger effort on code maintenance. Developers of large electronic structure codes are trying to relieve some complexity by transitioning standardized algorithms into separate libraries [BigDFT-PSolver, ELPA, ELSI, LibXC, LibGridXC, etc.]. This paradigm shift requires library developers to have a hybrid developer profile where the scientific and computational skill set becomes equally important. These topics have been extensively and publicly discussed between developers of various projects including ABINIT, ASE, ATK, BigDFT, CASTEP, FHI-aims, GPAW, Octopus, Quantum Espresso, SIESTA, and SPR-KKR.

High-quality standardized libraries are not only a highly challenging effort lying at the hands of the library developers, they also open possibilities for codes to take advantage of a standard way to access commonly used algorithms. Integration of these libraries, however, requires a significant initial effort that is often sacrificed for new developments that often not even reach the mainstream branch of the code. Additionally, there are multiple challenges in adopting new libraries which have their roots in a variety of issues: installation, data structures, physical units and parallelism – all of which are code-dependent. On the other hand, adoption of common libraries ensures the immediate propagation of improvements within the respective library’s field of research and ensures codes are up-to-date with much less effort [LibXC]. Indeed, well-established libraries can have a huge impact on multiple scientific communities at once [PETSc].

In the Electronic Structure community, two issues are emerging. Libraries are being developed [esl, esl-gitlab] but require an ongoing commitment from the community with respect to sharing the maintenance and development effort. Secondly, existing codes will benefit from libraries by adopting their use. Both issues are mainly governed by the exposure of the libraries and the availability of library core developers, which are typically researchers pressured by publication deliverables and fund-raising burdens. They are thus not able to commit a large fraction of their time to software development.

An effort to allow code developers to make use of, and develop, shared components is needed. This requires an efficient coordination between various elements:

– A common and consistent code development infrastructure/education in terms of compilation, installation, testing and documentation.
– How to use and integrate already published libraries into existing projects.
– Creating long-lasting synergies between developers to reach a “critical mass” of component contributors.
– Relevant quality metrics (“TRLs” and “SRLs”), to provide businesses with useful information .

This is what the Electronic Structure Library (ESL)[esl, esl-gitlab] has been doing since 2014, with a wiki, a data-exchange standard, refactoring code of global interest into integrated modules, and regularly organizing workshops, within a wider movement lead by the European eXtreme Data and Computing Initiative [exdci].

 

References

[BigDFT-PSolver] http://bigdft.org/Wiki/index.php?title=The_Solver_Package
[ELPA] https://gitlab.mpcdf.mgp.de/elpa/elpa
[ELSI] http://elsi-interchange.org
[LibXC] http://www.tddft.org/programs/libxc/
[LibGridXC] https://launchpad.net/libgridxc
[PETSc] https://www.mcs.anl.gov/petsc/
[esl] http://esl.cecam.org/
[esl-gitlab] http://gitlab.e-cam2020.eu/esl
[exdci] https://exdci.eu/newsroom/press-releases/exdci-towards-common-hpc-strategy-europe