- Spaces
Spaces
Use Spaces to collaboratively work with your team to manage and annotate data.
BioNT
BioNT provides training in digital skills relevant to the biotech and biomed sectors, while also fostering a community in these fields, engaging experienced learners and trainers. These self-paced learning courses cover topics from introductions to bioinformatics to machine learning, which you can use to learn these skills at your own pace.
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DOME
DOME 4.0 intends to offer an intelligent semantic industrial data ecosystem for knowledge creation across the entire materials to manufacturing value chains.
- 19 4 2
MultiXscale
MultiXscale is a EuroHPC JU Centre of Excellence in multiscale modelling. It is a collaborative 4-year project between the CECAM network and EESSI that will allow domain scientists to take advantage of the computational resources that will be offered by EuroHPC.
- 29 4 2
MARVEL
MARVEL targets the accelerated design and discovery of novel materials, via a materials’ informatics platform of database-driven high-throughput quantum simulations.
- 258 6 5
MᴀX
MaX (MAterials design at the eXascale) is a European Centre of Excellence which enables materials modelling, simulations, discovery and design at the frontiers of the current and future High Performance Computing (HPC), High Throughput Computing (HTC) and data analytics technologies.
- 289 12 4
Lhumos
this is a public space to showcase new features about the Lhumos training portal
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FocusCoE
A shared space for the other HPC Centres of Excellence to contribute content.
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- 9 1 3
ESDW: Topics in Classical MD
Classical molecular dynamics (MD) is a broad field, with many domains of expertise. Those specialist domains include topics like transition path sampling (which harvests many examples of a process in order to study it at a statistical level [1]), metadynamics (which runs a trajectory with modified dynamics that enhance sampling, and from which free energy profiles can be constructed [2]), as well as various topics focused on the underlying dynamics, either by providing better representations of the interactions between atoms (e.g., force fields [3] or neural network potentials [4]) or by changing the way the dynamics are performed (e.g., integrators [5]). <br> <br>Frequently, experts in one domain are not experienced with the software of other domains. This workshop aims to combine both depth, by extending domain-specific software, and breadth, by providing participants an opportunity to learn about software from other domains. As an extended software development workshop (ESDW), a key component of the workshop will be the development of modules that extend existing software packages. Ideally, some modules may connect multiple domain-specific packages. <br> <br>Topics at this workshop will include using and extending modern MD software in the domains of: <br> <br>* advanced path sampling methods (and the software package OpenPathSampling) <br>* metadynamics and the calculation of collective variables (and the software package PLUMED) <br>* machine learning for molecular dynamics simulatons (including local structure recognition and representation of potential energy surfaces) <br> <br>In addition, this workshop will feature an emphasis on performance testing and benchmarking software, with particular focus on high performance computing. This subject is relevant to all specialist domains. <br> <br>By combining introductions to software from different specialist fields with an opportunity to extend domain-specific software, this workshop is intended to provide opportunities for cross-pollination between domains that often develop independently. <br> <br>References <br>[1] Bolhuis, P.G. and Dellago, C. Trajectory‐Based Rare Event Simulations. Reviews in Computational Chemistry, 27, p. 111 (2010). <br>[2] A. Laio and F.L. Gervasio. Rep. Prog. Phys. 71, 126601 (2008). <br>[3] J.A. Maier, C. Martinez, K. Kasavajhala, L. Wickstrom, K.E. Hauser, and C. Simmerling. J. Chem. Theory. Comput. 11, 3696 (2015). <br>[4] T. Morawietz, A. Singraber, C. Dellago, and J. Behler. Proc. Natl. Acad. Sci USA, 113, 8368 (2016). <br>[5] B. Leimkuhler and C. Matthews. Appl. Math. Res. Express, 2013, 34 (2013).
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