Pillars & Work Packages

All Work Packages are currently looking for master and PhD students as well as postdocs

The NOMAD Center of Excellence is organized in three topical Pillars and thirteen Work Packages (WPs). The three pillars focus on exascale:

Pillars and WPs

Supporting the three ‘vertical’ research Pillars, we have ‘horizontal’ activities dedicated to

In addition, WP 10-12 address value-adding activities:

 

Brief Description of the Scientific Work Packages (WP 1-9)

Work Package 1: Exascale DFT

This WP has two major tasks ̶ enhancing the performance and functionality of the existing libraries ELPA and Libxc, hence allowing to profit and exploit the computational power of exascale platforms. It provides the opensource libraries, ELPA-X and Libxc-X, as major outcomes. The computational demand mandates close collaboration with WP8 in terms of co-design based on WP1 benchmark examples.

PI and Co-PI: Andreas Marek and Christian Carbogno

Work Package 2: Exascale Green-Function-Based Methods

This WP will enable the electronic structure community to apply accurate beyond-DFT methodologies, based on Green functions, to problems and systems currently out of reach with state-of-the-art computers and software. The physical properties covered by these methodologies are (1) accurate total energies based on the random-phase approximation (RPA) and on Møller-Plesset perturbation theory (MP2), (2) accurate electronic structure based on the GW approach of many-body perturbation theory, and (3) temperature-dependent effects based on many-bodytheory of electron-phonon coupling (EPC).

Initially, this library will be interfaced with ABINIT, exciting, FHI-aims and GPAW.

PI and Co-PI: Xavier Gonze and Claudia Draxl

Work Package 3: Exascale Coupled-cluster Methods

This WP develops advanced libraries that perform coupled cluster (CC) theory calculations for materials on exascale platforms as they become available. All CC calculations will be based on an enhanced CC4S library, named CC4S-X. CC4S-X will require input from the employed ab initio codes considering all code families. This input includes matrix elements of non-local operators such as those provided by WP1 and WP2. We note that the current efficiency and scalability of CC4S relies on its use of the CTF, which supports an efficient distribution of tensor algebraic operations on current massively parallel supercomputer platforms. This WP will involve profiling for bottleneck detection in CC4S-X and will result in proposals for the design and optimization of specific architectural features (co-design).

PI and Co-PI: Andreas Grüneis and Matthias Scheffler

Work Package 4: Exascale High-throughput Workflows

This WP will develop an exascale workflow environment for HTC. This includes the development of a library of HTC workflows, LibFlow-X, that can perform various types of ab initio tasks, using any of the aiCMS codes. The developments will be based on the widely-used workflow management tools FireWorks and ASE, which will be brought together and extended to meet the challenges posed by exascale computing. This WP will also form the basis for the beyond-DFT workflows developed in WP5 and the use cases in WP9 (from which feedback to this WP enables refinement). Although the focus of WP4 lies on ab initio calculations, the developed libraries will be general enough to be useful for second-principles frameworks as well, e.g., for classical molecular-dynamics simulations, as it is the case for ASE already.

PI and Co-PI: Kristian Sommer Thygesen and Geoffroy Hautier

Work Package 5: Beyond-DFT Workflows

The main goal of this work package is to develop and validate workflows for beyond-DFT calculations so that such calculations can take full advantage of exascale computers and distribute the many tasks required to obtain converged results onto many thousand cores. The methodologies covered will include the random-phase approximation (RPA), Møller–Plesset perturbation theory (MP2), the coupled cluster (CC) approach, and the GW approximation, i.e. the same methods that are the focus of WP2-3. Since beyond-DFT calculations often require much more memory than standard DFT calculations, special care will be given to automate and detect memory bottlenecks prior to submission of the jobs. Although similar problems can occur in DFT calculations, beyond- DFT calculations are much more demanding in terms of compute requirements, with the likelihood of failures during execution due to architectural bottlenecks (memory, communication-limited time-to-solution) being orders of magnitude larger than for DFT. This WP will rely on the LibFlow-X library (WP4) for setting up, post-processing the computations and managing the workflow aspects (including convergence loops and restarting failing computations).

PI and Co-PI: Geoffroy Hautier and Kristian Sommer Thygesen

Work Package 6: Big-Data Analytics

In this WP, we develop towards exascale artificial-intelligence tools with near-real-time performance and demonstrate them using a range of highlight applications drawn from Pillars 1 and 2. For these purposes, four complementary methods will be used: neural networks (NN), kernel methods, compressed sensing (SISSO), and subgroup discovery.

PI and Co-PI: Luca Ghiringhelli and James Kermode

Work Package 7: Data Infrastructure

Storage, processing, and provision of extreme-scale data The main objective of this work package is to develop the data infrastructure necessary to store high-volume and high-velocity calculation data (using applications as dealt with in Pillars 1 and 2), to automatically process this data, to provide it in a normalized form (to Pillar 3), and to offer tools enabling exploration and analysis by means of interactive software (the NOMAD AI-X Toolkit see also WP6). Since codes, workflows and high performance artificial intelligence are not, or cannot necessarily be, executed on the same site, the expected extreme-scale data and respective I/O requirements ask for a distributed infrastructure with multiple federated instances of the data infrastructure running on multiple HPC centers. This includes contribution of data to the existing central data store, the NOMAD Repository and Archive.

PI and Co-PI: Markus Scheidgen and James Kermode


Work Package 8: Co-Design

This WP will bring together the expertise of the aiCMS community and the European HPC Centers to guide the development of future exascale systems. Benchmarking results will serve as a reference for soft- and hardware developments, alongside Big-Data processing and HPAI, which represents an emerging use mode for HPC infrastructure.

PI and co-PI: Jose Maria Cela and Erwin Laure

Work Package 9: Use-case Demonstrators

This WP will demonstrate an integrated approach towards predictive, autonomous computational materials design that combines the core elements developed in Pillars 1-3 and WP8 to showcase the codes and infrastructure made available to the community by addressing two outstanding challenges of tremendous societal importance. Both use cases provide feedback to Pillars 1-3 and to WP8, to inform refinement and enhancement of their outputs.

Use case 1: The sustainable production of hydrogen is of paramount importance for a future infrastructure for the production of chemicals and fuels. To this end, photocatalytic water splitting (PCWS) could become a key technology but has so far been unfeasible due to limitations of the employed materials Based on the developments within Pillars 1-3, and in close collaboration with industrial partners, we will advance the field of PCWS by performing the first machine-learning-informed high-throughput screening of novel PCWS materials with beyond-DFT methods.

Use case 2: Thermoelectric (TE) “waste-heat recovery” devices can play a key role in establishing a sustainable energy economy, since they convert otherwise discarded heat, e.g., from industrial ovens, chemical plants, or combustion engines, into electric energy. By this means, they can contribute to reducing the overall energy consumption, even in scenarios involving traditional technologies.

PI and co-PI: Kristian Sommer Thygesen and Christan Carbogno