CSCS - Swiss National Supercomputing Centre

We introduce a powerful method for exploring the properties of the multidimensional free energy surfaces (FESs) of complex many-body systems by means of coarse-grained non-Markovian dynamics in the space defined by a few collective coordinates. A characteristic feature of these dynamics is the presence of a history-dependent potential term that, in time, fills the minima in the FES, allowing the efficient exploration and accurate determination of the FES as a function of the collective coordinates. We demonstrate the usefulness of this approach in the case of the dissociation of a NaCl molecule in water and in the study of the conformational changes of a dialanine in solution.

CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular, and biological systems. It is especially aimed at massively parallel and linear-scaling electronic structure methods and state-of-the-art ab initio molecular dynamics simulations. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern high-performance computing systems. This review revisits the main capabilities of CP2K to perform efficient and accurate electronic structure simulations. The emphasis is put on density functional theory and multiple post-Hartree-Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension.

Once the crystal structure of a chemical substance is known, many properties can be predicted reliably and routinely. Therefore if researchers could predict the crystal structure of a material before it is synthesized, they could significantly accelerate the discovery of new materials. In addition, the ability to predict crystal structures at arbitrary conditions of pressure and temperature is invaluable for the study of matter at extreme conditions, where experiments are difficult. Crystal structure prediction (CSP), the problem of finding the most stable arrangement of atoms given only the chemical composition, has long remained a major unsolved scientific problem. Two problems are entangled here: search, the efficient exploration of the multidimensional energy landscape, and ranking, the correct calculation of relative energies. For organic crystals, which contain a few molecules in the unit cell, search can be quite simple as long as a researcher does not need to include many possible isomers or conformations of the molecules; therefore ranking becomes the main challenge. For inorganic crystals, quantum mechanical methods often provide correct relative energies, making search the most critical problem. Recent developments provide useful practical methods for solving the search problem to a considerable extent. One can use simulated annealing, metadynamics, random sampling, basin hopping, minima hopping, and data mining. Genetic algorithms have been applied to crystals since 1995, but with limited success, which necessitated the development of a very different evolutionary algorithm. This Account reviews CSP using one of the major techniques, the hybrid evolutionary algorithm USPEX (Universal Structure Predictor: Evolutionary Xtallography). Using recent developments in the theory of energy landscapes, we unravel the reasons evolutionary techniques work for CSP and point out their limitations. We demonstrate that the energy landscapes of chemical systems have an overall shape and explore their intrinsic dimensionalities. Because of the inverse relationships between order and energy and between the dimensionality and diversity of an ensemble of crystal structures, the chances that a random search will find the ground state decrease exponentially with increasing system size. A well-designed evolutionary algorithm allows for much greater computational efficiency. We illustrate the power of evolutionary CSP through applications that examine matter at high pressure, where new, unexpected phenomena take place. Evolutionary CSP has allowed researchers to make unexpected discoveries such as a transparent phase of sodium, a partially ionic form of boron, complex superconducting forms of calcium, a novel superhard allotrope of carbon, polymeric modifications of nitrogen, and a new class of compounds, perhydrides. These methods have also led to the discovery of novel hydride superconductors including the "impossible" LiH(n) (n=2, 6, 8) compounds, and CaLi(2). We discuss extensions of the method to molecular crystals, systems of variable composition, and the targeted optimization of specific physical properties.

MOLEKEL is an interactive visualization ('postprocessing') program for molecular and electronic structure data, generating high-quality graphics for use in research and education. MOLEKEL has been ported to OpenGL and is now available on a number of platforms, including PC (http://www.cscs.ch/molekel/). We describe details of its implementation, capabilities and the new added features.

The possibility of observing chemical reactions in ab initio molecular dynamics runs is severely hindered by the short simulation time accessible. We propose a new method for accelerating the reaction process, based on the ideas of the extended Lagrangian and coarse-grained non-Markovian metadynamics. We demonstrate that by this method it is possible to simulate reactions involving complex atomic rearrangements and very large energy barriers in runs of a few picoseconds.

As the push towards exascale hardware has increased the diversity of system architectures, performance portability has become a critical aspect for scientific software. We describe the Kokkos Performance Portable Programming Model that allows developers to write single source applications for diverse high-performance computing architectures. Kokkos provides key abstractions for both the compute and memory hierarchy of modern hardware. We describe the novel abstractions that have been added to Kokkos version 3 such as hierarchical parallelism, containers, task graphs, and arbitrary-sized atomic operations to prepare for exascale era architectures. We demonstrate the performance of these new features with reproducible benchmarks on CPUs and GPUs.

Abstract Currently major efforts are underway toward refining the horizontal resolution (or grid spacing) of climate models to about 1 km, using both global and regional climate models (GCMs and RCMs). Several groups have succeeded in conducting kilometer-scale multiweek GCM simulations and decadelong continental-scale RCM simulations. There is the well-founded hope that this increase in resolution represents a quantum jump in climate modeling, as it enables replacing the parameterization of moist convection by an explicit treatment. It is expected that this will improve the simulation of the water cycle and extreme events and reduce uncertainties in climate change projections. While kilometer-scale resolution is commonly employed in limited-area numerical weather prediction, enabling it on global scales for extended climate simulations requires a concerted effort. In this paper, we exploit an RCM that runs entirely on graphics processing units (GPUs) and show examples that highlight the prospects of this approach. A particular challenge addressed in this paper relates to the growth in output volumes. It is argued that the data avalanche of high-resolution simulations will make it impractical or impossible to store the data. Rather, repeating the simulation and conducting online analysis will become more efficient. A prototype of this methodology is presented. It makes use of a bit-reproducible model version that ensures reproducible simulations across hardware architectures, in conjunction with a data virtualization layer as a common interface for output analyses. An assessment of the potential of these novel approaches will be provided.

The thermally induced beam distortions in end-pumped Nd:YAG, Nd:GSGG, and Nd:YLF rods were analyzed and the influence of edge- and face-cooling was investigated. The distributions of temperature, stress, and strain in the crystals were calculated by finite element analysis. Based on these data, the space-resolved changes of the refractive index were determined considering thermal dispersion, surface deformation, and strain-induced birefringence. The resulting optical path difference for one round-trip in the end-pumped rods was integrated numerically. For each rod, the induced thermal lens was determined over the extent of the pump spot radius. The calculations of the optical path difference were experimentally confirmed by investigations using a modified Twyman-Green interferometer with a polarized HeNe probe beam at 633 nm under lasing and nonlasing conditions.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Ab initio methods allow a more or less straightforward prediction of numerous physical properties of solids, but require the knowledge of their crystal structure. The evolutionary algorithm USPEX, developed by us in 2004–2006, enables reliable prediction of the stable crystal structure without relying on any experimental data. Numerous tests (mostly for systems with up to 28 atoms in the unit cell, and a few tests with up to 128 atoms/cell) showed a success rate of nearly 100%. USPEX has resulted in a number of predictions of hitherto unknown stable structures. We give a short overview of the method, introducing some new developments and results, and discuss a few alternative approaches. The method is illustrated by a test on an 80 atom supercell of MgSiO3 and by the search for new materials with compositions Al13K and Al12C. (Some figures in this article are in colour only in the electronic version) 1.

We explore whether the topology of energy landscapes in chemical systems obeys any rules and what these rules are. To answer this and related questions we use several tools: (i) Reduced energy surface and its density of states, (ii) descriptor of structure called fingerprint function, which can be represented as a one-dimensional function or a vector in abstract multidimensional space, (iii) definition of a "distance" between two structures enabling quantification of energy landscapes, (iv) definition of a degree of order of a structure, and (v) definitions of the quasi-entropy quantifying structural diversity. Our approach can be used for rationalizing large databases of crystal structures and for tuning computational algorithms for structure prediction. It enables quantitative and intuitive representations of energy landscapes and reappraisal of some of the traditional chemical notions and rules. Our analysis confirms the expectations that low-energy minima are clustered in compact regions of configuration space ("funnels") and that chemical systems tend to have very few funnels, sometimes only one. This analysis can be applied to the physical properties of solids, opening new ways of discovering structure-property relations. We quantitatively demonstrate that crystals tend to adopt one of the few simplest structures consistent with their chemistry, providing a thermodynamic justification of Pauling's fifth rule.

We introduce a new and highly efficient tagger for hadronically decaying top quarks, based on a deep neural network working with Lorentz vectors and the Minkowski metric. With its novel machine learning setup and architecture it allows us to identify boosted top quarks not only from calorimeter towers, but also including tracking information. We show how the performance of our tagger compares with QCD-inspired and image-recognition approaches and find that it significantly increases the performance for strongly boosted top quarks.

Abstract. The best hope for reducing long-standing global climate model biases is by increasing resolution to the kilometer scale. Here we present results from an ultrahigh-resolution non-hydrostatic climate model for a near-global setup running on the full Piz Daint supercomputer on 4888 GPUs (graphics processing units). The dynamical core of the model has been completely rewritten using a domain-specific language (DSL) for performance portability across different hardware architectures. Physical parameterizations and diagnostics have been ported using compiler directives. To our knowledge this represents the first complete atmospheric model being run entirely on accelerators on this scale. At a grid spacing of 930 m (1.9 km), we achieve a simulation throughput of 0.043 (0.23) simulated years per day and an energy consumption of 596 MWh per simulated year. Furthermore, we propose a new memory usage efficiency (MUE) metric that considers how efficiently the memory bandwidth – the dominant bottleneck of climate codes – is being used.

The electronic properties of a $Z$-DNA crystal synthesized in the laboratory are investigated by means of density-functional theory Car-Parrinello calculations. The electronic structure has a gap of only 1.28 eV. This separates a manifold of 12 occupied states which came from the $\ensuremath{\pi}$ guanine orbitals from the lowest empty states in which the electron is transferred to the $\mathrm{N}{\mathrm{a}}^{\mathrm{+}}$ from $\mathrm{P}{\mathrm{O}}_{\mathrm{4}}^{\mathrm{\ensuremath{-}}}$ groups and water molecules. We have evaluated the anisotropic optical conductivity. At low frequency the conductivity is dominated by the $\ensuremath{\pi}\ensuremath{\rightarrow}\mathrm{N}{\mathrm{a}}^{\mathrm{+}}$ transitions. Our calculation demonstrates that the cost of introducing electron holes in wet DNA strands could be lower than previously anticipated.

Highly accurate estimates of the high-spin/low-spin energy difference ΔEHLel in the high-spin complexes [Fe(NCH)6]2+ and [Co(NCH)6]2+ have been obtained from the results of CCSD(T) calculations extrapolated to the complete basis set limit. These estimates are shown to be strongly influenced by scalar relativistic effects. They have been used to assess the performances of the CASPT2 method and 30 density functionals of the GGA, meta-GGA, global hybrid, RSH, and double-hybrid types. For the CASPT2 method, the results of the assessment support the proposal [Kepenekian, M.; Robert, V.; Le Guennic, B. J. Chem. Phys. 2009, 131, 114702] that the ionization potential–electron affinity (IPEA) shift defining the zeroth-order Hamiltonian be raised from its standard value of 0.25 au to 0.50–0.70 au for the determination of ΔEHLel in Fe(II) complexes with a [FeN6] core. At the DFT level, some of the assessed functionals proved to perform within chemical accuracy (±350 cm–1) for the spin-state energetics of [Fe(NCH)6]2+, others for that of [Co(NCH)6]2+, but none of them simultaneously for both complexes. As demonstrated through a reparametrization of the CAM-PBE0 range-separated hybrid, which led to a functional that performs within chemical accuracy for the spin-state energetics of both complexes, performing density functionals of broad applicability may be devised by including in their training sets highly accurate data like those reported here for [Fe(NCH)6]2+ and [Co(NCH)6]2+.

Classical empirical force fields have dominated biomolecular simulations for over 50 years. Although widely used in drug discovery, crystal structure prediction, and biomolecular dynamics, they generally lack the accuracy and transferability required for first-principles predictive modeling. In this paper, we introduce MACE-OFF, a series of short-range transferable force fields for organic molecules created using state-of-the-art machine learning technology and first-principles reference data computed with a high level of quantum mechanical theory. MACE-OFF demonstrates the remarkable capabilities of short-range models by accurately predicting a wide variety of gas- and condensed-phase properties of molecular systems. It produces accurate, easy-to-converge dihedral torsion scans of unseen molecules as well as reliable descriptions of molecular crystals and liquids, including quantum nuclear effects. We further demonstrate the capabilities of MACE-OFF by determining free energy surfaces in explicit solvent as well as the folding dynamics of peptides and nanosecond simulations of a fully solvated protein. These developments enable first-principles simulations of molecular systems for the broader chemistry community at high accuracy and relatively low computational cost.

Exact-diagonalization studies for a doped t-J ladder (or double chain) show hole pairing in the ground state. The excitation spectrum separates into a limited number of quasiparticles which carry charge +\ensuremath{\Vert}e\ensuremath{\Vert} and spin 1/2 and a triplet mode. At half-filling the former vanish but the latter evolves continuously into the triplet band of the spin liquid. At low doping the quasiparticles form a dilute Fermi gas with a strong attraction but simultaneously the Fermi wave vector, as would be measured in photoemission, is large.

ABSTRACT Cosmological simulations are an important theoretical pillar for understanding non-linear structure formation in our Universe and for relating it to observations on large scales. In several papers, we introduce our MillenniumTNG (MTNG) project that provides a comprehensive set of high-resolution, large-volume simulations of cosmic structure formation aiming to better understand physical processes on large scales and to help interpret upcoming large-scale galaxy surveys. We here focus on the full physics box MTNG740 that computes a volume of $740\, \mathrm{Mpc}^3$ with a baryonic mass resolution of $3.1\times ~10^7\, \mathrm{M_\odot }$ using arepo with 80.6 billion cells and the IllustrisTNG galaxy formation model. We verify that the galaxy properties produced by MTNG740 are consistent with the TNG simulations, including more recent observations. We focus on galaxy clusters and analyse cluster scaling relations and radial profiles. We show that both are broadly consistent with various observational constraints. We demonstrate that the SZ-signal on a deep light-cone is consistent with Planck limits. Finally, we compare MTNG740 clusters with galaxy clusters found in Planck and the SDSS-8 RedMaPPer richness catalogue in observational space, finding very good agreement as well. However, simultaneously matching cluster masses, richness, and Compton-y requires us to assume that the SZ mass estimates for Planck clusters are underestimated by 0.2 dex on average. Due to its unprecedented volume for a high-resolution hydrodynamical calculation, the MTNG740 simulation offers rich possibilities to study baryons in galaxies, galaxy clusters, and in large-scale structure, and in particular their impact on upcoming large cosmological surveys.

Turbulence is a key ingredient for the evolution of the intracluster medium, whose properties can be predicted with high-resolution numerical simulations. We present initial results on the generation of solenoidal and compressive turbulence in the intracluster medium during the formation of a small-size cluster using highly resolved, non-radiative cosmological simulations, with a refined monitoring in time. In this first of a series of papers, we closely look at one simulated cluster whose formation was distinguished by a merger around z ∼ 0.3. We separate laminar gas motions, turbulence and shocks with dedicated filtering strategies and distinguish the solenoidal and compressive components of the gas flows using Hodge–Helmholtz decomposition. Solenoidal turbulence dominates the dissipation of turbulent motions (∼95 per cent) in the central cluster volume at all epochs. The dissipation via compressive modes is found to be more important (∼30 per cent of the total) only at large radii (≥0.5rvir) and close to merger events. We show that enstrophy (vorticity squared) is good proxy of solenoidal turbulence. All terms ruling the evolution of enstrophy (i.e. baroclinic, compressive, stretching and advective terms) are found to be significant, but in amounts that vary with time and location. Two important trends for the growth of enstrophy in our simulation are identified: first, enstrophy is continuously accreted into the cluster from the outside, and most of that accreted enstrophy is generated near the outer accretion shocks by baroclinic and compressive processes. Secondly, in the cluster interior vortex, stretching is dominant, although the other terms also contribute substantially.

The new challenges presented by exascale system architectures have resulted in difficulty achieving the desired scalability using traditional distributed-memory runtimes. Asynchronous many-task systems (AMT) are based on a new paradigm showing promise in addressing these challenges, providing application developers with a productive and performant approach to programming on next generation systems.

Evolutionary algorithms are an important emergent computing methodology. They have aroused intense interest in the past few years because of their versatility in solving difficult problems in the optimization and machine learning fields. Many applications to several different areas have been reported and the field is still in expansion. We will first briefly review the history and the methodological basis of evolutionary algorithms, followed by a simple example of their functioning. Parallel evolutionary algorithms will then be introduced, showing their good match to today&amp;apos;s parallel and distributed computers. We will then look at a couple of applications and, finally, references and comments to bibliographic and other information on evolutionary methods will be given to allow readers to broaden their knowledge in the field. 1 Introduction Evolutionary Algorithms (EAs) are a hot topic these days. Although they are probably a fashionable theme, there is also much solid work being done ...