We present a detailed overview of the calculation of the enthalpy of dissociation of structure II pure propane or mixed methane + propane hydrates, focussing primarily on methods that are based on either the Clausius-Clapeyron equation or the direct calculation of the enthalpies of all the components that are involved in the hydrate dissociation reaction. Molecular dynamics simulations are used extensively in order to calculate the enthalpies and molar volumes of water, methane, propane, and the sII mixed hydrate (with variant degree of occupancy) at pressure and temperature conditions along the three-phase (Hydrate-Liquid water-Vapor or Hydrate-Liquid water-Liquid hydrocarbon) equilibrium line.

Nature has only provided us with a limited number of bio-based and biodegradable building blocks. Therefore, the fine tuning of the sustainable polymer properties is expected to be achieved through the control of the composition of bio-based copolymers for targeted applications such as cosmetics. Until now, the main approaches to alleviate the experimental efforts and accelerate the discovery of new polymers have relied on machine learning models trained on experimental data, which implies an enormous and difficult work in the compilation of data from heterogeneous sources. On the other hand, molecular dynamics simulations of polymers have shown that they can accurately capture the experimental trends for a series of properties. However, the combination of different ratios of monomers in copolymers can rapidly lead to a combinatorial explosion, preventing the investigation of all possibilities via molecular dynamics simulations. In this work, we show that the combination of machine learning approaches and high-throughput molecular dynamics simulations permits to quickly and efficiently sample and characterize the relevant chemical design space for specific applications. Reliable simulation protocols have been implemented to evaluate the glass transition temperature of a series of 58 homopolymers, which exhibit a good agreement with experiments, and 488 copolymers. Overall, 2,184 simulations (4 replicas per polymer) were performed, for a total simulation time of 143.052 µs. These results, constituting a dataset of 546 polymers, have been used to train a machine learning model for the prediction of the MD-calculated glass transition temperature with a mean absolute error of 19.34 K and a R2 score of 0.83. Overall, within its applicability domain, this machine learning model provides an impressive acceleration over molecular dynamics simulations: the glass transition temperature of thousands of polymers can be obtained within seconds, whereas it would have taken node-years to simulate them. This type of approach can be tuned to address different design spaces or different polymer properties and thus have the potential to accelerate the discovery of new polymers.

Molecular dynamics simulations have been employed to investigate the effect of molecular polydispersity on the aggregation of asphaltene. To make the large combinatorial space of possible asphaltene blends accessible to a systematic study via simulation, an upfront unsupervised machine-learning approach (clustering) was employed to identify a reduced set of model molecules representative of the diversity of asphaltene. For these molecules, single asphaltene model simulations have shown a broad range of aggregation behaviors, driven by their structural features: size of the aromatic core, length of the aliphatic chains, and presence of heteroatoms. Then, the combination of these model molecules in a series of mixtures have highlighted the complex and diverse effects of molecular polydispersity on the aggregation process of asphaltene. Simulations yielded both antagonistic and synergistic effects mediated by the trigger or facilitator action of specific asphaltene model molecules. These findings illustrate the necessity of accounting for molecular polydispersity when studying the asphaltene aggregation process and have permitted establishing a robust protocol for the in silico evaluation of the performance of asphaltene inhibitors, as illustrated for the case of a nonylphenol resin.

We have performed molecular dynamics simulations to study the adsorption of ten hydrate anti-agglomerants onto a mixed methane-propane sII hydrate surface covered by layers of liquid water of various thickness. As a general trend, we found that the more liquid water that is present on the hydrate surface, the less favorable the adsorption becomes even though there are considerable differences between the individual molecules, indicating that the presence and thickness of this liquid water layer are crucial parameters for anti-agglomerant adsorption studies. Additionally, we found that there exists an optimal thickness of the liquid water layer favoring hydrate growth due to the presence of both liquid water and hydrate-forming guest molecules. For all other cases of liquid water layer thickness, hydrate growth is slower due to the limited availability of hydrate-forming guests close to the hydrate formation front. Finally, we investigated the connection between the thickness of the liquid water layer and the degree of subcooling and found a very good agreement between our molecular dynamics simulations and theoretical predictions.

We push the boundaries of electronic structure-based ab-initio molecular dynamics (AIMD) beyond 100 million atoms. This scale is otherwise barely reachable with classical force-field methods or novel neural network and machine learning potentials. We achieve this breakthrough by combining innovations in linear-scaling AIMD, efficient and approximate sparse linear algebra, low and mixed-precision floating-point computation on GPUs, and a compensation scheme for the errors introduced by numerical approximations. The core of our work is the non-orthogonalized local submatrix method (NOLSM), which scales very favorably to massively parallel computing systems and translates large sparse matrix operations into highly parallel, dense matrix operations that are ideally suited to hardware accelerators. We demonstrate that the NOLSM method, which is at the center point of each AIMD step, is able to achieve a sustained performance of 324 PFLOP/s in mixed FP16/FP32 precision corresponding to an efficiency of 67.7% when running on 1536 NVIDIA A100 GPUs.

Tungsten is an important and versatile transition metal and has a firm place at the heart of many technologies. A popular experimental technique for the characterisation of tungsten and tungsten-based compounds is X-ray photoelectron spectroscopy (XPS), which enables the assessment of chemical states and electronic structure through the collection of core level and valence band spectra. However, in the case of metallic tungsten, open questions remain regarding the origin, nature, and position of satellite features that are prominent in the photoelectron spectrum. These satellites are a fingerprint of the electronic structure of the material and have not been thoroughly investigated, at times leading to their misinterpretation. The present work combines high-resolution soft and hard X-ray photoelectron spectroscopy (SXPS and HAXPES) with reflection electron energy loss spectroscopy (REELS) and a multi-tiered ab-initio theoretical approach, including density functional theory (DFT) and many-body perturbation theory (G0W0 and GW+C), to disentangle the complex set of experimentally observed satellite features attributed to the generation of plasmons and interband transitions. This combined experiment-theory strategy is able to uncover previously undocumented satellite features, improving our understanding of their direct relationship to tungsten's electronic structure. Furthermore, it lays the groundwork for future studies into tungsten based mixed-metal systems and holds promise for the re-assessment of the photoelectron spectra of other transition and post-transition metals, where similar questions regarding satellite features remain.

The binary alloy of titanium-tungsten (TiW) is an established diffusion barrier in high-power semiconductor devices, owing to its ability to suppress the diffusion of copper from the metallization scheme into the surrounding silicon substructure. However, little is known about the response of TiW to high-temperature events or its behavior when exposed to air. Here, a combined soft and hard x-ray photoelectron spectroscopy (XPS) characterization approach is used to study the influence of post-deposition annealing and titanium concentration on the oxidation behavior of a 300nm-thick TiW film. The combination of both XPS techniques allows for the assessment of the chemical state and elemental composition across the surface and bulk of the TiW layer. The findings show that in response to high-temperature annealing, titanium segregates out of the mixed metal system and upwardly migrates, accumulating at the TiW/air interface. Titanium shows remarkably rapid diffusion under relatively short annealing timescales, and the extent of titanium surface enrichment is increased through longer annealing periods or by increasing the bulk titanium concentration. Surface titanium enrichment enhances the extent of oxidation both at the surface and in the bulk of the alloy due to the strong gettering ability of titanium. Quantification of the soft x-ray photoelectron spectra highlights the formation of three tungsten oxidation environments, attributed to WO2, WO3, and a WO3 oxide coordinated with a titanium environment. This combinatorial characterization approach provides valuable insights into the thermal and oxidation stability of TiW alloys from two depth perspectives, aiding the development of future device technologies.

The performance of lubricant additives, such as organic friction modifiers (OFMs), depends critically on their ability to adsorb onto the surfaces of moving components and form protective self-assembled layers (SAMs). Therefore, understanding the relationship between the concentration of the additive in the base oil and the resulting surface coverage is extremely important for lubricant formulations, as well as many other surfactant applications. Here, we use molecular dynamics (MD) simulations to study the adsorption isotherms of three different OFMs, stearic acid (SA), glycerol monoostearate (GMS), and glycerol monooleate (GMO), onto a hematite surface from hydrocarbon solvents, n-hexadecane and poly-α-olefin (PAO). First, we calculate the potential of mean force (PMF) of the adsorption process using MD simulations with the adaptive biasing force (ABF) algorithm. Our MD simulations show that SA has the weakest adsorption energy on hematite, followed by GMS, and finally GMO, due to the increasing number of functional groups available to bind to the surface. We also estimate the area occupied by each OFM molecule on the surface in the high-coverage limit using MD simulations of the annealing of OFM films with different initial surface coverages. We obtain a similar hard-disk area for GMS and GMO, but a lower value for SA, which is due to its smaller headgroup size. Based on the adsorption energy and surface area, we determine the corresponding adsorption isotherms using the molecular thermodynamic theory (MTT), which agree well with one available experimental data-set for SA. Two other experimental data-sets for SA require lateral interactions between surfactant molecules to be accounted for. SA forms monolayers with lower surface coverage than GMO and GMS at low concentrations (due to a smaller adsorption energy), but also has the highest plateau coverage (due to a smaller hard-disk area). We validate the adsorption energies from the MD simulations using high frequency reciprocating rig (HFRR) friction experiments with different concentrations of the OFMs in PAO. We use the Jahanmir and Beltzer model to estimate the surface coverage at each concentration and the adsorption energy of each OFM from the HFRR friction data. For OFMs with saturated tailgroups (SA and GMS), we obtain good agreement between the predictions made by the simulations and the experiments. The MD simulation and experimental results deviate for OFMs containing Z-unsaturated tailgroups (GMO), with the former suggesting stronger adsorption for GMO than GMS, while the latter predicts the opposite trend. We suggest that this is can be attributed to the higher steric barrier of adsorption of the OFMs with kinked Z-unsaturated tailgroup through a partially formed monolayer, an aspect which was not captured in the current simulations. This study demonstrates that MD simulations with the ABF algorithm, alongside MTT, are an accurate and efficient tool to predict adsorption isotherms at solid-liquid interfaces.

Using both computational and experimental methods, the capacity of four different surfactant molecules to inhibit the agglomeration of sII hydrate particles was assessed. The computational simulations were carried out using both steered and non-steered molecular dynamics (MD), simulating the coalescence process of a hydrate slab and a water droplet, both covered with surfactant molecules. The surfactants were ranked according to free energy calculations (steered MD) and the number of agglomeration events (non-steered MD). The experimental work was based on rocking cell measurements, determining the minimum effective dose necessary to inhibit agglomeration. Overall, good agreement was obtained between the performance predicted by the simulations and the experimental measurements. Moreover, the simulations allowed us to gain additional insights that are not directly accessible via experiments, such as an analysis of the mass density profiles, the diffusion coefficients, or the orientations of the long tails.

The dissociation process of spherical sII mixed methane-propane hydrate particles in liquid hydrocarbon was investigated via microsecond-long molecular dynamics simulations. A strong dependence of the melting temperature on the particle size was found. Analysis in the context of the Gibbs-Thomson effect provided insights into the fundamental properties of gas hydrates.

First-principles electronic structure calculations are now accessible to a very large community of users across many disciplines, thanks to many successful software packages, some of which are described in this special issue. The traditional coding paradigm for such packages is monolithic, i.e., regardless of how modular its internal structure may be, the code is built independently from others, essentially from the compiler up, possibly with the exception of linear-algebra and message-passing libraries. This model has endured and been quite successful for decades. The successful evolution of the electronic structure methodology itself, however, has resulted in an increasing complexity and an ever longer list of features expected within all software packages, which implies a growing amount of replication between different packages, not only in the initial coding but, more importantly, every time a code needs to be re-engineered to adapt to the evolution of computer hardware architecture. The Electronic Structure Library (ESL) was initiated by CECAM (the European Centre for Atomic and Molecular Calculations) to catalyze a paradigm shift away from the monolithic model and promote modularization, with the ambition to extract common tasks from electronic structure codes and redesign them as open-source libraries available to everybody. Such libraries include "heavy-duty" ones that have the potential for a high degree of parallelization and adaptation to novel hardware within them, thereby separating the sophisticated computer science aspects of performance optimization and re-engineering from the computational science done by, e.g., physicists and chemists when implementing new ideas. We envisage that this modular paradigm will improve overall coding efficiency and enable specialists (whether they be computer scientists or computational scientists) to use their skills more effectively and will lead to a more dynamic evolution of software in the community as well as lower barriers to entry for new developers. The model comes with new challenges, though. The building and compilation of a code based on many interdependent libraries (and their versions) is a much more complex task than that of a code delivered in a single self-contained package. Here, we describe the state of the ESL, the different libraries it now contains, the short- and mid-term plans for further libraries, and the way the new challenges are faced. The ESL is a community initiative into which several pre-existing codes and their developers have contributed with their software and efforts, from which several codes are already benefiting, and which remains open to the community.

With the development of low order scaling methods for performing Kohn-Sham density functional theory, it is now possible to perform fully quantum mechanical calculations of systems containing tens of thousands of atoms. However, with an increase in the size of the system treated comes an increase in complexity, making it challenging to analyze such large systems and determine the cause of emergent properties. To address this issue, in this paper, we present a systematic complexity reduction methodology which can break down large systems into their constituent fragments and quantify interfragment interactions. The methodology proposed here requires no a priori information or user interaction, allowing a single workflow to be automatically applied to any system of interest. We apply this approach to a variety of different systems and show how it allows for the derivation of new system descriptors, the design of QM/MM partitioning schemes, and the novel application of graph metrics to molecules and materials.

A review of the present status, recent enhancements, and applicability of the Siesta program is presented. Since its debut in the mid-1990s, Siesta's flexibility, efficiency, and free distribution have given advanced materials simulation capabilities to many groups worldwide. The core methodological scheme of Siesta combines finite-support pseudo-atomic orbitals as basis sets, norm-conserving pseudopotentials, and a real-space grid for the representation of charge density and potentials and the computation of their associated matrix elements. Here, we describe the more recent implementations on top of that core scheme, which include full spin-orbit interaction, non-repeated and multiple-contact ballistic electron transport, density functional theory (DFT)+U and hybrid functionals, time-dependent DFT, novel reduced-scaling solvers, density-functional perturbation theory, efficient van der Waals non-local density functionals, and enhanced molecular-dynamics options. In addition, a substantial effort has been made in enhancing interoperability and interfacing with other codes and utilities, such as wannier90 and the second-principles modeling it can be used for, an AiiDA plugin for workflow automatization, interface to Lua for steering Siesta runs, and various post-processing utilities. Siesta has also been engaged in the Electronic Structure Library effort from its inception, which has allowed the sharing of various low-level libraries, as well as data standards and support for them, particularly the PSeudopotential Markup Language definition and library for transferable pseudopotentials, and the interface to the ELectronic Structure Infrastructure library of solvers. Code sharing is made easier by the new open-source licensing model of the program. This review also presents examples of application of the capabilities of the code, as well as a view of on-going and future developments.

The BigDFT project was started in 2005 with the aim of testing the advantages of using a Daubechies wavelet basis set for Kohn-Sham (KS) density functional theory (DFT) with pseudopotentials. This project led to the creation of the BigDFT code, which employs a computational approach with optimal features of flexibility, performance, and precision of the results. In particular, the employed formalism has enabled the implementation of an algorithm able to tackle DFT calculations of large systems, up to many thousands of atoms, with a computational effort that scales linearly with the number of atoms. In this work, we recall some of the features that have been made possible by the peculiar properties of Daubechies wavelets. In particular, we focus our attention on the usage of DFT for large-scale systems. We show how the localized description of the KS problem, emerging from the features of the basis set, is helpful in providing a simplified description of large-scale electronic structure calculations. We provide some examples on how such a simplified description can be employed, and we consider, among the case-studies, the SARS-CoV-2 main protease.

Performance analysis tools are essential in the process of understanding application behavior, identifying critical performance issues and adapting applications to new architectures and increasingly scaling HPC systems. State-of-the-art tools provide extensive functionality and a plenitude of specialized analysis capabilities. At the same time, the complexity of the potential performance issues and sometimes the tools themselves remains a challenging task, especially for non-experts. In particular, identifying the main issues in the overwhelming amount of data and tool opportunities as well as quantifying their impact and potential for improvement can be tedious and time consuming. In this paper we present a structured approach to performance analysis used within the EU Centre of Excellence for Performance Optimization and Productivity (POP). The structured approach features a method to get a general overview, determine the focus of the analysis, and identify the main issues and areas for potential improvement with a statistical performance model that leads to starting points for a subsequent in-depth analysis. All steps of the structured approach are accompanied with according tools from the BSC tool suite and underlined with an exemplary performance analysis.

Density functional theory (DFT) has become a standard tool for ab-initio simulations for a wide range of applications. While the intrinsic cubic scaling of DFT was for a long time limiting the accessible system size to some hundred atoms, the recent progress with respect to linear scaling DFT methods has allowed to tackle problems that are larger by many orders of magnitudes. However, as these linear scaling methods were developed for insulators, they cannot, in general, be straightforwardly applied to metals, as a finite (electronic) temperature is needed to ensure locality of the density matrix. In this paper we show that, once finite electronic temperature is employed, the linear scaling version of the BIGDFT code is able to exploit this locality to provide a computational treatment that scales linearly with respect to the number of atoms of a metallic system. We provide prototype examples based on bulk Tungsten, which plays a key role in finding safe and long-lasting materials for Fusion Reactors; however we do not expect any major obstacles in extending this work to cover other metals. We believe that such an approach might help in opening the path towards novel approaches for investigating the electronic structure of such materials, in particular when large supercells are required.

We present the submatrix method, a highly parallelizable method for the approximate calculation of inverse p-th roots of large sparse symmetric matrices which are required in different scientific applications. Following the idea of Approximate Computing, we allow imprecision in the final result in order to utilize the sparsity of the input matrix and to allow massively parallel execution. For an n x n matrix, the proposed algorithm allows to distribute the calculations over n nodes with only little communication overhead. The result matrix exhibits the same sparsity pattern as the input matrix, allowing for efficient reuse of allocated data structures. We evaluate the algorithm with respect to the error that it introduces into calculated results, as well as its performance and scalability. We demonstrate that the error is relatively limited for well-conditioned matrices and that results are still valuable for error-resilient applications like preconditioning even for ill-conditioned matrices. We discuss the execution time and scaling of the algorithm on a theoretical level and present a distributed implementation of the algorithm using MPI and OpenMP. We demonstrate the scalability of this implementation by running it on a high-performance compute cluster comprised of 1024 CPU cores, showing a speedup of 665x compared to single-threaded execution.