Extending the coherence of spin defects in hBN enables advanced qubit control and quantum sensing.

Negatively-charged boron vacancy centers ([Formula: see text]) in hexagonal Boron Nitride (hBN) are attracting increasing interest since they represent optically-addressable qubits in a van der Waals material. In particular, these spin defects have shown promise as sensors for temperature, pressure, and static magnetic fields. However, their short spin coherence time limits their scope for quantum technology. Here, we apply dynamical decoupling techniques to suppress magnetic noise and extend the spin coherence time by two orders of magnitude, approaching the fundamental T1 relaxation limit. Based on this improvement, we demonstrate advanced spin control and a set of quantum sensing protocols to detect radiofrequency signals with sub-Hz resolution. The corresponding sensitivity is benchmarked against that of state-of-the-art NV-diamond quantum sensors. This work lays the foundation for nanoscale sensing using spin defects in an exfoliable material and opens a promising path to quantum sensors and quantum networks integrated into ultra-thin structures.

Molecular Dynamics Simulations of Asphaltene Aggregation: Machine-Learning Identification of Representative Molecules, Molecular Polydispersity, and Inhibitor Performance.

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.

Assessing the effect of a liquid water layer on the adsorption of hydrate anti-agglomerants using molecular simulations.

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.

Unveiling the Zero-Phonon Line of the Boron Vacancy Center by Cavity-Enhanced Emission.

Negatively charged boron vacancies (VB-) in hexagonal boron nitride (hBN) exhibit a broad emission spectrum due to strong electron-phonon coupling and Jahn-Teller mixing of electronic states. As such, the direct measurement of the zero-phonon line (ZPL) of VB- has remained elusive. Here, we measure the room-temperature ZPL wavelength to be 773 ± 2 nm by coupling the hBN layer to the high-Q nanobeam cavity. As the wavelength of cavity mode is tuned, we observe a pronounced intensity resonance, indicating the coupling to VB-. Our observations are consistent with the spatial redistribution of VB- emission. Spatially resolved measurements show a clear Purcell effect maximum at the midpoint of the nanobeam, in accord with the optical field distribution of the cavity mode. Our results are in good agreement with theoretical calculations, opening the way to using VB- as cavity spin-photon interfaces.

Molecular Simulations of Surfactant Adsorption on Iron Oxide from Hydrocarbon Solvents.

The performance of organic friction modifiers (OFMs) depends on their ability to adsorb onto surfaces and form protective monolayers. Understanding the relationship between OFM concentration in the base oil and the resulting surface coverage is important for improving lubricant formulations. Here, we use molecular dynamics (MD) simulations to study the adsorption of three OFMs─stearic acid (SA), glycerol monoostearate (GMS), and glycerol monooleate (GMO)─onto a hematite surface from two hydrocarbon solvents─n-hexadecane and poly(α-olefin) (PAO). We calculate the potential of mean force of the adsorption process using the adaptive biasing force algorithm, and the adsorption strength increases in the order SA < GMS < GMO. We estimate the minimum area occupied by OFM molecules on the surface using annealing MD simulations and obtained a similar hard-disk area for GMS and GMO but a lower value for SA. Using the MD results, we determine the adsorption isotherms using the molecular thermodynamic theory (MTT), which agree well with one previous experimental data set for SA on hematite. For two other experimental data sets for SA, lateral interactions between surfactant molecules need to be accounted for within the MTT framework. SA forms monolayers with lower surface coverage than GMO and GMS at low concentrations but also has the highest plateau coverage. We validate the adsorption energies from the MD simulations using high-frequency reciprocating rig friction experiments with different concentrations of the OFMs in PAO. For OFMs with saturated tailgroups (SA and GMS), we obtain good agreement between the simulations and the experiments. The results deviate for OFMs containing Z-unsaturated tailgroups (GMO) due to the additional steric hindrance, which is not accounted for in the current simulation framework. This study demonstrates that MD simulations, alongside MTT, are an accurate and efficient tool to predict adsorption isotherms at solid-liquid interfaces.

Size dependence of the dissociation process of spherical hydrate particles via microsecond molecular dynamics simulations.

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.

Ranking the Efficiency of Gas Hydrate Anti-agglomerants through Molecular Dynamic Simulations.

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 CECAM electronic structure library and the modular software development paradigm.

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.

Siesta: Recent developments and applications.

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 Emergent Yo-yo Movement of Nuclei Driven by Cytoskeletal Remodeling in Pseudo-synchronous Mitotic Cycles.

Many aspects in tissue morphogenesis are attributed to a collective behavior of the participating cells. Yet, the mechanism for emergence of dynamic tissue behavior is not well understood. Here, we report that the "yo-yo"-like nuclear movement in the Drosophila syncytial embryo displays emergent features indicative of collective behavior. Following mitosis, the array of nuclei moves away from the wave front by several nuclear diameters only to return to its starting position about 5 min later. Based on experimental manipulations and numerical simulations, we find that the ensemble of elongating and isotropically oriented spindles, rather than individual spindles, is the main driving force for anisotropic nuclear movement. ELMO-dependent F-actin restricts the time for the forward movement and ELMO- and dia-dependent F-actin is essential for the return movement. Our study provides insights into how the interactions among the cytoskeleton as individual elements lead to collective movement of the nuclear array on a macroscopic scale.

Complexity Reduction in Density Functional Theory Calculations of Large Systems: System Partitioning and Fragment Embedding.

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.

Flexibilities of wavelets as a computational basis set for large-scale electronic structure calculations.

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.

Efficient Computation of Sparse Matrix Functions for Large-Scale Electronic Structure Calculations: The CheSS Library.

We present CheSS, the "Chebyshev Sparse Solvers" library, which has been designed to solve typical problems arising in large-scale electronic structure calculations using localized basis sets. The library is based on a flexible and efficient expansion in terms of Chebyshev polynomials and presently features the calculation of the density matrix, the calculation of matrix powers for arbitrary powers, and the extraction of eigenvalues in a selected interval. CheSS is able to exploit the sparsity of the matrices and scales linearly with respect to the number of nonzero entries, making it well-suited for large-scale calculations. The approach is particularly adapted for setups leading to small spectral widths of the involved matrices and outperforms alternative methods in this regime. By coupling CheSS to the DFT code BigDFT, we show that such a favorable setup is indeed possible in practice. In addition, the approach based on Chebyshev polynomials can be massively parallelized, and CheSS exhibits excellent scaling up to thousands of cores even for relatively small matrix sizes.

Complexity Reduction in Large Quantum Systems: Fragment Identification and Population Analysis via a Local Optimized Minimal Basis.

We present, within Kohn-Sham density functional theory calculations, a quantitative method to identify and assess the partitioning of a large quantum-mechanical system into fragments. We then show how within this framework simple generalizations of other well-known population analyses can be used to extract, from first-principles, reliable electrostatic multipoles for the identified fragments. Our approach reduces arbitrariness in the fragmentation procedure and enables the possibility to assess quantitatively whether the corresponding fragment multipoles can be interpreted as observable quantities associated with a system moiety. By applying our formalism within the code BigDFT, we show that the use of a minimal set of in situ-optimized basis functions allows at the same time a proper fragment definition and an accurate description of the electronic structure.

Fragment approach to constrained density functional theory calculations using Daubechies wavelets.

In a recent paper, we presented a linear scaling Kohn-Sham density functional theory (DFT) code based on Daubechies wavelets, where a minimal set of localized support functions are optimized in situ and therefore adapted to the chemical properties of the molecular system. Thanks to the systematically controllable accuracy of the underlying basis set, this approach is able to provide an optimal contracted basis for a given system: accuracies for ground state energies and atomic forces are of the same quality as an uncontracted, cubic scaling approach. This basis set offers, by construction, a natural subset where the density matrix of the system can be projected. In this paper, we demonstrate the flexibility of this minimal basis formalism in providing a basis set that can be reused as-is, i.e., without reoptimization, for charge-constrained DFT calculations within a fragment approach. Support functions, represented in the underlying wavelet grid, of the template fragments are roto-translated with high numerical precision to the required positions and used as projectors for the charge weight function. We demonstrate the interest of this approach to express highly precise and efficient calculations for preparing diabatic states and for the computational setup of systems in complex environments.

Accurate and efficient linear scaling DFT calculations with universal applicability.

Density functional theory calculations are computationally extremely expensive for systems containing many atoms due to their intrinsic cubic scaling. This fact has led to the development of so-called linear scaling algorithms during the last few decades. In this way it becomes possible to perform ab initio calculations for several tens of thousands of atoms within reasonable walltimes. However, even though the use of linear scaling algorithms is physically well justified, their implementation often introduces some small errors. Consequently most implementations offering such a linear complexity either yield only a limited accuracy or, if one wants to go beyond this restriction, require a tedious fine tuning of many parameters. In our linear scaling approach within the BigDFT package, we were able to overcome this restriction. Using an ansatz based on localized support functions expressed in an underlying Daubechies wavelet basis - which offers ideal properties for accurate linear scaling calculations - we obtain an amazingly high accuracy and a universal applicability while still keeping the possibility of simulating large system with linear scaling walltimes requiring only a moderate demand of computing resources. We prove the effectiveness of our method on a wide variety of systems with different boundary conditions, for single-point calculations as well as for geometry optimizations and molecular dynamics.

Minima hopping guided path search: an efficient method for finding complex chemical reaction pathways.

The Minima Hopping global optimization method uses physically realizable molecular dynamics moves in combination with an energy feedback that guarantees the escape from any potential energy funnel. For the purpose of finding reaction pathways, we argue that Minima Hopping is particularly suitable as a guide through the potential energy landscape and as a generator for pairs of minima that can be used as input structures for methods capable of finding transition states between two minima. For Lennard-Jones benchmark systems we compared this Minima Hopping guided path search method to a known approach for the exploration of potential energy landscapes that is based on deterministic mode-following. Although we used a stabilized mode-following technique that reliably allows to follow distinct directions when escaping from a local minimum, we observed that Minima Hopping guided path search is far superior in finding lowest-barrier reaction pathways. We, therefore, suggest that Minima Hopping guided path search can be used as a simple and efficient way to identify energetically low-lying chemical reaction pathways. Finally, we applied the Minima Hopping guided path search approach to 75-atom and 102-atom Lennard-Jones systems. For the 75-atom system we found pathways whose highest energies are significantly lower than the highest energy along the previously published lowest-barrier pathway. Furthermore, many of these pathways contain a smaller number of intermediate transition states than the previously publish lowest-barrier pathway. In case of the 102-atom system Minima Hopping guided path search found a previously unknown and energetically low-lying funnel.

Daubechies wavelets for linear scaling density functional theory.

We demonstrate that Daubechies wavelets can be used to construct a minimal set of optimized localized adaptively contracted basis functions in which the Kohn-Sham orbitals can be represented with an arbitrarily high, controllable precision. Ground state energies and the forces acting on the ions can be calculated in this basis with the same accuracy as if they were calculated directly in a Daubechies wavelets basis, provided that the amplitude of these adaptively contracted basis functions is sufficiently small on the surface of the localization region, which is guaranteed by the optimization procedure described in this work. This approach reduces the computational costs of density functional theory calculations, and can be combined with sparse matrix algebra to obtain linear scaling with respect to the number of electrons in the system. Calculations on systems of 10,000 atoms or more thus become feasible in a systematic basis set with moderate computational resources. Further computational savings can be achieved by exploiting the similarity of the adaptively contracted basis functions for closely related environments, e.g., in geometry optimizations or combined calculations of neutral and charged systems.

3D-printed and CNC milled flow-cells for chemiluminescence detection.

Herein we explore modern fabrication techniques for the development of chemiluminescence detection flow-cells with features not attainable using the traditional coiled tubing approach. This includes the first 3D-printed chemiluminescence flow-cells, and a milled flow-cell designed to split the analyte stream into two separate detection zones within the same polymer chip. The flow-cells are compared to conventional detection systems using flow injection analysis (FIA) and high performance liquid chromatography (HPLC), with the fast chemiluminescence reactions of an acidic potassium permanganate reagent with morphine and a series of adrenergic phenolic amines.

Metrics for measuring distances in configuration spaces.

In order to characterize molecular structures we introduce configurational fingerprint vectors which are counterparts of quantities used experimentally to identify structures. The Euclidean distance between the configurational fingerprint vectors satisfies the properties of a metric and can therefore safely be used to measure dissimilarities between configurations in the high dimensional configuration space. In particular we show that these metrics are a perfect and computationally cheap replacement for the root-mean-square distance (RMSD) when one has to decide whether two noise contaminated configurations are identical or not. We introduce a Monte Carlo approach to obtain the global minimum of the RMSD between configurations, which is obtained from a global minimization over all translations, rotations, and permutations of atomic indices.