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We present an algorithm to find first order saddle points on the potential energy surface (PES). The algorithm is formulated as a constrained optimization problem that involves two sets of atomic coordinates (images), a time-varying distance constraint and a constraint on the energy difference. Both images start in different valleys of the PES and are pulled toward each other by gradually reducing the distance. The search space is restricted to the pairs of configurations that share the same potential energy. By minimizing the energy while the distance shrinks, a minimum of the constrained search space is tracked. In simple cases, the two images are confined to their respective sides of the barrier until they finally converge near the saddle point. If one image accidentally crosses the barrier, the path is split at suitable locations and the algorithm is repeated recursively. The optimization is implemented as a combination of a quasi-Newton optimization and a linear constraint. The method was tested on a set of Lennard-Jones-38 cluster transitions and a set of 121 molecular reactions using density functional theory calculations. The efficiency in terms of energy and force evaluation is better than with competing methods as long as they do not switch to single-ended methods. The construction of a continuous search path with small steps and the ability to focus on arbitrary subsegments of the path provide an additional value in terms of robustness and flexibility. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We report six new dynamically stable structures of SrTiO3 at various pressures ranging from 0 to 200 GPa. These structures were found by exploring the enthalpy surface with the Minima Hopping structure prediction method. The potential energy surface was generated by a machine learned potential, the charge equilibration via neural network technique (CENT), based on an extensive training data set of highly diverse SrTiO3 periodic and cluster structures. All our CENT structures were validated at the level of density functional theory. For our new structures, we performed phonon calculations and NVT molecular dynamics calculations to investigate their dynamical stability. Finally, X‐ray diffraction patterns were simulated to help to identify our predicted structures in experiments. [ABSTRACT FROM AUTHOR]

A new intermetallic compound, the first to be structurally identified in the Cu−Bi binary system, is reported. This compound is accessed by high-pressure reaction of the elements. Its detailed characterization, physical property measurements, and ab initio calculations are described. The commensurate crystal structure of Cu11Bi7 is a unique variation of the NiAs structure type. Temperature-dependent electrical resistivity and heat capacity measurements reveal a bulk superconducting transition at Tc=1.36 K. Density functional theory calculations further demonstrate that Cu11Bi7 can be stabilized (relative to decomposition into the elements) at high pressure and temperature. These results highlight the ability of high-pressure syntheses to allow for inroads into heretofore-undiscovered intermetallic systems for which no thermodynamically stable binaries are known. [ABSTRACT FROM AUTHOR]

Novel ZnSb polymorphs with improved thermoelectric (TE) properties are discovered by a systematic ab initio structural search based on DFT calculations revealing the glass-like energy landscape and polymorphism of the system. [ABSTRACT FROM AUTHOR]

The structure of metallic glasses is a long-standing question because the lack of long-range order makes diffraction based techniques difficult to be applied. Here, we used scanning tunneling microscopy with large tunneling resistance of 6 GΩ at low temperature in order to minimize forces between probe and sample and reduce thermal fluctuations of metastable structures. Under these extremely gentle conditions, atomic structures of Ni40Ta60 metallic glasses are revealed with unprecedented lateral resolution. In agreement with previous models and experiments, icosahedral-like clusters are observed. The clusters show a high degree of mobility, which explains the need of low temperatures for stable imaging. In addition to icosahedrons, chain-like structures are resolved and comparative density functional theory (DFT) calculations confirm that these structures are meta-stable. The co-existence of icosahedral and chain-like structures might be an key ingredient for the understanding of the mechanical properties of metallic glasses. [ABSTRACT FROM AUTHOR]

Based on a recently introduced metric for measuring distances between configurations, we introduce distance-energy (DE) plots to characterize the potential energy surface of clusters. Producing such plots is computationally feasible on the density functional level since it requires only a few hundred stable low energy configurations including the global minimum. By using standard criteria based on disconnectivity graphs and the dynamics of Lennard-Jones clusters, we show that the DE plots convey the necessary information about the character of the potential energy surface and allow us to distinguish between glassy and nonglassy systems. We then apply this analysis to real clusters at the density functional theory level and show that both glassy and nonglassy clusters can be found in simulations. It turns out that among our investigated clusters only those can be synthesized experimentally which exhibit a nonglassy landscape. [ABSTRACT FROM AUTHOR]

Abstract: In this contribution we will describe in detail a Density Functional Theory method based on a Daubechies wavelets basis set, named BigDFT. We will see that, thanks to wavelet properties, this code shows high systematic convergence properties, very good performances and an excellent efficiency for parallel calculations. BigDFT code operation are also well-suited for GPU acceleration. We will discuss how the problematic of fruitfully benefit of this new technology can be match with the needs of robustness and flexibility of a complex code like BigDFT. This work may be of interest not only for expert in electronic structure calculations, but may also provide feedback to the wider community of high performance scientific computing. [Copyright &y& Elsevier]

Based on an analysis of the short-range chemical environment of each atom in a system, standard machine-learning-based approaches to the construction of interatomic potentials aim at determining directly the central quantity, which is the total energy. This prevents, for instance, an accurate description of the energetics of systems in which long-range charge transfer or ionization is important. We propose therefore not to target directly with machine-learning methods the total energy but an intermediate physical quantity, namely, the charge density, which then in turn allows us to determine the total energy. By allowing the electronic charge to distribute itself in an optimal way over the system, we can describe not only neutral but also ionized systems with unprecedented accuracy. We demonstrate the power of our approach for both neutral and ionized NaCl clusters where charge redistribution plays a decisive role for the energetics. We are able to obtain chemical accuracy, i.e., errors of less than a millihartree per atom compared to the reference density functional results for a huge data set of configurations with large structural variety. The introduction of physically motivated quantities which are determined by the short-range atomic environment via a neural network also leads to an increased stability of the machine-learning process and transferability of the potential. [ABSTRACT FROM AUTHOR]

The distance dependence and atomic-scale contrast recently observed in nominal contact potential difference (CPD) signals simultaneously recorded by Kelvin probe force microscopy (KPFM) using noncontact atomic force microscopy (NCAFM) on defect-free surfaces of insulating as well as semiconducting samples have stimulated theoretical attempts to explain such effects. Especially in the case of insulators, it is not quite clear how the applied bias voltage affects electrostatic forces acting on the atomic scale. We attack this problem in two steps. First, the electrostatics of the macroscopic tip-cantilever-sample system is treated by a finite-difference method on an adjustable nonuniform mesh. Then the resulting electric field under the tip apex is inserted into a series of atomistic wavelet-based density functional theory (DFT) calculations. Results are shown for a realistic neutral but reactive silicon nanoscale tip interacting with a NaCl(001) sample. Bias-dependent forces and resulting atomic displacements are computed to within an unprecedented accuracy. Theoretical expressions for amplitude modulation (AM) and frequency modulation (FM) KPFM signals and for the corresponding local contact potential differences (LCPD) are obtained by combining the macroscopic and atomistic contributions to the electrostatic force component generated at the voltage modulation frequency, and evaluated for several tip oscillation amplitudes A up to 10 nm. For A = 0.1 Å, the computed LCPD contrast is proportional to the slope of the atomistic force versus bias in the AM mode and to its derivative with respect to the tip-sample separation in the FM mode. Being essentially constant over a few volts, this slope is the basic quantity that determines variations of the atomic-scale LCPD contrast. Already above A = 1 Å, the LCPD contrasts in both modes exhibit almost the same spatial dependence as the slope. In the AM mode, this contrast is approximately proportional to A-1/2, but remains much weaker than the contrast in the FM mode, which drops somewhat faster as A is increased. These trends are a consequence of the macroscopic contributions to the KPFM signal, which are stronger in the AM-mode and especially important if the sample is an insulator even at subnanometer separations where atomic-scale contrast appears. [ABSTRACT FROM AUTHOR]