van der Waals density functionals built upon the electron-gas tradition: Facing the challenge of competing interactions

The theoretical description of sparse matter attracts much interest, in particular for those ground-state properties that can be described by density functional theory (DFT). One proposed approach, the van der Waals density functional (vdW-DF) method, rests on strong physical foundations and offers simple yet accurate and robust functionals. A very recent functional within this method called vdW-DF-cx [K. Berland and P. Hyldgaard, Phys. Rev. B 89, 035412] stands out in its attempt to use an exchange energy derived from the same plasmon-based theory from which the nonlocal correlation energy was derived. Encouraged by its good performance for solids, layered materials, and aromatic molecules, we apply it to several systems that are characterized by competing interactions. These include the ferroelectric response in PbTiO$_3$, the adsorption of small molecules within metal-organic frameworks (MOFs), the graphite/diamond phase transition, and the adsorption of an aromatic-molecule on the Ag(111) surface. Our results indicate that vdW-DF-cx is overall well suited to tackle these challenging systems. In addition to being a competitive density functional for sparse matter, the vdW-DF-cx construction presents a more robust general purpose functional that could be applied to a range of materials problems with a variety of competing interactions

Understanding adhesion at as-deposited interfaces from ab initio thermodynamics of deposition growth: thin-film alumina on titanium carbide

We investigate the chemical composition and adhesion of chemical vapour deposited thin-film alumina on TiC using and extending a recently proposed nonequilibrium method of ab initio thermodynamics of deposition growth (AIT-DG) [Rohrer J and Hyldgaard P 2010 Phys. Rev. B 82 045415]. A previous study of this system [Rohrer J, Ruberto C and Hyldgaard P 2010 J. Phys.: Condens. Matter 22 015004] found that use of equilibrium thermodynamics leads to predictions of a non-binding TiC/alumina interface, despite the industrial use as a wear-resistant coating. This discrepancy between equilibrium theory and experiment is resolved by the AIT-DG method which predicts interfaces with strong adhesion. The AIT-DG method combines density functional theory calculations, rate-equation modelling of the pressure evolution of the deposition environment and thermochemical data. The AIT-DG method was previously used to predict prevalent terminations of growing or as-deposited surfaces of binary materials. Here we extent the method to predict surface and interface compositions of growing or as-deposited thin films on a substrate and find that inclusion of the nonequilibrium deposition environment has important implications for the nature of buried interfaces.Comment: 8 pages, 6 figures, submitted to J. Phys.: Condens. Matte

Performance of van der Waals Corrected Functionals for Guest Adsorption in the M-2(dobdc) Metal-Organic Frameworks

Small-molecule binding in metal–organic frameworks (MOFs) can be accurately studied both experimentally and computationally, provided the proper tools are employed. Herein, we compare and contrast properties associated with guest binding by means of density functional theory (DFT) calculations using nine different functionals for the M2(dobdc) (dobdc4– = 2,5-dioxido,1,4-benzenedicarboxylate) series, where M = Mg, Mn, Fe, Co, Ni, Cu, and Zn. Additionally, we perform Quantum Monte Carlo (QMC) calculations for one system to determine if this method can be used to assess the performance of DFT. We also make comparisons with previously published experimental results for carbon dioxide and water and present new methane neutron powder diffraction (NPD) data for further comparison. All of the functionals are able to predict the experimental variation in the binding energy from one metal to the next; however, the interpretation of the performance of the functionals depends on which value is taken as the reference. On the one hand, if we compare against experimental values, we would conclude that the optB86b-vdW and optB88-vdW functionals systematically overestimate the binding strength, while the second generation of van der Waals (vdW) nonlocal functionals (vdw-DF2 and rev-vdW-DF2) correct for this providing a good description of binding energies. On the other hand, if the QMC calculation is taken as the reference then all of the nonlocal functionals yield results that fall just outside the error of the higher-level calculation. The empirically corrected vdW functionals are in reasonable agreement with experimental heat of adsorptions but under bind when compared with QMC, while Perdew–Burke–Ernzerhof fails by more than 20 kJ/mol regardless of which reference is employed. All of the functionals, with the exception of vdW-DF2, predict reasonable framework and guest binding geometries when compared with NPD measurements. The newest of the functionals considered, rev-vdW-DF2, should be used in place of vdW-DF2, as it yields improved bond distances with similar quality binding energies

On post-Lie algebras, Lie--Butcher series and moving frames

Pre-Lie (or Vinberg) algebras arise from flat and torsion-free connections on differential manifolds. They have been studied extensively in recent years, both from algebraic operadic points of view and through numerous applications in numerical analysis, control theory, stochastic differential equations and renormalization. Butcher series are formal power series founded on pre-Lie algebras, used in numerical analysis to study geometric properties of flows on euclidean spaces. Motivated by the analysis of flows on manifolds and homogeneous spaces, we investigate algebras arising from flat connections with constant torsion, leading to the definition of post-Lie algebras, a generalization of pre-Lie algebras. Whereas pre-Lie algebras are intimately associated with euclidean geometry, post-Lie algebras occur naturally in the differential geometry of homogeneous spaces, and are also closely related to Cartan's method of moving frames. Lie--Butcher series combine Butcher series with Lie series and are used to analyze flows on manifolds. In this paper we show that Lie--Butcher series are founded on post-Lie algebras. The functorial relations between post-Lie algebras and their enveloping algebras, called D-algebras, are explored. Furthermore, we develop new formulas for computations in free post-Lie algebras and D-algebras, based on recursions in a magma, and we show that Lie--Butcher series are related to invariants of curves described by moving frames.Comment: added discussion of post-Lie algebroid

Perspectives on weak interactions in complex materials at different length scales

Nanocomposite materials consist of nanometer-sized quantum objects such as atoms, molecules, voids or nanoparticles embedded in a host material. These quantum objects can be exploited as a super-structure, which can be designed to create material properties targeted for specific applications. For electromagnetism, such targeted properties include field enhancements around the bandgap of a semiconductor used for solar cells, directional decay in topological insulators, high kinetic inductance in superconducting circuits, and many more. Despite very different application areas, all of these properties are united by the common aim of exploiting collective interaction effects between quantum objects. The literature on the topic spreads over very many different disciplines and scientific communities. In this review, we present a cross-disciplinary overview of different approaches for the creation, analysis and theoretical description of nanocomposites with applications related to electromagnetic properties

Perspectives on weak interactions in complex materials at different length scales

Nanocomposite materials consist of nanometer-sized quantum objects such as atoms, molecules, voids or nanoparticles embedded in a host material. These quantum objects can be exploited as a super-structure, which can be designed to create material properties targeted for specific applications. For electromagnetism, such targeted properties include field enhancements around the bandgap of a semiconductor used for solar cells, directional decay in topological insulators, high kinetic inductance in superconducting circuits, and many more. Despite very different application areas, all of these properties are united by the common aim of exploiting collective interaction effects between quantum objects. The literature on the topic spreads over very many different disciplines and scientific communities. In this review, we present a cross-disciplinary overview of different approaches for the creation, analysis and theoretical description of nanocomposites with applications related to electromagnetic properties

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.This study was supported by COST Action CA18131 “Statistical and machine learning techniques in human microbiome studies”. Estonian Research Council grant PRG548 (JT). Spanish State Research Agency Juan de la Cierva Grant IJC2019-042188-I (LM-Z). EO was founded and OA was supported by Estonian Research Council grant PUT 1371 and EMBO Installation grant 3573. AG was supported by Statutory Research project of the Department of Computer Networks and Systems

High Resolution Melt analysis for mutation screening in PKD1 and PKD2

<p>Abstract</p> <p>Background</p> <p>Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disorder. It is characterized by focal development and progressive enlargement of renal cysts leading to end-stage renal disease. <it>PKD1 </it>and <it>PKD2 </it>have been implicated in ADPKD pathogenesis but genetic features and the size of <it>PKD1 </it>make genetic diagnosis tedious.</p> <p>Methods</p> <p>We aim to prove that high resolution melt analysis (HRM), a recent technique in molecular biology, can facilitate molecular diagnosis of ADPKD. We screened for mutations in <it>PKD1 </it>and <it>PKD2 </it>with HRM in 37 unrelated patients with ADPKD.</p> <p>Results</p> <p>We identified 440 sequence variants in the 37 patients. One hundred and thirty eight were different. We found 28 pathogenic mutations (25 in <it>PKD1 </it>and 3 in <it>PKD2 </it>) within 28 different patients, which is a diagnosis rate of 75% consistent with literature mean direct sequencing diagnosis rate. We describe 52 new sequence variants in <it>PKD1 </it>and two in <it>PKD2</it>.</p> <p>Conclusion</p> <p>HRM analysis is a sensitive and specific method for molecular diagnosis of ADPKD. HRM analysis is also costless and time sparing. Thus, this method is efficient and might be used for mutation pre-screening in ADPKD genes.</p