References of "Tkatchenko, Alexandre 50009596"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailUnifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
Schütt, Kristof; Gastegger, Michael; Tkatchenko, Alexandre UL et al

in Nature Communications (2019), 10(1), 5024

Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate ... [more ▼]

Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of molecular structures for targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry. [less ▲]

Detailed reference viewed: 66 (1 UL)
Full Text
Peer Reviewed
See detailImpact of nuclear vibrations on van der Waals and Casimir interactions at zero and finite temperature
Venkataram, Prashanth; Hermann, Jan; Vongkovit, Teerit et al

in Science Advances (2019), 5(1), 0456

Recent advances in measuring van der Waals (vdW) interactions have probed forces on molecules at nanometric separations from metal surfaces and demonstrated the importance of infrared nonlocal ... [more ▼]

Recent advances in measuring van der Waals (vdW) interactions have probed forces on molecules at nanometric separations from metal surfaces and demonstrated the importance of infrared nonlocal polarization response and temperature effects, yet predictive theories for these systems remain lacking. We present a theoretical framework for computing vdW interactions among molecular structures, accounting for geometry, short-range electronic delocalization, dissipation, and collective nuclear vibrations (phonons) at atomic scales, along with long-range electromagnetic interactions in arbitrary macroscopic environments. We primarily consider experimentally relevant low-dimensional carbon allotropes, including fullerenes, carbyne, and graphene, and find that phonons couple strongly with long-range electromagnetic fields depending on molecular dimensionality and dissipation, especially at nanometric scales, creating delocalized phonon polaritons that substantially modify infrared molecular response. These polaritons, in turn, alter vdW interaction energies between molecular and macroscopic structures, producing nonmonotonic power laws and nontrivial temperature variations at nanometric separations feasible in current experiments. [less ▲]

Detailed reference viewed: 17 (5 UL)
Full Text
Peer Reviewed
See detailSchNetPack: A Deep Learning Toolbox For Atomistic Systems
Schütt, Kristof; Kessel, Pan; Gastegger, Michael et al

in Journal of Chemical Theory and Computation (2019), 15

SchNetPack is a toolbox for the development and application of deep neural networks that predict potential energy surfaces and other quantum-chemical properties of molecules and materials. It contains ... [more ▼]

SchNetPack is a toolbox for the development and application of deep neural networks that predict potential energy surfaces and other quantum-chemical properties of molecules and materials. It contains basic building blocks of atomistic neural networks, manages their training, and provides simple access to common benchmark datasets. This allows for an easy implementation and evaluation of new models. For now, SchNetPack includes implementations of (weighted) atom-centered symmetry functions and the deep tensor neural network SchNet, as well as ready-to-use scripts that allow one to train these models on molecule and material datasets. Based on the PyTorch deep learning framework, SchNetPack allows one to efficiently apply the neural networks to large datasets with millions of reference calculations, as well as parallelize the model across multiple GPUs. Finally, SchNetPack provides an interface to the Atomic Simulation Environment in order to make trained models easily accessible to researchers that are not yet familiar with neural networks. [less ▲]

Detailed reference viewed: 44 (0 UL)
Full Text
Peer Reviewed
See detailAdvances in Density-Functional Calculations for Materials Modeling
Maurer, Reinhard; Freysoldt, Christoph; Reilly, Anthony et al

in Annual Review of Materials Research (2019), 49

During the past two decades, density-functional (DF) theory has evolved from niche applications for simple solid-state materials to become a workhorse method for studying a wide range of phenomena in a ... [more ▼]

During the past two decades, density-functional (DF) theory has evolved from niche applications for simple solid-state materials to become a workhorse method for studying a wide range of phenomena in a variety of system classes throughout physics, chemistry, biology, and materials science. Here, we review the recent advances in DF calculations for materials modeling, giving a classification of modern DF-based methods when viewed from the materials modeling perspective. While progress has been very substantial, many challenges remain on the way to achieving consensus on a set of universally applicable DF-based methods for materials modeling. Hence, we focus on recent successes and remaining challenges in DF calculations for modeling hard solids, molecular and biological matter, low-dimensional materials, and hybrid organic-inorganic materials. [less ▲]

Detailed reference viewed: 40 (0 UL)
Full Text
Peer Reviewed
See detailsGDML: Constructing accurate and data efficient molecular force fields using machine learning
Chmiela, Stefan; Sauceda, Huziel; Poltavsky, Igor et al

in Computer Physics Communications (2019), 240

We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global potential energy surfaces ... [more ▼]

We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global potential energy surfaces (PES) for molecules with a few dozen atoms from a limited number of user-provided reference molecular conformations and the associated atomic forces. Here, we introduce a Python software package to reconstruct and evaluate custom sGDML force fields (FFs), without requiring in-depth knowledge about the details of the model. A user-friendly command-line interface offers assistance through the complete process of model creation, in an effort to make this novel machine learning approach accessible to broad practitioners. Our paper serves as a documentation, but also includes a practical application example of how to reconstruct and use a PBE0+MBD FF for paracetamol. Finally, we show how to interface sGDML with the FF simulation engines ASE (Larsen et al., 2017) and i-PI (Kapil et al., 2019) to run numerical experiments, including structure optimization, classical and path integral molecular dynamics and nudged elastic band calculations. [less ▲]

Detailed reference viewed: 44 (2 UL)
Full Text
Peer Reviewed
See detailComputational polymorph screening reveals late-appearing and poorly-soluble form of rotigotine
Mortazavi, Majid; Hoja, Johannes; Aerts, Luc et al

in Communications Chemistry (2019), 2

The active pharmaceutical ingredient rotigotine—a dopamine agonist for the treatment of Parkinson’s and restless leg diseases—was known to exist in only one polymorphic form since 1985. In 2008, the ... [more ▼]

The active pharmaceutical ingredient rotigotine—a dopamine agonist for the treatment of Parkinson’s and restless leg diseases—was known to exist in only one polymorphic form since 1985. In 2008, the appearance of a thermodynamically more stable and significantly less soluble polymorph led to a massive batch recall followed by economic and public health implications. Here, we carry out state-of-the-art computational crystal structure prediction, revealing the late-appearing polymorph without using any prior information. In addition, we predict a third crystalline form of rotigotine having thermodynamic stability between forms I and II. We provide quantitative description of the relative stability and solubility of the rotigotine polymorphs. Our study offers new insights into a challenging polymorphic system and highlights the robustness of contemporary computational crystal structure prediction during pharmaceutical development. [less ▲]

Detailed reference viewed: 32 (0 UL)
Full Text
Peer Reviewed
See detailTheory and practice of modeling van der Waals interactions in electronic-structure calculations
Stöhr, Martin; Van Voorhis, Troy; Tkatchenko, Alexandre UL

in Chemical Society Reviews (2019), 48

The accurate description of long-range electron correlation, most prominently including van der Waals (vdW) dispersion interactions, represents a particularly challenging task in the modeling of molecules ... [more ▼]

The accurate description of long-range electron correlation, most prominently including van der Waals (vdW) dispersion interactions, represents a particularly challenging task in the modeling of molecules and materials. vdW forces arise from the interaction of quantum-mechanical fluctuations in the electronic charge density. Within (semi-)local density functional approximations or Hartree–Fock theory such interactions are neglected altogether. Non-covalent vdW interactions, however, are ubiquitous in nature and play a key role for the understanding and accurate description of the stability, dynamics, structure, and response properties in a plethora of systems. During the last decade, many promising methods have been developed for modeling vdW interactions in electronic-structure calculations. These methods include vdW-inclusive Density Functional Theory and correlated post-Hartree–Fock approaches. Here, we focus on the methods within the framework of Density Functional Theory, including non-local van der Waals density functionals, interatomic dispersion models within many-body and pairwise formulation, and random phase approximation-based approaches. This review aims to guide the reader through the theoretical foundations of these methods in a tutorial-style manner and, in particular, highlight practical aspects such as the applicability and the advantages and shortcomings of current vdW-inclusive approaches. In addition, we give an overview of complementary experimental approaches, and discuss tools for the qualitative understanding of non-covalent interactions as well as energy decomposition techniques. Besides representing a reference for the current state-of-the-art, this work is thus also designed as a concise and detailed introduction to vdW-inclusive electronic structure calculations for a general and broad audience. [less ▲]

Detailed reference viewed: 34 (0 UL)
Full Text
Peer Reviewed
See detailQuantitative imaging of electric surface potentials with single-atom sensitivity
Wagner, Christian; Green, Matthew; Maiworm, Michael et al

in Nature Materials (2019), 18

Because materials consist of positive nuclei and negative electrons, electric potentials are omnipresent at the atomic scale. However, due to the long range of the Coulomb interaction, large-scale ... [more ▼]

Because materials consist of positive nuclei and negative electrons, electric potentials are omnipresent at the atomic scale. However, due to the long range of the Coulomb interaction, large-scale structures completely outshine small ones. This makes the isolation and quantification of the electric potentials that originate from nanoscale objects such as atoms or molecules very challenging. Here we report a non-contact scanning probe technique that addresses this challenge. It exploits a quantum dot sensor and the joint electrostatic screening by tip and surface, thus enabling quantitative surface potential imaging across all relevant length scales down to single atoms. We apply the technique to the characterization of a nanostructured surface, thereby extracting workfunction changes and dipole moments for important reference systems. This authenticates the method as a versatile tool to study the building blocks of materials and devices down to the atomic scale. [less ▲]

Detailed reference viewed: 29 (1 UL)
Full Text
Peer Reviewed
See detailMolecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
Sauceda, Huziel; Chmiela, Stefan; Poltavsky, Igor et al

in Journal of Chemical Physics (2019), 150

We present the construction of molecular force fields for small molecules (less than 25 atoms) using the recently developed symmetrized gradient-domain machine learning (sGDML) approach [Chmiela et al ... [more ▼]

We present the construction of molecular force fields for small molecules (less than 25 atoms) using the recently developed symmetrized gradient-domain machine learning (sGDML) approach [Chmiela et al., Nat. Commun. 9, 3887 (2018) and Chmiela et al., Sci. Adv. 3, e1603015 (2017)]. This approach is able to accurately reconstruct complex high-dimensional potential-energy surfaces from just a few 100s of molecular conformations extracted from ab initio molecular dynamics trajectories. The data efficiency of the sGDML approach implies that atomic forces for these conformations can be computed with high-level wavefunction-based approaches, such as the “gold standard” coupled-cluster theory with single, double and perturbative triple excitations [CCSD(T)]. We demonstrate that the flexible nature of the sGDML model recovers local and non-local electronic interactions (e.g., H-bonding, proton transfer, lone pairs, changes in hybridization states, steric repulsion, and n → π* interactions) without imposing any restriction on the nature of interatomic potentials. The analysis of sGDML molecular dynamics trajectories yields new qualitative insights into dynamics and spectroscopy of small molecules close to spectroscopic accuracy. [less ▲]

Detailed reference viewed: 43 (1 UL)
Full Text
Peer Reviewed
See detailIon-Hydroxyl Interactions: From High-Level Quantum Benchmarks to Transferable Polarizable Force Fields
Wineman-Fisher, Vered; Al-Hamdani, Yasmine; Addou, Iqbal et al

in Journal of Chemical Theory and Computation (2019), 154

Ion descriptors in molecular mechanics models are calibrated against reference data on ion–water interactions. It is then typically assumed that these descriptors will also satisfactorily describe ... [more ▼]

Ion descriptors in molecular mechanics models are calibrated against reference data on ion–water interactions. It is then typically assumed that these descriptors will also satisfactorily describe interactions of ions with other functional groups, such as those present in biomolecules. However, several studies now demonstrate that this transferability assumption produces, in many different cases, large errors. Here we address this issue in a representative polarizable model and focus on transferability of cationic interactions from water to a series of alcohols. Both water and alcohols use hydroxyls for ion-coordination, and, therefore, this set of molecules constitutes the simplest possible case of transferability. We obtain gas phase reference data systematically from “gold-standard” quantum Monte Carlo and CCSD(T) methods, followed by benchmarked vdW-corrected DFT. We learn that the original polarizable model yields large gas phase water → alcohol transferability errors – the RMS and maximum errors are 2.3 and 5.1 kcal/mol, respectively. These errors are, nevertheless, systematic in that ion-alcohol interactions are overstabilized, and systematic errors typically imply that some essential physics is either missing or misrepresented. A comprehensive analysis shows that when both low- and high-field responses of ligand dipole polarization are described accurately, then transferability improves significantly – the RMS and maximum errors in the gas phase reduce, respectively, to 0.9 and 2.5 kcal/mol. Additionally, predictions of condensed phase transfer free energies also improve. Nevertheless, within the limits of the extrathermodynamic assumptions necessary to separate experimental estimates of salt dissolution into constituent cationic and anionic contributions, we note that the error in the condensed phase is systematic, which we attribute, at least, partially to the parametrization in long-range electrostatics. Overall, this work demonstrates a rational approach to boosting transferability of ionic interactions that will be applicable broadly to improving other polarizable and nonpolarizable models. [less ▲]

Detailed reference viewed: 21 (0 UL)
Full Text
Peer Reviewed
See detailNonadditivity of the Adsorption Energies of Linear Acenes on Au(111): Molecular Anisotropy and Many-Body Effects
Maass, Friedrich; Ajdari, Mohsen; Cheenicode Kabeer, Fairoja et al

in Journal of Physical Chemistry Letters (2019), 10

Adsorption energies of chemisorbed molecules on inorganic solids usually scale linearly with molecular size and are well described by additive scaling laws. However, much less is known about scaling laws ... [more ▼]

Adsorption energies of chemisorbed molecules on inorganic solids usually scale linearly with molecular size and are well described by additive scaling laws. However, much less is known about scaling laws for physisorbed molecules. Our temperature-programmed desorption experiments demonstrate that the adsorption energy of acenes (benzene to pentacene) on the Au(111) surface in the limit of low coverage is highly nonadditive with respect to the molecular size. For pentacene, the deviation from an additive scaling of the adsorption energy amounts to as much as 0.7 eV. Our first-principles calculations explain the observed nonadditive behavior in terms of anisotropy of molecular polarization stemming from many-body electronic correlations. The observed nonadditivity of the adsorption energy has implications for surface-mediated intermolecular interactions and the ensuing on-surface self-assembly. Thus, future coverage-dependent studies should aim to gain insights into the impact of these complex interactions on the self-assembly of π-conjugated organic molecules on metal surfaces. [less ▲]

Detailed reference viewed: 21 (0 UL)
Full Text
Peer Reviewed
See detailUnderstanding non-covalent interactions in larger molecular complexes from first principles
Al-Hamdani, Yasmine; Tkatchenko, Alexandre UL

in Journal of Chemical Physics (2019), 150

Non-covalent interactions pervade all matter and play a fundamental role in layered materials, biological systems, and large molecular complexes. Despite this, our accumulated understanding of non ... [more ▼]

Non-covalent interactions pervade all matter and play a fundamental role in layered materials, biological systems, and large molecular complexes. Despite this, our accumulated understanding of non-covalent interactions to date has been mainly developed in the tens-of-atoms molecular regime. This falls considerably short of the scales at which we would like to understand energy trends, structural properties, and temperature dependencies in materials where non-covalent interactions have an appreciable role. However, as more reference information is obtained beyond moderately sized molecular systems, our understanding is improving and we stand to gain pertinent insights by tackling more complex systems, such as supramolecular complexes, molecular crystals, and other soft materials. In addition, accurate reference information is needed to provide the drive for extending the predictive power of more efficient workhorse methods, such as density functional approximations that also approximate van der Waals dispersion interactions. In this perspective, we discuss the first-principles approaches that have been used to obtain reference interaction energies for beyond modestly sized molecular complexes. The methods include quantum Monte Carlo, symmetry-adapted perturbation theory, non-canonical coupled cluster theory, and approaches based on the random-phase approximation. By considering the approximations that underpin each method, the most accurate theoretical references for supramolecular complexes and molecular crystals to date are ascertained. With these, we also assess a handful of widely used exchange-correlation functionals in density functional theory. The discussion culminates in a framework for putting into perspective the accuracy of high-level wavefunction-based methods and identifying future challenges. [less ▲]

Detailed reference viewed: 33 (2 UL)
Full Text
Peer Reviewed
See detailReliable and practical computational description of molecular crystal polymorphs
Hoja, Johannes; Ko, Hsin-Yu; Neumann, Marcus A. et al

in Science Advances (2019), 5

Reliable prediction of the polymorphic energy landscape of a molecular crystal would yield profound insight into drug development in terms of the existence and likelihood of late-appearing polymorphs ... [more ▼]

Reliable prediction of the polymorphic energy landscape of a molecular crystal would yield profound insight into drug development in terms of the existence and likelihood of late-appearing polymorphs. However, the computational prediction of molecular crystal polymorphs is highly challenging due to the high dimensionality of conformational and crystallographic space accompanied by the need for relative free energies to within 1 kJ/mol per molecule. In this study, we combine the most successful crystal structure sampling strategy with the most successful first-principles energy ranking strategy of the latest blind test of organic crystal structure prediction methods. Specifically, we present a hierarchical energy ranking approach intended for the refinement of relative stabilities in the final stage of a crystal structure prediction procedure. Such a combined approach provides excellent stability rankings for all studied systems and can be applied to molecular crystals of pharmaceutical importance. [less ▲]

Detailed reference viewed: 39 (2 UL)
Full Text
Peer Reviewed
See detailStability of functionalized platform molecules on Au(111)
Jasper-Tönnies, Torben; Poltavskyi, Igor UL; Ulrich, Sandra et al

in Journal of Chemical Physics (2018), 149

Trioxatriangulenium (TOTA) platform molecules were functionalized with methyl, ethyl, ethynyl, propynyl, and hydrogen and sublimated onto Au(111) surfaces. Low-temperature scanning tunneling microscopy ... [more ▼]

Trioxatriangulenium (TOTA) platform molecules were functionalized with methyl, ethyl, ethynyl, propynyl, and hydrogen and sublimated onto Au(111) surfaces. Low-temperature scanning tunneling microscopy data reveal that >99% of ethyl-TOTA and methyl-TOTA remain intact, whereas 60% of H-TOTA and >99% of propynyl-TOTA and ethynyl-TOTA decompose. The observed tendency toward fragmentation on Au(111) is opposite to the sequence of gas-phase stabilities of the molecules. Although Au(111) is the noblest of all metal surfaces, the binding energies of the decomposition products to Au(111) destabilize the functionalized platforms by 2 to 3.9 eV (190–370 kJ/mol) and even render some of them unstable as revealed by density functional theory calculations. Van der Waals forces are important, as they drive the adsorption of the platform molecules. [less ▲]

Detailed reference viewed: 100 (0 UL)
Full Text
Peer Reviewed
See detailTowards exact molecular dynamics simulations with machine-learned force fields
Chmiela, Stefan; Sauceda, Huziel E.; Müller, Klaus-Robert et al

in Nature Communications (2018), 9

Detailed reference viewed: 340 (5 UL)
Full Text
Peer Reviewed
See detaili-PI 2.0: A Universal Force Engine for Advanced Molecular Simulations
Kapil, Venkat; Rossi, Mariana; Marsalek, Ondrej et al

in Computer Physics Communications (2018)

Progress in the atomic-scale modeling of matter over the past decade has been tremendous. This progress has been brought about by improvements in methods for evaluating interatomic forces that work by ... [more ▼]

Progress in the atomic-scale modeling of matter over the past decade has been tremendous. This progress has been brought about by improvements in methods for evaluating interatomic forces that work by either solving the electronic structure problem explicitly, or by computing accurate approximations of the solution and by the development of techniques that use the Born–Oppenheimer (BO) forces to move the atoms on the BO potential energy surface. As a consequence of these developments it is now possible to identify stable or metastable states, to sample configurations consistent with the appropriate thermodynamic ensemble, and to estimate the kinetics of reactions and phase transitions. All too often, however, progress is slowed down by the bottleneck associated with implementing new optimization algorithms and/or sampling techniques into the many existing electronic-structure and empirical-potential codes. To address this problem, we are thus releasing a new version of the i-PI software. This piece of software is an easily extensible framework for implementing advanced atomistic simulation techniques using interatomic potentials and forces calculated by an external driver code. While the original version of the code (Ceriotti et al., 2014) was developed with a focus on path integral molecular dynamics techniques, this second release of i-PI not only includes several new advanced path integral methods, but also offers other classes of algorithms. In other words, i-PI is moving towards becoming a universal force engine that is both modular and tightly coupled to the driver codes that evaluate the potential energy surface and its derivatives. [less ▲]

Detailed reference viewed: 112 (1 UL)
Full Text
Peer Reviewed
See detailPhonon-Polariton Mediated Thermal Radiation and Heat Transfer among Molecules and Macroscopic Bodies: Nonlocal Electromagnetic Response at Mesoscopic Scales
Venkataram, Prashanth S.; Hermann, Jan; Tkatchenko, Alexandre UL et al

in Physical Review Letters (2018), 121

Detailed reference viewed: 98 (1 UL)
Full Text
Peer Reviewed
See detailCapturing intensive and extensive DFT/TDDFT molecular properties with machine learning
Pronobis, Wiktor; Sch utt, Kristof; Tkatchenko, Alexandre UL et al

in European Physical Journal B -- Condensed Matter (2018), 91

Machine learning has been successfully applied to the prediction of chemical properties of small organic molecules such as energies or polarizabilities. Compared to these properties, the electronic ... [more ▼]

Machine learning has been successfully applied to the prediction of chemical properties of small organic molecules such as energies or polarizabilities. Compared to these properties, the electronic excitation energies pose a much more challenging learning problem. Here, we examine the applicability of two existing machine learning methodologies to the prediction of excitation energies from time-dependent density functional theory. To this end, we systematically study the performance of various 2- and 3-body descriptors as well as the deep neural network SchNet to predict extensive as well as intensive properties such as the transition energies from the ground state to the rst and second excited state. As perhaps expected current state-of-the-art machine learning techniques are more suited to predict extensive as opposed to intensive quantities. We speculate on the need to develop global descriptors that can describe both extensive and intensive properties on equal footing. [less ▲]

Detailed reference viewed: 97 (1 UL)
Full Text
Peer Reviewed
See detailSubtle Fluorination of Conjugated Molecules Enables Stable Nanoscale Assemblies on Metal Surfaces
Niederhausen, Jens; Zhang, Yuan; Kabeer, Fairoja et al

in Journal of Physical Chemistry C (2018), 122(33), 10

In molecular self-assembly on surfaces, the structure is governed by the intricate balance of attractive and repulsive forces between molecules as well as between molecules and the substrate. Frequently ... [more ▼]

In molecular self-assembly on surfaces, the structure is governed by the intricate balance of attractive and repulsive forces between molecules as well as between molecules and the substrate. Frequently, repulsive interactions between molecules adsorbed on a metal surface dominate in the low-coverage regime, and dense self-assembled structures can only be observed close to full monolayer coverage. Here, we demonstrate that fluorination at selected positions of conjugated molecules provides for sufficiently strong, yet nonrigid, H···F bonding capability that (i) enables the formation of stable nanoscale molecular assemblies on a metal surface and (ii) steers the assemblies’ structure. This approach should be generally applicable and will facilitate the construction and study of individual nanoscale molecular assemblies with structures that are not attainable in the high-coverage regime. [less ▲]

Detailed reference viewed: 83 (0 UL)
Full Text
Peer Reviewed
See detailvan der Waals Interactions in Material Modelling
Hermann, Jan; Tkatchenko, Alexandre UL

in Handbook of Materials Modeling: Methods: Theory and Modelling (2018)

Van der Waals (vdW) interactions stem from electronic zero-point fluctuations and are often critical for the correct description of structure, stability, and response properties of molecules and materials ... [more ▼]

Van der Waals (vdW) interactions stem from electronic zero-point fluctuations and are often critical for the correct description of structure, stability, and response properties of molecules and materials, including biomolecules, nanomaterials, and material interfaces. Here, we give a conceptual as well as mathematical overview of the current state of modeling vdW interactions,focusing in particular on the consequences of different approximations for practical applications. We present a systematic classification of approximate first-principles models based on the adiabatic-connection fluctuation-dissipation theorem, namely the nonlocal density functionals, interatomic methods, and methods based on the random-phase approximation. The applicability of these methods to different types of materials and material properties is discussed in connection with availability of theoretical and experimental benchmarks. We conclude with a roadmap of the open problems that remain to be solved to construct a universal, efficient, and accurate vdW model for realistic material modeling. [less ▲]

Detailed reference viewed: 171 (5 UL)