References of "Tkatchenko, Alexandre 50009596"
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See detailCoulomb interactions between dipolar quantum fluctuations in van der Waals bound molecules and materials
Stoehr, Martin UL; Sadhukhan, Mainak; Al-Hamdani, Yasmine S. et al

in Nature Communications (2021), 12(1), 137

Mutual Coulomb interactions between electrons lead to a plethora of interesting physical and chemical effects, especially if those interactions involve many fluctuating electrons over large spatial scales ... [more ▼]

Mutual Coulomb interactions between electrons lead to a plethora of interesting physical and chemical effects, especially if those interactions involve many fluctuating electrons over large spatial scales. Here, we identify and study in detail the Coulomb interaction between dipolar quantum fluctuations in the context of van der Waals complexes and materials. Up to now, the interaction arising from the modification of the electron density due to quantum van der Waals interactions was considered to be vanishingly small. We demonstrate that in supramolecular systems and for molecules embedded in nanostructures, such contributions can amount to up to 6 kJ/mol and can even lead to qualitative changes in the long-range van der Waals interaction. Taking into account these broad implications, we advocate for the systematic assessment of so-called Dipole-Correlated Coulomb Singles in large molecular systems and discuss their relevance for explaining several recent puzzling experimental observations of collective behavior in nanostructured materials. [less ▲]

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See detailAccurate Many-Body Repulsive Potentials for Density-Functional Tight Binding from Deep Tensor Neural Networks
Stoehr, Martin UL; Medrano Sandonas, Leonardo UL; Tkatchenko, Alexandre UL

in Journal of Physical Chemistry Letters (2020), 11(16), 68356843

We combine density-functional tight binding (DFTB) with deep tensor neural networks (DTNN) to maximize the strengths of both approaches in predicting structural, energetic, and vibrational molecular ... [more ▼]

We combine density-functional tight binding (DFTB) with deep tensor neural networks (DTNN) to maximize the strengths of both approaches in predicting structural, energetic, and vibrational molecular properties. The DTNN is used to construct a nonlinear model for the localized many-body interatomic repulsive energy, which so far has been treated in an atom-pairwise manner in DFTB. Substantially improving upon standard DFTB and DTNN, the resulting DFTB-NNrep model yields accurate predictions of atomization and isomerization energies, equilibrium geometries, vibrational frequencies, and dihedral rotation profiles for a large variety of organic molecules compared to the hybrid DFT-PBE0 functional. Our results highlight the potential of combining semiempirical electronic-structure methods with physically motivated machine learning approaches for predicting localized many-body interactions. We conclude by discussing future advancements of the DFTB-NNrep approach that could enable chemically accurate electronic-structure calculations for systems with tens of thousands of atoms. [less ▲]

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See detailExploring chemical compound space with quantum-based machine learning
O. Anatole von Lilienfeld; Klaus- Robert Müller; Tkatchenko, Alexandre UL

in Nature Reviews Chemistry (2020)

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See detailFluctuational electrodynamics in atomic and macroscopic systems: van derWaals interactions and radiative heat transfer
Venkataram, Prashanth S.; Hermann, Jan; Tkatchenko, Alexandre UL et al

in Physical Review. B, Condensed Matter and Materials Physics (2020)

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See detailPredictive QM/MM modeling of modulations in protein-protein binding by lysine methylation
Rahman, Sanim; Wineman-Fisher, Vered; Al-Hamdani, Yasmine et al

in Journal of Molecular Biology (2020)

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See detailMachine learning for chemical discovery
Tkatchenko, Alexandre UL

in Nature Communications (2020)

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See detailMachine Learning Meets Quantum Physics
Schütt, Kristof T; Chmiela, Stefan; von Lilienfeld, O Anatole et al

in Springer Nature (2020)

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See detailvan der Waals interactions in material modelling
Hermann, Jan; Tkatchenko, Alexandre UL

in Handbook of Materials Modeling: Methods: Theory and Modeling (2020)

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See detailAccurate description of nuclear quantum effects with high-order perturbed path integrals (HOPPI)
Poltavskyi, Igor UL; Kapil, Venkat; Ceriotti, Michele et al

in Journal of Chemical Theory and Computation (2020)

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See detailNonlocal electronic correlations in the cohesive properties of high-pressure hydrogen solids
Cui, Ting-Ting; Li, Jian-Chen; Gao, Wang et al

in Journal of Physical Chemistry Letters (2020)

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See detailMachine learning for molecular simulation
Frank Noé; Tkatchenko, Alexandre UL; Klaus-Robert Müller et al

in Annual Review of Physical Chemistry (2020)

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See detailImproved description of ligand polarization enhances transferability of ion–ligand interactions
Wineman-Fisher, Vered; Al-Hamdani, Yasmine; Nagy, R Péter et al

in Journal of Chemical Physics (2020)

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See detailMolecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields
Sauceda, Huziel E; Gastegger, Michael; Chmiela, Stefan et al

in Journal of Chemical Physics (2020)

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See detailFrom quantum to continuum mechanics in the delamination of atomically-thin layers from substrates
Hauseux, Paul; Nguyen, Thanh-Tung; Ambrosetti, Alberto et al

in Nature Communications (2020)

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See detailDFTB+, a software package for efficient approximate density functional theory based atomistic simulations
Hourahine, Ben; Aradi, Bálint; Blum, Volker et al

in The Journal of Chemical Physics (2020), 152(12), 124101

DFTB+ is a versatile community developed open source software package offering fast and efficient methods for carrying out atomistic quantum mechanical simulations. By implementing various methods ... [more ▼]

DFTB+ is a versatile community developed open source software package offering fast and efficient methods for carrying out atomistic quantum mechanical simulations. By implementing various methods approximating density functional theory (DFT), such as the density functional based tight binding (DFTB) and the extended tight binding method, it enables simulations of large systems and long timescales with reasonable accuracy while being considerably faster for typical simulations than the respective ab initio methods. Based on the DFTB framework, it additionally offers approximated versions of various DFT extensions including hybrid functionals, time dependent formalism for treating excited systems, electron transport using non-equilibrium Green’s functions, and many more. DFTB+ can be used as a user-friendly standalone application in addition to being embedded into other software packages as a library or acting as a calculation-server accessed by socket communication. We give an overview of the recently developed capabilities of the DFTB+ code, demonstrating with a few use case examples, discuss the strengths and weaknesses of the various features, and also discuss on-going developments and possible future perspectives. [less ▲]

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See detailQuantum mechanics of proteins in explicit water: The role of plasmon-like solute-solvent interactions
Stoehr, Martin UL; Tkatchenko, Alexandre UL

in Science Advances (2019), 5(12), 0024

Quantum-mechanical van der Waals dispersion interactions play an essential role in intraprotein and protein-water interactions—the two main factors affecting the structure and dynamics of proteins in ... [more ▼]

Quantum-mechanical van der Waals dispersion interactions play an essential role in intraprotein and protein-water interactions—the two main factors affecting the structure and dynamics of proteins in water. Typically, these interactions are only treated phenomenologically, via pairwise potential terms in classical force fields. Here, we use an explicit quantum-mechanical approach of density-functional tight-binding combined with the many-body dispersion formalism and demonstrate the relevance of many-body van der Waals forces both to protein energetics and to protein-water interactions. In contrast to commonly used pairwise approaches, many-body effects substantially decrease the relative stability of native states in the absence of water. Upon solvation, the protein-water dispersion interaction counteracts this effect and stabilizes native conformations and transition states. These observations arise from the highly delocalized and collective character of the interactions, suggesting a remarkable persistence of electron correlation through aqueous environments and providing the basis for long-range interaction mechanisms in biomolecular systems. [less ▲]

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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 ▲]

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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 ▲]

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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 ▲]

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