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See detailQM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
Hoja, Johannes UL; Medrano Sandonas, Leonardo UL; Ernst, Brian G. et al

in Scientific Data (2021), 8(43),

We introduce QM7-X, a comprehensive dataset of 42 physicochemical properties for ≈4.2 million equilibrium and non-equilibrium structures of small organic molecules with up to seven non-hydrogen (C, N, O ... [more ▼]

We introduce QM7-X, a comprehensive dataset of 42 physicochemical properties for ≈4.2 million equilibrium and non-equilibrium structures of small organic molecules with up to seven non-hydrogen (C, N, O, S, Cl) atoms. To span this fundamentally important region of chemical compound space (CCS), QM7-X includes an exhaustive sampling of (meta-)stable equilibrium structures—comprised of constitutional/structural isomers and stereoisomers, e.g., enantiomers and diastereomers (including cis-/trans- and conformational isomers)—as well as 100 non-equilibrium structural variations thereof to reach a total of ≈4.2 million molecular structures. Computed at the tightly converged quantum-mechanical PBE0+MBD level of theory, QM7-X contains global (molecular) and local (atom-in-a-molecule) properties ranging from ground state quantities (such as atomization energies and dipole moments) to response quantities (such as polarizability tensors and dispersion coefficients). By providing a systematic, extensive, and tightly-converged dataset of quantum-mechanically computed physicochemical properties, we expect that QM7-X will play a critical role in the development of next-generation machine-learning based models for exploring greater swaths of CCS and performing in silico design of molecules with targeted properties. [less ▲]

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

in Lecture Notes in 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 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 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 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 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 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 detailMolecular basis for SARS-CoV-2 spike affinity for human ACE2 receptor
Delgado, Julian M; Duro, Nalvi; Rogers, David M et al

in bioRxiv (2020)

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See detailMachine learning for chemical discovery
Tkatchenko, Alexandre UL

in Nature Communications (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 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 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 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 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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