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See detailNanoscale Phononic Analog of the Ranque-Hilsch Vortex Tube
Medrano Sandonas, Leonardo UL; Rodríguez Méndez, Álvaro; Gutierrez, Rafael et al

in Physical Review Applied (2021), 15(034008),

Thermal management is a current global challenge that must be addressed exhaustively. We propose the design of a nanoscale phononic analog of the Ranque-Hilsch vortex tube in which heat flowing at a given ... [more ▼]

Thermal management is a current global challenge that must be addressed exhaustively. We propose the design of a nanoscale phononic analog of the Ranque-Hilsch vortex tube in which heat flowing at a given temperature is split into two different streams going to the two ends of the device, inducing a temperature asymmetry. Our nanoscale prototype consists of two carbon nanotubes (capped and open) connected by molecular chains. The results show that the structural asymmetry in the contact regions is the key factor for producing the flux asymmetry and, hence, the induced temperature-bias effect. The effect can be controlled by tuning the thermal-equilibration temperature, the number of chains, and the chain length. Deposition on a substrate adds another variable to the manipulation of the flux asymmetry but the effect vanishes at very large substrate temperatures. Our study yields insights into the thermal management in nanoscale materials, especially the crucial issue of whether the thermal asymmetry can survive phonon scattering over relatively long distances, and thus provides a starting point for the design of a nanoscale phononic analog of the Ranque-Hilsch vortex tube. [less ▲]

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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 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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