Technical University of Berlin

About: Technical University of Berlin is a education organization based out in Berlin, Germany. It is known for research contribution in the topics: Laser & Catalysis. The organization has 27292 authors who have published 59342 publications receiving 1414623 citations. The organization is also known as: Technische Universität Berlin & TU Berlin.

Triazine‐Based Graphitic Carbon Nitride: a Two‐Dimensional Semiconductor

Abstract: Graphitic carbon nitride has been predicted to be structurally analogous to carbon-only graphite, yet with an inherent bandgap. We have grown, for the first time, macroscopically large crystalline thin films of triazine-based, graphitic carbon nitride (TGCN) using an ionothermal, interfacial reaction starting with the abundant monomer dicyandiamide. The films consist of stacked, two-dimensional (2D) crystals between a few and several hundreds of atomic layers in thickness. Scanning force and transmission electron microscopy show long-range, in-plane order, while optical spectroscopy, X-ray photoelectron spectroscopy, and density functional theory calculations corroborate a direct bandgap between 1.6 and 2.0 eV. Thus TGCN is of interest for electronic devices, such as field-effect transistors and light-emitting diodes.

The underestimated potential of solar energy to mitigate climate change

Abstract: Despite being currently under-represented in IPCC reports, PV generation represents a growing share of power generation. This Perspective argues that underestimating PV potential led to suboptimal integration measures and that specific deployment strategies for emerging economies should be developed.

Bypassing the Kohn-Sham equations with machine learning

Abstract: Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields. Machine learning holds the promise of learning the energy functional via examples, bypassing the need to solve the Kohn-Sham equations. This should yield substantial savings in computer time, allowing larger systems and/or longer time-scales to be tackled, but attempts to machine-learn this functional have been limited by the need to find its derivative. The present work overcomes this difficulty by directly learning the density-potential and energy-density maps for test systems and various molecules. We perform the first molecular dynamics simulation with a machine-learned density functional on malonaldehyde and are able to capture the intramolecular proton transfer process. Learning density models now allows the construction of accurate density functionals for realistic molecular systems.Machine learning allows electronic structure calculations to access larger system sizes and, in dynamical simulations, longer time scales. Here, the authors perform such a simulation using a machine-learned density functional that avoids direct solution of the Kohn-Sham equations.

Experimental observation of chimeras in coupled-map lattices

Abstract: In systems of oscillators, phase-locking behaviour can, in theory, coexist with incoherent dynamics—invoking the fabled chimera state. Now, experimental realization of a coupled-map lattice reveals dynamical states displaying coexisting spatial domains of coherence and incoherence.