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.

SchNet - A deep learning architecture for molecules and materials.

Abstract: Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning, in general, and deep learning, in particular, are ideally suitable for representing quantum-mechanical interactions, enabling us to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for molecules and materials, where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study on the quantum-mechanical properties of C20-fullerene that would have been infeasible with regular ab initio molecular dynamics.

Structure-borne sound

Abstract: Although sound waves in structures cannot be heard directly, and only be felt at low frequencies, they play an important role in noise control, because many sound signals are generated or transmitted in structures before they are radiated into the surrounding medium. In several respects sound waves in structures and sound waves in gases or liquids are similar, there are, however, also fundamental differences, which are due to the fact that solids have a certain shear stiffness, wheras gases or liquids show practically none. As a consequence acoustic energy can be transported not only by the normal compressional waves but also by shear waves and many combinations of compressional (sometimes loosely called longitudinal) and shear waves . For noise control purposes bending waves (which are a special combination of compressional and shear waves) are of primary importance; for some special cases (quasi-) longitudinal waves and torsional waves also have to be considered.

Quantum-chemical insights from deep tensor neural networks.

Abstract: Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol−1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems. Machine learning is an increasingly popular approach to analyse data and make predictions. Here the authors develop a ‘deep learning’ framework for quantitative predictions and qualitative understanding of quantum-mechanical observables of chemical systems, beyond properties trivially contained in the training data.

Compositional segregation in shaped Pt alloy nanoparticles and their structural behaviour during electrocatalysis

Abstract: Shape-selective monometallic nanocatalysts offer activity benefits based on structural sensitivity and high surface area. In bimetallic nanoalloys with well-defined shape, site-dependent metal surface segregation additionally affects the catalytic activity and stability. However, segregation on shaped alloy nanocatalysts and their atomic-scale evolution is largely unexplored. Exemplified by three octahedral PtxNi1-x alloy nanoparticle electrocatalysts with unique activity for the oxygen reduction reaction at fuel cell cathodes, we reveal an unexpected compositional segregation structure across the {111} facets using aberration-corrected scanning transmission electron microscopy and electron energy-loss spectroscopy. In contrast to theoretical predictions, the pristine PtxNi1-x nano-octahedra feature a Pt-rich frame along their edges and corners, whereas their Ni atoms are preferentially segregated in their {111} facet region. We follow their morphological and compositional evolution in electrochemical environments and correlate this with their exceptional catalytic activity. The octahedra preferentially leach in their facet centres and evolve into 'concave octahedra'. More generally, the segregation and leaching mechanisms revealed here highlight the complexity with which shape-selective nanoalloys form and evolve under reactive conditions.