Publications by authors named "Timo Sommer"

Article Synopsis
  • The text discusses the importance of databases for molecules and materials in chemical research, particularly those that include quantum chemistry data from methods like density functional theory.
  • It highlights the success of platforms like the Materials Project in the materials community while noting that molecular quantum data is still scattered across various datasets without a unified system.
  • The authors present seven guiding principles, called QUANTUM, based on the FAIR principles, aimed at improving the integration and development of molecular databases for better accessibility and usefulness in future research.
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Silicon nitride is a low-loss photonic integrated circuit (PIC) platform. However, silicon nitride also shows small nonlinear optical properties and is dielectric, which makes the implementation of programmability challenging. Typically, the thermo-optic effect is used for this, but modulators based on this effect are often slow and cross talk-limited.

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Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors, the use of data science tools is to date slowed down by a lack of accessible data. In this work, we present a new and publicly available superconductivity dataset ('3DSC'), featuring the critical temperature T of superconducting materials additionally to tested non-superconductors.

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Optical integrated quantum computing protocols, in particular using the dual-rail encoding, require that waveguides cross each other to realize, e.g., SWAP or Toffoli gate operations.

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Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials.

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Aluminum nitride (AlN) is an emerging material for integrated quantum photonics due to its large χ nonlinearity. Here we demonstrate the hybrid integration of AlN on silicon nitride (SiN) photonic chips. Composite microrings are fabricated by reactive DC sputtering of c-axis oriented AlN on top of pre-patterned SiN.

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Visualizing eigenmodes is crucial in understanding the behavior of state-of-the-art micromechanical devices. We demonstrate a method to optically map multiple modes of mechanical structures simultaneously. The fast and robust method, based on a modified phase-lock loop, is demonstrated on a silicon nitride membrane and shown to outperform three alternative approaches.

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