Three SB professors awarded SNSF Advanced Grants

Due to Switzerland's status as a non-associated third country in Horizon Europe, the SNSF has launched the transitional measure “SNSF Advanced Grants 2022” on behalf of the federal government. The call was aimed at researchers who wish to carry out innovative, high-risk research in Switzerland.

The SNSF has now announced the 18 awardees of the 2022 call for Advanced Grants. Among them are Professors Beat Fierz, Tobias Kippenberg, and Berend Smit at EPFL’s School of Basic Sciences.

Project descriptions

SITE-SPECIFIC ACCESS OF REGULATORY CHROMATIN IN CELLS (siteSEARCH) – Beat Fierz

Transcription factors are proteins that search the genome and bind to highly specific DNA sites, turning nearby genes ‘on’ or ‘off’. These mechanisms are fundamentally important for cell function through all live stages, and allow cells to react to external signals such as hormones. But how can transcription factors search the ~ 6 billion base pairs of the genome to find their target sites? To complicate matters, the human genomic DNA is packaged into a nucleoprotein complex called chromatin, and is thus generally not physically accessible. Therefore, many questions remain about how transcription factors function.

The project siteSEARCH aims to develop new chemical biology and imaging methods to observe this process directly in living cells, focusing on the important transcription factor class of human steroid receptors. The project will bridge detailed in vitro studies with single-molecule investigations in cells, and has potentially wide-ranging implications for our understanding of gene control.

HYBRID NONLINEAR INTEGRATED PHOTONIC CIRCUITS (HEROIC) – Tobias Kippenberg

Over the past 20 years, integrated photonics has become a key ingredient to optical data communications. They have also become indispensable in large-scale data centers, replacing lossy and bandwidth-limited copper cables and allowing for fast and energy-efficient communication between servers. This revolution was made possible by compact transceivers based on silicon photonics1 that can transmit and receive data over short distances. However, silicon is not an ideal material for optics: it lacks a direct bandgap for light emission, exhibits strongly nonlinear losses such as two-photon absorption and subsequent free- carrier absorption that limit power handling, exhibits high propagation losses – even with the most advanced micro-electronic processing.

The “Hybrid nonlinear integrated photonic circuits” project will lay the foundation of a new generation of photonic integrated circuits, which use or contain one or more novel material platforms into a single “hybrid” system while exhibiting unprecedented low loss. Such hybrid integrated photonic devices will offer a compact form factor, wafer-scale manufacturability, and novel physical principles of light generation and amplification. Equally, such circuits will enable performance that is not possible today with currently existing technologies, either bulk component or fiber based, or using silicon photonics. The project will lay the technological foundation for much broader adoption of photonic integrated circuits beyond data centers for optical frequency metrology, optical AMO Physics, and integrated photonics in the UV, as well as contribute to a new generation of technology to make data center communication more efficient.

Such next-generation integrated photonic circuits have tremendous potential to allow for continuing the development of the next generation of energy efficient and high-bandwidth transceivers, which are urgently needed in data centers and HPC for AI accelerators, but can equally provide solutions for emerging applications, such as ranging (LiDAR) sensors, or information processing through hybrid computing in AI applications, as well as emerging quantum science and technology.

BIG DATA IN NANOPOROUS MATERIALS: SCIENCE BEYOND UNDERSTANDING – Berend Smit

Metal-organic frameworks (MOFs) are the ideal playground for data science in Chemistry and Chemical Engineering. MOFs are crystalline materials consisting of a metal node and an organic linker. By combining different metal nodes and organic linkers, chemists can synthesize infinite materials for applications ranging from gas separation, gas storage, sensing, catalysis, etc. The holy grain of MOF synthesis is to design an optimal MOF for a given application.

There are some fundamental reasons why we are still far from this goal. Chemical design space is infinite, and all possible MOFs are impossible to screen, experimentally or computationally. In addition, we need to find out the optimal materials in the whole design space. Different mechanisms might give an optimal material, or the design space is multi-objective, in case we cannot even rank materials.

In this project, we aim to develop data-science methods to obtain a global insight into the molecular characteristics, the “materials gene,” that makes a particular material promising for a given application. If we have identified the optimal metal node and linker combination, one must find the right synthesis conditions, which is, at present, mainly trial and error. We aim to develop a machine-learning approach to leverage the data of 100,000 synthesized MOFs to obtain insights into whether and how a particular MOF can be synthesized.