The incorporation of heavier Group 14 double bonds (E=E) (E = Si, Ge) into the main chain of polymers poses considerable synthetic challenges. The catalyst- and byproduct-free polymerization protocols proposed herein build upon exciting preliminary results regarding molecular model systems reported by the host group. Three different strategies will give access to various unprecedented polymer types: (1) The development of a reliable protecting group strategy in Si=Si chemistry will enable the synthesis of silicon- and germanium- analogues of poly(phenylenevinylene)s (dimetalla-PPVs). (2) The reactions of difunctional bis(alkynyl)arenes and bis(isocyano)arenes, respectively, with arylene-bridged tetrasiladienes will give access to sigma-pi conjugated hybrid polymers. (3) The concept of stabilizing low-valent main-group species by N-heterocyclic carbene (NHC) and subsequent removal of NHC by a Lewis acid to generate double bonds between Group 14 elements will afford poly(digermene)s. Entirely unprecedented building blocks will be used so that this project will be at the forefront of the newly emerging field of applications and property driven chemistry of heavier main-group elements. It will provide the synthetic tools necessary to exploit the anticipated unique physical and chemical properties of unsaturated main-group polymers. With this explicit focus on material aspects, the proposed research has an excellent fit with the European priorities: despite being fundamental in nature, the project targets smart polymers with bespoke properties, may well develop significant economic and consequentially also societal impact in the longer run. The synergistic combination of the areas of expertise of PKM (chemistry of Group 14 and 15, N-heterocyclic carbenes) and DS (low-coordinate main-group chemistry) will fruitfully enrich both scientific mindsets and weave close ties for future cooperation after PKM excelled in his ambition to obtain an academic position in research.

Introducing molecules into quantum cavities to enhance selected physical properties or to control chemical processes is a highly active domain of research. The coupled system of molecules and the resonant electromagnetic field, called a molecular polariton, have properties very different from those of free molecules, providing a possibility of non-invasively influencing molecular systems on a quantum level. Developments in experimental techniques and electronic structure methods continuously open up new possibilities of tuning polaritons for specific applications. The description of molecules in quantum cavities requires models deeply rooted in quantum electrodynamics and necessitates novel perspectives in wavefunction-based methods. Although it is now possible to calculate the potential energy surfaces of polaritons, the description of other properties, especially response functions, is an open challenge. In this project, we aim to develop theories and methods for the calculation of polaritonic response properties in quantum cavities, with the goal to understand the possibilities of quantum control of intra- and intermolecular processes. Our developments are expected to provide experimental and fundamental research with a theoretical framework and software implementations for the future design of chemical processes in quantum cavities. Achieving this result will rely on an interdisciplinary work between theoretical molecular physics, high-performance software development, as well as applied computational chemistry methods. This synergy between electronic structure theory expertise of the Host with the background in response function theory of the Applicant will also be supported by a larger effort at the Saarland University to utilize quantum cavities in molecular science. Overall, the successful project will give a theoretical background for planning polaritonic devices for tailored applications by a non-invasive altering physical and chemical properties.

To date, the design of ethical machine learning (ML) algorithms has been dominated by technology owners and remains broadly criticized for strategically seeking to avoid legally enforceable restrictions. In order to foster trust in ML technologies, society demands technology designers to deeply engage all relevant stakeholders in the ML development. This ERC project aims at responding to this call with a society-aware approach to ML (SAML). My goal is to enable the collaborative design of ML algorithms, so that they are not only driven by economic interests of the technology owners but are agreed upon by all stakeholders, and ultimately, trusted by society. To this end, I aim to develop multi-party ML algorithms that explicitly account for the goals of different stakeholders---i.e., owners, those experts that design the algorithm (e.g., technology companies); consumers, those that are affected by the algorithm (e.g., users); and regulators, those experts that set the regulatory framework for their use (e.g., policy makers). The proposed methodology will enable quantifying and jointly optimizing the business goals of the owners (e.g., profit); the benefits of the consumers (e.g., information access); and the risks defined by the regulators (e.g., societal polarization). The SAML project involves a high-risk/high-gain paradigm shift from an owner-centered to a society-centered (multi-party) ML design. On the one hand, it will require significant and challenging methodological innovations at every stage of the ML development: from the data collection all the way to the algorithm learning. On the other hand, it will impact how ML technologies are deployed in society by enabling an informed discussion among different stakeholders and, in general, by society about these new technologies. The results of this project will provide the urgently needed methodological foundations to ensure that these new technologies are at the service of society.

The pivotal role of software in our modern world mandates strong requirements on quality, correctness, and reliability of software systems. In software development and maintenance, the ability to understand program artifacts plays a key role for programmers to fulfill these requirements. Despite significant progress, research on program comprehension has a fundamental limitation: program comprehension is a cognitive process that cannot be directly observed, which leaves considerable room for misinterpretation, uncertainty, and confounders. In Brains On Code, we will develop a neuroscientific foundation of program comprehension. Instead of merely observing whether there is a difference regarding program comprehension (e.g., between two programming methods), we aim at precisely and reliably determining the key factors that cause the difference. This is especially challenging as humans are the subjects of study, and inter-personal variance and other confounding factors obfuscate the results. The key idea of Brains On Code is to leverage established methods from cognitive neuroscience to obtain insights into the underlying processes and influential factors of program comprehension. Brains On Code will pursue a multimodal approach that integrates different neuro-physiological measures as well as a cognitive computational modeling approach to establish the theoretical foundation. This way, Brains On Code will lay the foundations of measuring and modeling program comprehension and offer substantial feedback for programming methodology, language design, and education. Addressing longstanding foundational questions such as "How can we reliably measure program comprehension?", "What makes a program hard to understand?", and "What skills should programmers have?" will become into reach. A success of Brains On Code would not only help answer these questions, but also provide an outline for applying the methodology beyond program code (models, specifications, etc.).