ERC FUNDED PROJECTS

The suite of graphene’s unique properties and applications can be enormously enhanced by its functionalization. As non-covalently functionalized graphenes do not target all graphene’s properties and may suffer from limited stability, covalent functionalization represents a promising way for controlling graphene’s properties. To date, only a few well-defined graphene derivatives have been introduced. Among them, fluorographene (FG) stands out as a prominent member because of its easy synthesis and high stability. Being a perfluorinated hydrocarbon, FG was believed to be as unreactive as the two-dimensional counterpart perfluoropolyethylene (Teflon®). However, our recent experiments showed that FG is not chemically inert and can be used as a viable precursor for synthesizing graphene derivatives. This surprising behavior indicates that common textbook grade knowledge cannot blindly be applied to the chemistry of 2D materials. Further, there might be specific rules behind the chemistry of 2D materials, forming a new chemical discipline we tentatively call 2D chemistry. The main aim of the project is to explore, identify and apply the rules of 2D chemistry starting from FG. Using the knowledge gained of 2D chemistry, we will attempt to control the chemistry of various 2D materials aimed at preparing stable graphene derivatives with designed properties, e.g., 1-3 eV band gap, fluorescent properties, sustainable magnetic ordering and dispersability in polar media. The new graphene derivatives will be applied in sensing, imaging, magnetic delivery and catalysis and new emerging applications arising from the synergistic phenomena are expected. We envisage that new applications will be opened up that benefit from the 2D scaffold and tailored properties of the synthesized derivatives. The derivatives will be used for the synthesis of 3D hybrid materials by covalent linking of the 2D sheets joined with other organic and inorganic molecules, nanomaterials or biomacromolecules.

1 831 103 €

Start date: 2016-06-01, End date: 2022-05-31

ACADEMIA proposes a pioneering study of a neglected corpus of manuscripts stemming from the practice of quodlibetal debates held at Faculties of Arts of European universities, flourishing from the 14th to the early 16th century. As prescribed by the university statutes, dozens of professors participated periodically in these unique collective works of the Middle Ages, which encompassed all the disciplines pursued at the university, from logic to medicine to theology. The PI hypothesises that the professors presented at the hitherto mostly ignored quodlibets their recent scientific innovations, which they then published in the first collective volumes of European academia. The PI thus proposes a novel theoretical framework for understanding the quodlibets: they stand at the origin of the modern concept of science as a collective intellectual enterprise, similar to modern conferences and the subsequent dissemination of results. This makes them and their written form critical for understanding European intellectual and scientific traditions, both past and present. ACADEMIA’s ambition is to establish the corpus of these debates as a new field of study through an extensive examination of manuscripts, thus filling a substantial gap, radically extending the fields of the history of universities and intellectual history, and reconstructing the roots of the modern practice of fostering collective science. A complex analysis of the corpus will bring about a substantial change in our understanding of medieval practices of the production and sharing of knowledge. Aiming to examine the quodlibets as a phenomenon successively interconnecting European intellectual space, ACADEMIA focuses on fourteen universities at which the PI has identified the tradition so far and on their mutual relations and development. ACADEMIA employs an interdisciplinary team and an innovative combination of approaches from history, codicology, palaeography, philology, hermeneutics and Digital Humanities.

1 260 485 €

Start date: 2021-07-01, End date: 2026-06-30

The goal of the AI4REASON project is a breakthrough in what is considered a very hard problem in AI and automation of reasoning, namely the problem of automatically proving theorems in large and complex theories. Such complex formal theories arise in projects aimed at verification of today's advanced mathematics such as the Formal Proof of the Kepler Conjecture (Flyspeck), verification of software and hardware designs such as the seL4 operating system kernel, and verification of other advanced systems and technologies on which today's information society critically depends. It seems extremely complex and unlikely to design an explicitly programmed solution to the problem. However, we have recently demonstrated that the performance of existing approaches can be multiplied by data-driven AI methods that learn reasoning guidance from large proof corpora. The breakthrough will be achieved by developing such novel AI methods. First, we will devise suitable Automated Reasoning and Machine Learning methods that learn reasoning knowledge and steer the reasoning processes at various levels of granularity. Second, we will combine them into autonomous self-improving AI systems that interleave deduction and learning in positive feedback loops. Third, we will develop approaches that aggregate reasoning knowledge across many formal, semi-formal and informal corpora and deploy the methods as strong automation services for the formal proof community. The expected outcome is our ability to prove automatically at least 50% more theorems in high-assurance projects such as Flyspeck and seL4, bringing a major breakthrough in formal reasoning and verification. As an AI effort, the project offers a unique path to large-scale semantic AI. The formal corpora concentrate centuries of deep human thinking in a computer-understandable form on which deductive and inductive AI can be combined and co-evolved, providing new insights into how humans do mathematics and science.

1 499 500 €

Start date: 2015-09-01, End date: 2020-10-31

The description of high pressure phases or polymorphism of molecular solids represents a significant scientific challenge both for experiment and theory. Theoretical methods that are currently used struggle to describe the tiny energy differences between different phases. It is the aim of this project to develop a scheme that would allow accurate and reliable predictions of the binding energies of molecular solids and of the energy differences between different phases. To reach the required accuracy, we will combine the coupled cluster approach, widely used for reference quality calculations for molecules, with the random phase approximation (RPA) within periodic boundary conditions. As I have recently shown, RPA-based approaches are already some of the most accurate and practically usable methods for the description of extended systems. However, reliability is not only a question of accuracy. Reliable data need to be precise, that is, converged with the numerical parameters so that they are reproducible by other researchers. Reproducibility is already a growing concern in the field. It is likely to become a considerable issue for highly accurate methods as the calculated energies have a stronger dependence on the simulation parameters such as the basis set size. Two main approaches will be explored to assure precision. First, we will develop the so-called asymptotic correction scheme to speed-up the convergence of the correlation energies with the basis set size. Second, we will directly compare the lattice energies from periodic and finite cluster based calculations. Both should yield identical answers, but if and how the agreement can be reached for general system is currently far from being understood for methods such as coupled cluster. Reliable data will allow us to answer some of the open questions regarding the stability of polymorphs and high pressure phases, such as the possibility of existence of high pressure ionic phases of water and ammonia.

924 375 €

Start date: 2018-01-01, End date: 2022-12-31