Quantum phases of matter in novel 2D materials host fascinating correlated electron properties, such as unconventional superconductivity, novel insulating phases and exotic magnetic order. These phenomena are a hotbed of new forms of energy-efficient technologies, which require fundamental understanding and exploration of these material classes. Since the beginning, scientists have been struggling with the puzzling lack of consistent predictability of such materials, leading predominantly to serendipitous discoveries. The key ingredient driving these exotic quantum phases are electron-electron interactions, so-called correlations. These correlations between the electrons play a prominent role in their movement, and often result into atomic-scale charge and spin order, and are amplified in 2D materials compared to their 3D counterparts. Owing to the 2D nature, a new state-of-the-art methodology is needed to elucidate the electronic and magnetic properties in correlated 2D quantum materials. DeQ investigates the role of electron correlations and their interplay with structural and spin degrees of freedom at the single-atom level in insulating quantum phases of novel 2D materials. To accomplish this aim, my innovative strategy is to quantify atomic-scale charge and spin order at transitions between different quantum phases in three classes of hallmark 2D materials: twisted bilayers, correlated quasi-2D compounds, and 2D magnetic materials. My novel approach is based on creating a new state of the art in atomic imaging and spectroscopy, the JAQ setup. The development of JAQ will enable us to precisely tune relevant parameters, like electric and magnetic fields, in the highest-quality materials available. The outcome of DeQ will be groundbreaking for predicting electron correlations in novel quantum phases in 2D materials, which that are a hotbed of innovative forms of energy-efficient technologies.

Replication and division are two of the most fundamental properties of living systems. Without replication, Darwinian evolution would not be possible, and life could never have reached the degree of complexity we see today. However, exactly how mixtures of non-living molecules developed the ability to replicate and divide, remains one of the biggest mysteries in modern science. Various molecular replicators have been investigated previously, but they are all destined to become extinct by dilution, since they lack a surrounding compartment that divides spontaneously during replication. In this proposal, we aim at developing a new class of coacervate-based protocells that are capable of active growth and template-directed replication. The coacervates we propose here are condensed liquid droplets with a unique dual role: they act as a compartment that holds together and concentrates the template molecules and the building blocks, and they provide the right chemical environment for the replication reactions to take place at an appreciable rate. The coacervate-based protocells are composed of oligopeptides with low complexity sequences, inspired by the intrinsically disordered proteins found in membrane-free organelles inside cells. Active growth is achieved through fuel-driven reactions, either by elongation of existing peptides or by specific chemical modifications at the peptide side chains that enhance their coacervation potential. Longer peptides can also act as templates for conjugation of end-functionalized peptide fragments with sequence patterns complementary to the template. Protocells with sufficiently high growth or replication rates are not only stable against Ostwald ripening, but are also predicted to undergo spontaneous division through a shape instability. This would mark a key step in the emergence of minimal cells and open the way for the evolution of more complex life-like systems.

How did three soldiers override their initial freezing response to overpower an armed terrorist in the Thalys-train to Paris in 2015? This question is relevant for anyone aiming to optimize approach-avoidance (AA) decisions during threat. It is particularly relevant for patients with anxiety disorders whose persistent avoidance is key to the maintenance of their anxiety. Computational psychiatry has made great progress in formalizing how we make (mal)adaptive decisions. Current models, however, largely ignore the transient psychophysiological state of the decision maker. Parasympathetic state and flexibility in switching between para- and sympathetic states are directly related to freezing, and are known to bias AA-decisions toward avoidance. The central aim of this research program is to forge a mechanistic understanding of how we compute AA-decisions on the basis of those psychophysiological states, and to identify alterations in anxiety patients in order to guide new personalized neurocognitive interventions into their persistent avoidance. I will develop a neurocomputational model of AA-decisions that accounts for transient psychophysiological states, in order to define which decision parameters are altered in active and passive avoidance in anxiety. I will test causal premises of the model using state-of-the-art techniques, including pharmacological and electrophysiological interventions. Based on these insights I will for the first time apply personalized brain stimulation to anxiety patients. Clinically, this project should open the way to effective intervention with fearful avoidance in anxiety disorders that rank among the most common, costly and persistent mental disorders. Theoretically, conceptualizing transient psychophysiological states as causal factor in AA-decision models is essential to understanding passive and active avoidance. Optimizing AA-decisions also holds broad societal relevance given currently increased fearful avoidance of outgroups.