Strong light-matter coupling (SC) is increasingly proposed as a powerful tool for post-synthetic control of the optoelectronic properties of organic materials. This technology aims to exploit the easily tuneable polariton states arising from the SC between confined light fields and excitons in organic materials to rewrite molecular energy landscapes and redirect physical pathways. Singlet fission (SF) is a promising technology for improving the efficiency of photovoltaic solar cells beyond their theoretical limit. The SF process consists of the splitting of a singlet excited state into two entangled triplet-triplet states that later become two independent triplets, yielding up to two excited states per absorbed photon –hence, more efficient solar cells. Despite its great potential, SF has been observed only in a limited number of organic compounds and in many cases with a low efficiency, being the synthesis of new derivatives a huge challenge. Recently, some theoretical studies proposed SC as a post-synthesis solution to enhance the SF performance of inefficient materials, by controlling their energy landscape. However, the growing difficulty in reproducing key results in the field of Organic Polaritonics (OP) suggests a poor understanding of the involved phenomena. The major research ambition of this MSCA proposal is to understand the working principles in the OP field and demonstrate that SC can be exploited to enhance the SF efficiency. The implementation of this MSCA proposal will provide a deep knowledge of SC at the molecular scale and how to control it at the macroscale within polaritonic devices, realizing the post-synthetic control of the molecular properties. This achievement will lead to important breakthroughs in Materials Science and Photonics, setting the basis for the OP field. Besides, the proposed research and training activities will expand my experience, research expertise and networks, providing a boost to my career as an independent researcher.

The UN-BIASED project aims at developing an innovative Scientific Modelling paradigm capable of mitigating potential cognitive biases affecting the modelling process in engineering applications. Nowadays, modelling is mostly a subjective process, strongly driven by the prejudice of the Modeller and anchored to the knowledge of well-determined pre-set physics. In practical applications, this often results into models affected by epistemic uncertainty. Data-driven techniques open the path for the construction of computerized models that are able to learn the physics underlying a complex system from the available data alone, requiring little, if not at all, subjectivity. Interestingly, these tools are generally used to obtain mere predictions and no credit is usually given to the possibility of translating the learned patterns and relations into interpretable theories and hypotheses. I propose to assess the physics learned by data-driven algorithms in terms of compliance with fundamental principles e.g., laws of thermodynamics, and to test them against a priori subjective hypotheses. This will expose differences between the actual experiment and the Modeller’s understanding of it. This allows for inverting the rationale underlying the classical modelling process, from a theory-to-data deductive assessment to a data-to-theory inductive inference. The ultimate goal is to advance the state-of-the-art by crafting a two-way modelling framework combining the hypotheses-driven and the data-driven approaches, to mitigate the consequences of biased modelling choices and improve the knowledge about complex physical systems. The proposed paradigm is not to be intended as a substitution of the classical Scientific Modelling method, but rather as an extension of it. The project is conceived with aerospace applications in mind, but the proposed methodology is straightforwardly applicable to the modelling of any physical problem of interest for the academy or the industry.

A new space era is coming. Numerous miniaturized probes will soon pervade the solar system for commercial and exploration needs. Constellations made of thousands of spacecraft will soon revolutionize space-based services. In the next future, minor bodies will be the final destination of diverse space missions, since they can give answers to the origin of life and provide resources for the sustainable development of the humanity. However, the state-of-the-art is to control space probes from ground. The need for large teams and specific infrastructures yields extremely expensive operations, that do not scale for small probes. The CASTOR project (Challenging Autonomous Spacecraft through Trajectory Optimization with Robustness) will address the spacecraft operation problem by fostering autonomous guidance and control for future small satellites in the vicinity of minor bodies. In order to reach its aim, the project envisages the definition of an efficient method for robust guidance and control in close proximity and its deployment on appropriate spacecraft-compatible hardware. A concurrent methodology will be exploited to implement the high-performance high-efficiency hardware-software set. Validation in a relevant laboratory environment will be performed exploiting the EXTREMA Simulation Hub, developed within the ERC EXTREMA project at Politecnico di Milano, and its associated facility RAFFAELLO, able to simulate the neighbourhood of an asteroid. CASTOR foresees the involvement of two top-level space agencies, NASA and ESA, and of one of the most important universities in the space research, Politecnico di Milano, that will surely help the project to reach its objectives and maximize its future impact. The outcomes from this project will have a significant impact on the future of space exploration and exploitation, increasing dramatically the potential scientific return and opening the space and its market to new operators, such as small enterprises and universities.

Curbing greenhouse gas emissions is a challenge of the utmost importance for our society future and requires urgent decisions on the implementation of clear-cut climate economic policies. Integrated Assessment Models (IAMs) allow to explore alternative energy scenarios in the next 30-70 years. They are key to support the design of climate policies as they highlight the nexus between climate modelling, social science, and energy systems. However, the use of IAMs to inform climate policies does not come free of controversial aspects. Primarily, the inherent uncertainty of IAMs long-term outputs has created several difficulties for the integration of the modelling insights in the policy design. Modelling outputs diverge across IAMs models quite dramatically when they are asked for example to quantify the uptake of key technologies for the decarbonisation, such as renewables and carbon capture and storage. Uncertainty in IAMs descends from lack of knowledge of the future and from IAMs incomplete representations of the future. Uncertainty cannot be removed, but reduced, understood, and conveyed appropriately to policy makers to avoid that different projections cause delayed actions. This project aims to fill this gap providing a methodology which defines the sources of uncertainty, either due to IAMs inputs or IAMs structure, and quantify their relative importance. The methodology will be embodied in an emulator of IAMs, MANET (the eMulAtor of iNtegratAd assEssmenT models) formulated using machine learning techniques to reproduce IAMs outputs. The project will provide a proof of concept of MANET focusing on the uptake of key decarbonisation technologies. The emulator will provide a simplified version of the IAM outputs as a response surface of the model to any variation of the inputs. MANET will be a flexible tool for policy makers and scientists for a direct comparison of IAMs with no limitation of the solution domain.

EvOoC aims at developing smart mechanically active Organs-on-Chip platforms as clinically relevant in vitro setups to unravel mechanisms underlying tissue regeneration and progression of unmet diseases. A decade ago, developmental engineering (DE) proposed to model in vitro clinically relevant tissues replica by recapitulation of embryonic developmental events. Despite physical forces have recently been suggested as main driver of developmental processes, mechanical conditioning never prevailed as key DE strategy. This is related to a lack in current in vitro mechanobiology setups, mainly based on open loop systems, which disregard the fact that native mechanical environment varies in time as function of tissue state itself. EvOoC vision is to elevate mechanobiology as leading DE approach through a ground-breaking paradigm, named mechanical re-evolution, based on the high-risk/high-gain hypothesis that an iterative manipulation of mechanical forces is necessary to guide in vitro adult tissue development at unprecedented levels. Towards this vision, I will deliver a new method (Evolving OoC, EvOoC), integrating three enabling functions: “Move” - to apply native-inspired mechanical forces to tissues in vitro; “Sense” – to monitor their comprehensive effect on tissue development; “Adapt” – to modulate forces as a function of tissue responses through machine learning (ML)-based algorithms, towards an unsupervised tissue evolution. I will take advantages of two paradigmatic test-cases (cartilage and heart) to showcase the power of mechanical re-evolution in guiding in vitro tissue physiological and pathological states, towards the identification of a brand-new class of mechanotherapeutics for unmet pathologies. By combining principles of microfabrication, DE, mechanobiology and ML, EvOoC will revolutionize basic studies in tissue development and disease modeling, facilitating innovative translational strategies to tackle tissue repair in manifold applications.