Discovering excitonic superconductors

The discovery of room temperature superconductivity at ambient pressure has the potential to revolutionize numerous industries and drive significant advancements in energy technologies. One of the proposed mechanisms for achieving high-temperature superconductivity involves the formation of excitons through light-induced processes, which can facilitate the pairing of electrons and enable superconducting currents at significantly higher temperatures than traditional phonon-mediated superconductors. While previous research identified transition metal dichalcogenides (TMDs) on carbonaceous layer as promising candidates for observing exciton-mediated superconductivity, experimental confirmation has yet to be observed. In this work, we employ a physics-informed machine learning approach to identify, synthesise and test TMDs on a carbonaceous layer as potential excitonic superconductor candidates.

Our proposed framework involves construction of a machine learning model using computed properties such as band gap and exciton binding energies obtained through automated first-principles calculations. Additionally, we integrate experimentally acquired data and existing information available in materials databases, such as the Materials Project, to enhance the predictive capabilities of the machine learning model. Guided by the TMDs property predictions, we perform synthesis experiments mainly employing thermochemical methods to synthesise TMDs nanoparticles that are subsequently used to coat carbon fibres, or that are coated with carbon themselves. The synthesised materials are then characterized and tested for superconductivity using an optical cryostat, enabling light penetration while cooling the samples to near absolute zero temperatures. The acquired data are subsequently used to refine the machine learning model's predictions, and the cycle of synthesis and characterization continues, leading to uncovering properties of novel TMD materials.

1. PEOPLE: We will train students with skills that are in demand across a spectrum of industries from pharma/biotech to materials, as well as in academia, law and publishing. The enhanced experience they receive - through interactive brainstorming, problem and dragons' den type business sessions - will equip them with confidence in their own abilities and fast-track their leadership skills. 100% Employment of students from the previous CDT in Chemical Synthesis is indicative of the high demand for the skills we provide, but as start-ups and SMEs become increasingly important in the healthcare, medicine and energy sectors, training in IP, entrepreneurship and commercialisation will stimulate our students to explore their own ventures. Automation and machine learning are set to transform the workplace in the next 20 years, and our students will be in the vanguard of those primed to make best use of these shifts in work patterns. Our graduates will have an open and entrepreneurial mindset, willing to seek solution to problems that cross disciplines and require non-traditional approaches to scientific challenges.

2. ECONOMY: Built on the country's long history of scientific ingenuity and creativity, the >£50bn turnover and annual trade surplus of £5 bn makes the British chemical sector one of the most important creators of wealth for the national economy. Our proposal to integrate training in chemical synthesis with emerging fields such as automation/AI/ML will ensure that the UK maintains this position of economic strength in the face of rapidly developing competition. With the field of drug development desperately looking for innovative new directions, we will disseminate, through our proposed extensive industrial stakeholders, smarter and more efficient ways of designing and implementing molecular synthesis using automation, machine learning and virtual reality interfaces. This will give the UK the chance to take a world-leading position in establishing how molecules may be made more rapidly and economically, how compound libraries may be made broader in scope and accessed more efficiently, and how processes may be optimized more quickly and to a higher standard of resilience. Chemical science underpins an estimated 21% of the economy (>£25bn sales; 6 million people), so these innovations have the potential for far-reaching transformative impact.

3. SCIENCE: The science emerging from our CDT will continue to be at the highest academic level by international standards, as judged by an outstanding publication record. Incorporating automation, machine learning, and virtual reality into the standard toolkit of chemical synthesis would initiate a fundamental change in the way molecules are made. Automated methods for making limited classes of molecules (eg peptides) have transformed related biological fields, and extending those techniques to allow a wide range of small molecules to be synthesized will stimulate not only chemistry but also related pivotal fields in the bio- and materials sciences. Synthesis of the molecular starting points is often the rate-limiting step in innovation. Removing this hurdle will allow selection of molecules according to optimal function, not ease of synthesis, and will accelerate scientific progress in many sectors.

4. SOCIETY: Health benefits will emerge from the ability of both academia and the pharmaceutical industry to generate drug targets more rapidly and innovatively. Optimisation of processes opens the way for advances in energy efficiency and resource utilization by avoiding non-renewable, environmentally damaging, or economically volatile feedstocks. The societal impact of automation will extend more widely to the freeing of time to allow more creative working and also recreational pastimes. We thus aim to be among the pioneers in a new automation-led working model, and our students will be trained to think through the broader consequences of automation for society as a whole