Synthetic polymers are crucial in diverse industries, but current AI-driven design methodologies primarily target linear homopolymers, with limited emphasis on developing customized approaches for copolymers. To address this gap, we introduce a generative model for goal-directed synthetic copolymer design using reinforcement learning. Our model operates in a graph generation environment, facilitating efficient monomer unit design while incorporating domain-specific constraints to ensure high validity rates. In a case study optimizing for Hydrogen Evolution Rate (HER) and synthetic accessibility, our approach showcases the efficacy of reinforcement learning in advancing copolymer design. Furthermore, experimental results underscore the challenges in designing effective scoring functions due to the sparse nature of polymer datasets, emphasizing the need for robust property predictors in polymer design methodologies before integrating more complex generative models into the design process.

Bluetick offers a juridical research platform that enables lawyers to search for cases and jurisprudence efficiently. Most Dutch legal alternatives are still old-fashioned search engines. Bluetick wants to move towards a zero-search-based approach where the system learns about the user's preference and provides them with recommendations. For the user, this means that they have to spend less time searching for cases while still finding all the relevant material. To reach this goal of zero-search, the quality of the recommendations must be high. Therefore improvements in this area are believed to result in a more lucrative product.

This report describes the process of improving the version of the recommender system that was already implemented by Bluetick. The main contributions are evaluated by their effect on the recommender system, and their role in creating a more maintainable, extensible and transparent product.

The first contribution of the team was a refactor of the old system. Using classes and interfaces, the new version makes it easier to do advanced computations on the results, while the interface makes it easier for Bluetick to add additional parts on which recommendations can be based. Secondly, similar to many existing webshops, the new system provides the user with insight into why items are recommended. Lastly, the user is now able to provide the system with relevant law articles at the start, so that the recommender system can give recommendations before the first search.