Building a vivid community is the primary ambition of the Molecular and Material Design Technology hub. Coordination and support actions for strengthening connections, visibility, and creating and nurturing the community with events and get togethers organized at a lively physical meeting place where MMD fellows, project leaders link up to stakeholders in Amsterdam and beyond.

To this end there will be collaborations with eg. Sustainalab, where Faculty of Science researchers can meet and interact at a fixed moment in the week, located in Matrix One in close proximity of highly specialized labs at the various Faculty of Science institutes (IOP, HIMS, SILS, IBED). The MMD hub will seek strategic collaborations with major tech, chemical and material companies worldwide, and create public–private partnerships using e.g. the ICAI model as a best practice example, and will also build strong relationships within the Amsterdam Ecosystem both in education (e.g. with HvA, VU, UMC Amsterdam and AMOLF), in co-creation with companies located in the Amsterdam region, and in national and international consortia.

The MMD Impulse call aims to establish and further strengthen emerging collaborations that combine theoretical, experimental, analytical/instrumentation and computational expertise in the field of molecular and material design. Small grants from 50-150 k€ each, directed towards combinations of tenured scholars, will help establish already emerging collaborations. The focus is on developing new ideas to a next phase, for instance by proof-of-concepts. The proposed research should focus on molecular and material design creating impact in the Faculty of Science themes Health and/or Green. Applications should contribute to accelerating change through initiatives that are not yet happening elsewhere in Faculty of Science and/or are not easily funded via other channels.

Ten projects were selected to receive MMD Grants.

• Enabling growth-adaptive eXpansion of vascular grafts with PAtieNt-specific auxetic material Design (EXPAND), Dr. Gábor Závodszky and Dr. Corentin Coulais 
  Patients with pediatric implants typically have years of somatic growth ahead of them after treatment, which can lead to severe adverse effects. The project aims to develop a method for designing vascular grafts that can adapt to patient-specific somatic growth from childhood to adulthood. For this, they use the unique properties of auxetic materials. 
   
• Digital development of drug nanocarriers for Alzheimer’s disease, Dr. ir. Ioana Ilie, Prof. dr. Joost Reek and Dr. Carlos Fitzsimons
  The development of medicine for Alzheimer's disease is hampered due to the blood-brain barrier. Nanocarriers can be used to get across the barrier, but understanding their properties is challenging. The goal of the project is to create a digital platform for developing drug- and RNA-loaded nanocages targeting Alzheimer's disease. To achieve this, they combine in-vivo experiments with an AI-powered feedback loop.
   
• Predicting and designing olfactory molecules, Prof. dr. Astrid Groot, Prof. dr. Jo Ellis-Monaghan, Dr. Patrick Forré, and Dr. Saer Samanipour 
  Olfactory interactions are central to countless applications in health and the environment, such as pest control. However, researchers can't classify the stimuli of oders into a single physical parameter. The project aims to discover predictive patterns odors leading to controllable olfactory molecule design.
   
• MAP-RoboBioConjugator: Molecular Analytics Platform for the Robotic BioConjugation chemistry self-optimization processes, Prof. dr. ing. Timothy Noël, Dr. Andrea Gargano, and Dr. Bob Pirok 
  Peptide and protein-based therapeutics have revolutionized the way we combat diseases. Their activity can be improved using flow chemistry and robotic solutions. However, current analysis methods often take hours per sample with manual data reporting. This project aims to develop a robotic platform to speed up this fine-tuning process to facilitate the discovery of novel therapies.
   
• Metamaterials designed by AI for safe electric vehicles, Dr. Corentin Coulais and Dr. Jan-Willem van de Meent
  To reduce the enormous footprint of steel production, its usage can be optimized in various applications by using mechanical metamaterials. However, the design cycle of these metamaterials is time-consuming. This project aims to develop deep learning methods to accelerate the computational design of metamaterials, enabling new technology for sustainable steel. They will focus on protective casing for car batteries.
   
• Towards a novel therapeutic approach for Parkinson’s disease: Boosting dopamine production and neuronal survival by activating the Gucy2c receptor, Dr. Lars van der Heide, Prof. dr. Dorus Gadella, Dr. ir. Ioana Ilie, and Prof. dr. Peter Vivian Coveney 
  Doctors usually treat Parkinson’s disease patients with levodopa, which causes side-effects and loses its effectiveness over time. Recently, researchers discovered that targeting the Gucy2c receptor would theoretically have no side-effects and prevent further neurodegeneration. The project aims to develop a ligand targeting Gucy2c by combining rational design with a computational active learning workflow and experimental work.
   
• Optimizing Porous Thin Films for Enhanced Charge Transfer in Solar Energy Conversion Applications, Dr. Sonja Pullen, Dr. Emilia Olsson, and Dr. Bettina Baumgartner
  Solar energy could be used for H2 production and CO2 conversion, but the conversion materials currently have low efficiency. Metal-organic frameworks (MOFs) could overcome these limitations, but their synthesis strategies are often irreproducible. The project aims to develop a theoretical framework which they can use to identify optimal MOF structures and develop automated workflows for their synthesis.
   
• Geometric deep learning for late-stage drug discovery, Dr. ir. Bernd Ensing, Dr. Tati Fernández Ibáñez, and Prof. dr. Jaap van Buul
  Endothelial cell dysfunction is a key factor in cardiovascular diseases, but there are currently no specific medications approved for treatment. An efficient but also challenging approach to optimize drug candidates is to improve their properties by changing chemical groups. The aim of this project is to further develop the latest geometric learning technologies, which can help to predict possibilities to improve properties of drugs.
   
• Designing astronomical Polycyclic Aromatic Hydrocarbons (PAHs), dr. Alessandra Candian, Prof. dr. Wybren Jan Buma, Dr. Daniela Huppenkothen
  This will permit to build precise models with the existing training data and allow us to predict PAH spectra under interstellar conditions. This approach is highly innovative and novel in astrochemistry.
   
• Generative AI for designing novel enzymes, prof. dr. Aalt Jan van Dijk, Dr. Francesco Mutti en dr. Erik Bekkers
  This project aims to revolutionize enzyme design by leveraging cutting-edge technology in generative AI.

By investing in a dedicated MMD compute support team we aim to empower researchers by providing them with the necessary infrastructure, tools, and expertise to efficiently collect, manage, analyze, and visualize data. By leveraging cutting-edge technologies and best practices in data engineering, this team aims to accelerate the research and enable breakthrough discoveries across various domains.

The MMD compute support team will collaborate closely with researchers across various disciplines to understand their data needs and requirements. The team will also closely collaborate with the IT department and UvA Data Science Center to leverage existing infrastructure, resources, and expertise in implementing data engineering solutions. The mechanisms by which they will work is through a voucher system connected to MMD impulse or other projects

In MMD Data we aim for investments that contribute to closing ‘data gaps’ in the envisioned scientific discovery cycles. This could be to facilitate seamless and automated data extraction and sharing from highly specialized experimental infrastructure, relying as much as possible on standardized data formats, facilitating high-throughput data acquisition, and automatically feeding into downstream computational workflows. And vice-versa, to facilitate direct coupling of output from computational workflows to steer experiments, again in an automated fashion. This should lead to fully closed and automated high-throughput discovery cycles, but also to ‘data and workflow’ lakes that store all data, computational models, and workflows in dedicated repositories and facilitate their re-use.

MMD Data will work closely together with MMD Compute. Investments in MMD Data still need to be specified but are will be in required infrastructure (e.g. software or hardware). Initiatives funded in the MMD Impulse program are expected to closely work together in the creation of standardized data types and to define and work towards the MMD data lake. A strong link to the UvA Data Centre is envisioned.