Public Funding for Zeitler Technologies Limited

Matterhorn accelerates resource efficient materials design, by intelligently scheduling experiments with the help of Machine Learning (ML). Born out of UCL's AI Centre, we give materials companies access to world-class academic advances in Machine Learning driven Design of Experiments (DOE). We provide a freely available software that seamlessly integrates with a user-friendly data platform for managing experiments ("Matterhorn STUDIO", http://matterhorn.studio). Historically, developing resource efficient materials is a process driven by theory and intuition. Inevitably, due to the ever increasing complexity of the materials, returns from such theoretical analysis and intuition-based experimentation are diminishing, therefore increasing development costs with fewer successes. Recent advances in DOE methods have played a central role in revolutionising laboratories, for example, pharmaceutical industries can afford to implement a "closed loop" of experimentation, reaching "Level 4" of Matthew Reeve's Digital Maturity Framework. A fully automated "Level 4" laboratory frees the scientists to work on other more complex tasks. Recent reports claim 10 to 100 times faster materials development with a 10 to 100 times reduction of costs (acceleration.utoronto.ca/). Unfortunately, most labs cannot afford the investments required to achieve "closed loop" experimentation. Matterhorn enables these labs to upgrade to Level 1 and 2 instead. We have observed that hiring a single data scientist is often the first step for early adopters, that want to upgrade their labs to higher levels. These data scientists are the main beneficiary of Matterhorn, since it solves their problem of deciding which algorithm to use and how to securely manage the data. Moving forward, we would like to share Matterhorn with the wider materials community in their efforts towards resource efficient materials. This grant will help make that possible by helping us develop our platform where materials scientist can make their first steps in machine learning. Matterhorn will provide dedicated models for a wide set of material fields such as bioengineering or solid-state chemistry. With the help of an easy to use platform and accessible tutorials, we hope to inspire and support the next generation of material scientist to develop their skills in data-driven materials discovery and advance progress in the UK and global materials ecosystem as a whole, while providing a dedicated platform to take care of their data-management, experimentation schedule and team collaboration.