911±¬ÁÏÍø

Aim:

To provide a practical platform that will allow students to integrate automated material synthesis, high-throughput data acquisition from material characterisation and data analysis into a holistic form of materials engineering.ÌýÌý

Synopsis:

This lab-based module will provide students with a holistic learning platform to apply concepts and methodologies of accelerated optimisation in a practical context via data acquisition, analysis and learning from large data sets. The content will be centered on solution-based materials, such as inorganic nanomaterials, liposomes, or proteins assemblies. Optimal nanoparticle design, liposome formulation, protein aggregation, colloidal motility, materials degradation and self-healing are of crucial importance in tomorrow’s solutions for vaccines, disease diagnostics and drug delivery but learned principles will be equally applicable to other industries. In each case, the project will contain routine use of automation elements, application of combinatorial synthetic procedures suitable for automation, materials characterisation via high-throughput screening and the application of advanced statistical tool kits, including machine learning algorithms.ÌýÌý

Learning Outcomes:

• Apply theoretical concepts of high-throughput materials chemistry, fundamentals of data science and automation of materials manufacturing hands-on in a laboratory environment.ÌýÌý

• Apply principles of data science for problem-solving in experimental materials chemistry.ÌýÌý

• Explain and exemplify how learning from big data sets can inform experiments in an iterative feedback loop following design – make – test cycles.ÌýÌý

• Overcome technical challenges in materials fabrication through collaboration in a multidisciplinary team.ÌýÌý

• Produce accurate and defensible oral and written reports in data-driven material optimisation.