Imperial Materials

Céline Bourquin, an undergraduate student in the Department of Materials, shares her experience interning at Safran Aircraft Engines, where she worked in the experimental casting lab for advanced turbine blades.

Safran is a French aerospace company that designs and produces engines for aircraft, helicopters and rockets, and aeronautical and military equipment. My one-month internship took place in the laboratory for experimental casting of advanced turbine blades, on the Safran Aircraft Engines site.

The lab team works on characterising materials such as metals, wax, ceramics or superalloys and analyse their interactions, with the objective to improve their properties under extreme conditions such as high temperatures and high stress. While some of their projects focus on finding the microstructures necessary for ideal mechanical properties, others concentrate on elaborating programmes such as heat treatment methods.

During this internship, I contributed to the sample preparations for the analysis of heat-treated superalloys. Heat treatment is a process that involves heating and cooling materials to achieve desired properties. The process begins by heating the material to a specific temperature, which allows for changes in its microstructure. After reaching the desired temperature, the material is cooled at a controlled rate. In my work, I observed the samples under the microscope to check at which temperatures incipient melts start to appear after cutting, mounting and polishing. The incipient melts indicate chemical segregation, or non-homogeneous composition, which would lead to a decrease in desired mechanical properties and ultimately to reduced engine efficiency. These analyses are necessary to identify which heat-treatment programmes are more adapted to the alloy, and what changes are needed to improve the programmes.

This was my first industrial work experience. I was positively surprised and relieved to discover that what we had learnt in the first year courses was used and referred to regularly in this laboratory. I felt reassured that I wasn’t completely out of place, and that I could perhaps contribute at least a little to their projects. The internship also gave me the opportunity to learn more about large corporations, their processes and culture. In particular, due to the nature of the work carried out in the lab, there was great emphasis placed on safety and security. I was very grateful to the team who made me feel welcome and useful. Everyone was very positive which made going to work enjoyable and I learnt that team dynamics really influence workplace ambiance. Nonetheless, I must admit I was exhausted at the end of each day, but it was a kind of tiredness that comes from doing something new and rewarding. Looking back, I finished each day feeling a little more confident and excited about the path I’d chosen.

 

Read Inside the lab: Internship reflections on experimental casting by Céline Bourquin in full

In our PhD Spotlight series, we speak with research students in the Department of Materials to learn more about their work, inspirations, and experiences during their PhD journey.

This time, we caught up with Vincent Chung, who is exploring how data-driven approaches can help identify synthesizable materials and improve the efficiency of experimental validation. Vincent recently celebrated a major milestone by passing his Phd viva. In this spotlight, he shares what inspired him to pursue this path, talks about his research, and reflects on the challenges and insights gained along the way.

1. What inspired you to study for a PhD?

I have been interested in materials discovery since secondary school, where I was fascinated by books that describe a better future with technologies based on new materials (e.g. Michio Kaku’s “Physics of the Future”). The major reason that I decide to study for a PhD is my Master Project, which was to discover a new material for photocatalytic applications using machine learning. In the end of the project, the identified hypothetical material could not be synthesised, which got me interested in finding ways to identify materials that can be synthesised.

2. How would you explain your research to someone outside the field?

With the data I curated manually, I train machine learning models to predict whether hypothetical inorganic materials can be synthesised using solid-state reactions, which is one of the simplest and oldest synthesis method (sometimes referred to as the shake-and-bake, where you mix the starting chemicals the heat them, kind of like baking a cake!). Afterwards, I train different models to predict the synthesis conditions for the solid-state reactions and compare the use of different training and testing data. The comparison highlights the impact of the training data on the models’ performances.

3. Why did you study this area and why is it important?

There are more and more people who are interested in using data-driven approaches (e.g. large language model is the most recent trend) every year, but not enough attention in whether the data used for training these models is sufficient in terms of quality and variety. This could lead to misinterpretation or misuse of models when they are deployed in real world environment like laboratories.

4. What difference do you hope your research will make?

I hope my research will convince researchers the importance of having better quality and variety of data as opposed to simply implementing the latest and more sophisticated models. New machine learning models are constantly being developed and would sometimes overshadow previous ones, but high-quality data will always be useful.

5. What do you enjoy most about what you do?

I enjoy finding insights and reaching conclusions from analysing data and training models. It is exciting when you learn something new from data that is overlooked by others. It is also fun to look for new ways that the data you curated can be used.

6. What’s something your colleagues would be surprised to learn about you?

I used to play Muay Thai as a hobby and enjoy reading Chinese novels, especially the Xianxia and Wuxia genres.

7. What’s been the biggest challenge so far in your PhD journey?

The biggest challenges I faced during my PhD were problems not related to my research (which would have surprised myself from the past), but several health and family problems. I had to make compromises and changes to my research plan and life during the journey.

8. What advice would you give someone thinking about starting a PhD?

The advice that I would give to someone planning to start a PhD is to really understand the expectation and commitment of a PhD candidate. The best way to do so is to communicate or work with current PhD candidates or postdoctoral fellow (e.g. through the undergraduate research opportunity programme at Imperial, which was what I did), preferably ones whose research aligns with your interest.

 

Vincent’s work highlights the growing role of data-driven methods in materials discovery, and we’re excited to follow the progress of his research in the years ahead.

Read PhD Spotlight: Vincent Chung on unlocking new possibilities in data-driven materials discovery in full