A resume mix-up landed a material scientist into a data scientist role. This happy accident led him to discover how data drives the design and operation of materials and experiments.

At its core, nuclear energy is all about generating electricity by converting water to steam to drive a turbine - very, very powerfully. But there’s a ton of data management that goes into its successful operation.

Our guest this week is David Pham, a senior materials engineer at TerraPower, who talks about his current projects Natrium, a Gen IV nuclear reactor, and ARMI, their in-house materials database solution. Join us as we also discuss:

🔹How data from materials, mechanical properties, chemical operations dictates the experiments and models in nuclear processes.

🔹 The importance of rigorous record keeping and data management in a sensitive field like nuclear energy.

🔹 The regulatory and auditing challenges in the nuclear industry, and how it departs from traditional sectors like oil and gas.

🔹 How data science will become more and more important for nuclear and material (and any kind of) engineers in the future.

🔹 And much more! 

We hope you enjoy the episode! And as always, let us know what topics you’d like us to cover next! 

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Disclaimer: Any opinions expressed by either guests or hosts in this show are their own, and do not represent the opinions of the companies or organizations for which they are affiliated.

#NuclearPower #DataScience #Materials

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Timestamps: 

• Teaser (0:00 – 0:48 )


• Intro, life updates, and episode highlights (0:48 – 5:18 )


• Introducing David Pham (5:18 – 5:55 )


• Dispelling myths about nuclear energy (5:55 - 7:29 )


• Data management at TerraPower (7:29 - 8:32 )


• Regulations in the nuclear energy sector (8:32 - 10:06 )


• How data dictates design and operations (10:06 - 12:16 )


• Auditing the data and calculations (12:16 - 15:41 )


• Project Natrium and materials selection (15:41 - 24:10 )


• Importance of data science for materials engineers (24:10 - 28:05 )


• Data sources and quality management (28:05 - 41:18 )


• Advice to data scientists and aspiring engineers (41:10 - 52:05 )


• Conclusion (52:05 – 52:27 )


• MSE Academy (52:27 – 53:33 )