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Alien Elements

Explore Data Science & Chemistry

Alien Elements is an activity developed by Learning Undefeated to help students explore the field of data science and how it can be applied to the studies of chemistry.

Students will use the physical and chemical properties of alien elements to group and sort them into a table that conveys information about relevant trends and patterns, very similar to Periodic Table.  Students will also be introduced to ideas of how data science can be predictive through the use of certain analytical tools.  In this session, students will model a generative adversarial network (GAN), a type of machine learning that utilizes two neural networks. Students will act as the generators and interact with a discriminator, a computer program that has been trained to identify patterns in a given data set about molecular compounds. Students will use the feedback from the discriminator to propose possible compounds that could exist but are not present in the discriminator’s data set. This is an example of synthetic data generation, a tool that is becoming more and more important in science today.

While GANs have many uses in science, it’s more commonly known for its applications with images and videos, specifically with deepfakes.  Students will view examples of deepfakes and discuss potential dangers of this technology as well as discuss how government agencies, like DARPA and the NSA, are working to detect and label deepfakes to prevent misinformation.

Learning Objectives

  • Standards Alignments & Connections

  • Activity Components


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