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Artificial Intelligence for Scientific Discovery and Understanding




QI: MIT Quest for Intelligence

Faculty Supervisor:

Marin Soljacic

Faculty email:


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Please send CV and transcript to Samuel Kim at samkim@mit.edu

Project Description

In the last decade, we have witnessed enormous progress in applications of artificial intelligence (AI) for a wide variety of different tasks, including natural language processing, image recognition, self-driving cars, and playing games. We want to investigate how some of these recent AI techniques can be used for scientific discovery. We want to move away from pure data-science approaches since many current deep learning techniques lack interpretability or generalizability (extrapolation outside of the training data set). Rather, we want to more closely integrate physics and machine learning. The main goal of this project is to develop new AI tools to support scientific discovery in broad areas of science and engineering. We will design deep learning architectures that can extract interpretable parameters and derive generalizable laws of nature so that we can facilitate scientific discovery and data analysis. We will also design deep learning architectures to automate design of experiments and optimization of physical systems. Our datasets are generated from a variety of systems in physics and engineering.


Experience with Python and machine learning is required. Prefer experience with deep learning in Tensorflow/Pytorch. Exposure to probability, optimization, differential equations, numerical simulation, Bayesian statistics, Julia, and/or symbolic regression is helpful, but not required.