Scientific Machine Learning

Scientific Machine Learning Mini-Course

The mini-course is a short seminar series (12+ weeks starting Oct 1, 2020) with the goal of cross-pollinating ideas between the various emerging methods at the intersection of physics and machine learning.

Seminar Format: Presenters can use the opportunity to showcase a paper or two with an explicit focus on the methodology and approach. Duration: 40 minutes of methodology + 20 minutes of implementation (code) walk-through + 20 minutes of questions.

Seminar Time: Thursdays 8:30 pm to 10 pm Eastern Time

Webpage for most recent updates

Tentative Speakers and Topics

Overleaf Template for Methodology Walk-Through

Colab Notebook Format for Code Walk-Through


The seminar series is funded by the ARPA-E DIFFERENTIATE program and the Carnegie Mellon Presidential Fellowship.


In the past, I am glad to have been a panelist on the topic, Machine Learning Based Approaches to Accelerate Energy Materials Discovery and Optimization, which led to an energy focus article that summarizes recent progress, challenges, knowledge gaps, and future directions in machine learning research and applications to energy materials discovery and optimization. The core ideas from the panel discussion crystallized in my mind several aspects to carefully consider and be mindful of while applying machine learning methods to limited-data engineering challenges. Often it’s useful to synergistally stack the ML models to the extent possible with the known physics of the problem for effective learning even in low-data regimes.

In a similar vein, my recent work on Deep Learning With Physics-Based Descriptors for Electrolyte Design to Enable Ammonia Synthesis could be of interest to you. The work involves a closed-loop design methodology between computation and experiments (Manthiram group, MIT).