Update: Discovery Hackathon on Materials Design with Graph Learning

From April 19 to April 23, 2021, the TAMIDS Scientific Machine Learning Lab organized a one-week-long Hackathon to explore potential applications of graph learning in materials design. Our team consisted of two faculty members from the Department of Materials Science and Engineering, one research scientist from TAMIDS, and 8 graduate students drawn from the Department of Materials Science and Engineering, Department of Chemistry, and the Department of Electrical and Computer Engineering. Together, we were able to modify and deploy the MatErials Graph Network (MEGNet) model, one of the best libraries of graph learning in the material science field, on Grace, the new supercomputer at Texas A&M. 

MEGNet has been widely used to automatically encode thousands of materials into crystal graphs, which are then used to train a graph neural net (GNN) to predict a material’s properties. Our team built off of MEGNet to create a GNN that would take in our own customized graphs, which we think may encode unique physics that may describe long-range interactions better than conventional crystal graphs. Additionally, we wrote programs to encode crystals into graphs using Python and to mine the Material Project database for building up our training data of known materials. We also managed to set up the software environment and train the model on the supercomputer Grace.

“As a graduate student pursuing a Ph.D. in material science, I have been very interested in applying graph theory to my own research”, said student participant Daniel Willhelm. “This Hackathon has provided a crash course on the tools I need, as well as deepened my fundamental understanding of graph learning. This was possible through the close collaboration and discussions with other students and Dr. Tao”.

Participants

Name	Department	Title	Expertise
Jian Tao	TAMIDS/HPRC	Research Scientist	HPC, ML
Raymundo Arroyave	MSEN	Professor	ML, Material Discovery
Xiaofeng Qian	MSEN	Assistant Professor	ML, Material Discovery
Allison Arabelo	MSEN	Ph. D. Student	Material discovery
Cheng-Han Li	Chemistry	Ph. D. Student	ML, Material Discovery
Daniel Sauceda	MSEN	Ph. D. Student	Material Discovery
Guillermo Vazquez Tovar	MSEN	Ph. D. Student	Material Discovery
Brent Vela	MSEN	Ph. D. Student	Material Discovery, ML
Daniel Willhelm	MSEN	Ph. D. Student	Material Discovery
Nathan Wilson	MSEN	Ph. D. Student	Material Discovery
Ziyu Xiang	ECEN	Ph. D. Student	GNN

Sponsorship and Awards

Daily Schedule (April 19 – 23)

9:00AM – 9:30AM	Kickstart the hackathon (on April 19) / Review the progress and discuss the plan for the day (other days)
9:30AM – 12:00PM	Morning session
12:00PM – 1:30PM	Lunch break
1:00PM – 3:30PM	Afternoon session
3:30PM – 4:00PM	Progress Report