Title: Machine Learning for Accelerated Material Discovery
Abstract: In this talk, I will briefly discuss my past research and then focus on two projects where we applied state-of-the-art machine learning techniques to accelerate material discovery. First, we used a machine learning model to develop an accurate force field for adsorption. [1] Second, we attempted to use machine learning to predict synthesis conditions for metal-organic frameworks (MOFs). [2] While the machine learning model performed well in the first case, it unfortunately had poor performance in the second task.  However, we were surprised to find that even this subpar machine learning model outperformed human predictions, highlighting the potential utility of machine learning in this context.


[1] Bag, Saientan, Manuel Konrad, Tobias Schloder, Pascal Friederich, and Wolfgang Wenzel. "Fast Generation of Machine Learning-Based Force Fields for Adsorption Energies." Journal of Chemical Theory and Computation 17, no. 11 (2021): 7195-7202.

[2]  Luo, Yi, Saientan Bag, Orysia Zaremba, Adrian Cierpka, Jacopo Andreo, Stefan Wuttke, Pascal Friederich, and Manuel Tsotsalas. "MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning." Angewandte Chemie International Edition 61, no. 19 (2022): e202200242.

Venue:  C V Raman Hall, May 30, 11:30 AM

 

 

 

Narayan Pradhan

Chair- School of Materials Sciences