Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning (2019)
Journal Article
Witman, M., Ling, S., Grant, D. M., Walker, G. S., Agarwal, S., Stavila, V., & Allendorf, M. D. (2020). Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning. Journal of Physical Chemistry Letters, 11(1), 40-47. https://doi.org/10.1021/acs.jpclett.9b02971

An open question in the metal hydride community is whether there are simple, physics-based design rules that dictate the thermodynamic properties of these materials across the variety of structures and chemistry they can exhibit. While black box mach... Read More about Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning.