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Bayesian inference applied to grain boundaries

Many materials are so complex, that we can only gain partial understanding of their structures and properties using individual experimental or modeling methods. However, we can gain a more complete picture by combining results from many kinds of different techniques. Bayesian inference provides a rigorous mathematical framework for intelligently combining vastly different types of data such that the whole is greater than the sum of the parts. We are applying this technique to infer the properties of grain boundaries as a function of their crystallographic character.

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Sponsors
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- DOE Office of Science, Office of Basic Energy Sciences

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Selected publications
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R. Aggarwal, M. J. Demkowicz, and Y. Marzouk, “Bayesian inference of substrate properties from film behavior,” Modelling and Simulation in Materials Science and Engineering 23, 015009 (2015)

R. Aggarwal, M. J. Demkowicz, and Y. Marzouk, “Information-Driven Experimental Design in Materials Science,” in Information Science for Materials Discovery and Design, edited by T. Lookman, F. Alexander, and K. Rajan (Springer, 2016)

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People
:

Raghav Aggarwal
Akbar Bagri
Matteo Seita
Sanket Navale