most citedReliable and Explainable Machine Learning Methods for Accelerated Material Discovery

1 citations · 1 across the 1 of their papers we have counts for

collaborators

2 papers

physics.comp-ph2019

Predicting Compressive Strength of Consolidated Molecular Solids Using Computer Vision and Deep Learning

Brian Gallagher, Matthew Rever, Donald Loveland +6

We explore the application of computer vision and machine learning (ML) techniques to predict material properties (e.g. compressive strength) based on SEM images. We show that it's…

physics.comp-ph2019★ 1 cited

Reliable and Explainable Machine Learning Methods for Accelerated Material Discovery

Bhavya Kailkhura, Brian Gallagher, Sookyung Kim +2

Material scientists are increasingly adopting the use of machine learning (ML) for making potentially important decisions, such as, discovery, development, optimization, synthesis…