1.2k citations · 4.5k across the 34 of their papers we have counts for
5 papers · 1 filter
Reflections on the future of machine learning for materials research
Naohiro Fujinuma, Brian L. DeCost, Jason Hattrick-Simpers +1
Applied machine learning (ML) has rapidly spread throughout the physical sciences; in fact, ML-based data analysis and experimental decision-making has become commonplace. We sugge…
Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping
A. Gilad Kusne, Austin McDannald, Brian DeCost +5
Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, scien…
Recent Advances and Applications of Deep Learning Methods in Materials Science
Kamal Choudhary, Brian DeCost, Chi Chen +10
Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data mo…
Uncertainty Prediction for Machine Learning Models of Material Properties
Francesca Tavazza, Brian De Cost, Kamal Choudhary
Uncertainty quantification in Artificial Intelligence (AI)-based predictions of material properties is of immense importance for the success and reliability of AI applications in m…
Atomistic Line Graph Neural Network for Improved Materials Property Predictions
Kamal Choudhary, Brian DeCost
Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machin…