Conrad W. Rosenbrock

Machine-learned multi-system surrogate models for materials prediction

Chandramouli Nyshadham, Matthias Rupp, Brayden Bekker et al. · npj Computational Materials · 2019 · 159 citations · Full text

Discovering the building blocks of atomic systems using machine learning: application to grain boundaries

Conrad W. Rosenbrock, Eric R. Homer, Gábor Cśanyi et al. · npj Computational Materials · 2017 · 108 citations · Full text

Robustness of the cluster expansion: Assessing the roles of relaxation and numerical error

Andrew H. Nguyen, Conrad W. Rosenbrock, C. Shane Reese et al. · Physical review. B./Physical review. B · 2017 · 47 citations · Full text

Machine-Learning Informed Representations for Grain Boundary Structures

Eric R. Homer, Derek M. Hensley, Conrad W. Rosenbrock et al. · Frontiers in Materials · 2019 · 36 citations · Full text

Geometric Learning, Convolutional Neural Network, Grain Boundaries +14

Quantifying and connecting atomic and crystallographic grain boundary structure using local environment representation and dimensionality reduction techniques

Jonathan L. Priedeman, Conrad W. Rosenbrock, Oliver K. Johnson et al. · Acta Materialia · 2018 · 33 citations · Full text

Materials Science, Crystal Structure, Engineering +14