Conrad W. Rosenbrock

Materials Science · Physical Sciences

6h-index401citations28works0.02yr avg
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Research Topics

Machine Learning in Materials Science(11), X-ray Diffraction in Crystallography(7), Hydrogen embrittlement and corrosion behaviors in metals(5), Mineral Processing and Grinding(3), Neural Networks and Applications(2)

Publications28 total

Optimal Routes to Ultrafast Polarization Reversal in Ferroelectric LiNbO3
arXiv (Cornell University)·2023Open Access
Machine-learned interatomic potentials for alloys and alloy phase diagrams
npj Computational Materials·2021· 5 citedOpen Access
Machine-Learning Informed Representations for Grain Boundary Structures
Frontiers in Materials·2019· 36 citedOpen Access
Machine-learned Interatomic Potentials for Alloys and Alloy Phase\n Diagrams
arXiv (Cornell University)·2019· 2 citedOpen Access
Machine-learned multi-system surrogate models for materials prediction
npj Computational Materials·2019· 159 citedOpen Access
Contrasting the Building Blocks of Grain Boundaries using Local vs. Multi-Scale Universal Descriptors
APS·2019
General machine-learning surrogate models for materials prediction
arXiv (Cornell University)·2018Open Access
Quantifying and connecting atomic and crystallographic grain boundary structure using local environment representation and dimensionality reduction techniques
Acta Materialia·2018· 33 citedOpen Access
Structural Characterization of Grain Boundaries and Machine Learning of Grain Boundary Energy and Mobility
arXiv (Cornell University)·2018· 4 citedOpen Access
3D Scattering Transform Representation of Materials: From Molecules to Crystals
Bulletin of the American Physical Society·2018
A Practical Python API for Querying AFLOWLIB
arXiv (Cornell University)·2017Open Access
Discovering the building blocks of atomic systems using machine learning: application to grain boundaries
npj Computational Materials·2017· 108 citedOpen Access
Robustness of the cluster expansion: Assessing the roles of relaxation and numerical error
Physical review. B./Physical review. B·2017· 47 citedOpen Access
Invariant Representations for Robust Materials Prediction
Bulletin of the American Physical Society·2017
Machine Learning to Discover and Optimize Materials
ScholarsArchive (Brigham Young University)·2017· 1 citedOpen Access
Invariant Representations of Materials (Or, Making Machine Learning Work)
Bulletin of the American Physical Society·2016
Numerical Algorithm for Pólya Enumeration Theorem
ACM Journal of Experimental Algorithmics·2016Open Access
The Robustness of Cluster Expansion: Assessing the Role of Relaxation
Bulletin of the American Physical Society·2016
Using Machine Learning to Discover Theories of Everything
Bulletin of the American Physical Society·2015
Ten-fold speed up of DFT: Improving k-point integration
Bulletin of the American Physical Society·2015
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Frequent Co-authors

Gus L. W. Hart(24), Gábor Cśanyi(5), Eric R. Homer(5), Rodney W. Forcade(5), Chandramouli Nyshadham(4), Alexander V. Shapeev(4), Andrew H. Nguyen(4), Derek C. Thomas(3), Jeremy J. Jorgensen(3), Matthew M. Burbidge(3), Bret C. Hess(3), Stefano Curtarolo(3), Matthias Rupp(2), Brayden Bekker(2), Tim Mueller(2), David Wingate(2), Derek M. Hensley(2), Jonathan L. Priedeman(2), Lance J. Nelson(2), Richard Vanfleet(2)