179 citations · 344 across the 7 of their papers we have counts for
8 papers
Optimal Routes to Ultrafast Polarization Reversal in Ferroelectric LiNbO3
R. Tanner Hardy, Conrad Rosenbrock, Gus L. W. Hart +1
We use the frozen phonon method to calculate the anharmonic potential energy surface and to model the ultrafast ferroelectric polarization reversal in LiNbO3 driven by intense puls…
Machine-learned Interatomic Potentials for Alloys and Alloy Phase Diagrams
Conrad W. Rosenbrock, Konstantin Gubaev, Alexander V. Shapeev +4
We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions. We compare two different approaches. Moment tens…
Machine-learned multi-system surrogate models for materials prediction
Chandramouli Nyshadham, Matthias Rupp, Brayden Bekker +6
Surrogate machine-learning models are transforming computational materials science by predicting properties of materials with the accuracy of ab initio methods at a fraction of the…
Structural Characterization of Grain Boundaries and Machine Learning of Grain Boundary Energy and Mobility
Conrad W. Rosenbrock, Jonathan L. Priedeman, Gus L. W. Hart +1
Recent advances in the numerical representation of materials opened the way for successful machine learning of grain boundary (GB) energies and the classification of GB mobility an…
A Practical Python API for Querying AFLOWLIB
Conred W. Rosenbrock
Large databases such as aflowlib.org provide valuable data sources for discovering material trends through machine learning. Although a REST API and query language are available, t…
Discovering the Building Blocks of Atomic Systems using Machine Learning
Conrad W. Rosenbrock, Eric R. Homer, Gábor Csányi +1
Machine learning has proven to be a valuable tool to approximate functions in high-dimensional spaces. Unfortunately, analysis of these models to extract the relevant physics is ne…
The Robustness of Cluster Expansion: Assessing the Roles of Relaxation and Numerical Error
Andrew H. Nguyen, Conrad W. Rosenbrock, C. Shane Reese +1
Cluster expansion (CE) is effective in modeling the stability of metallic alloys, but sometimes cluster expansions fail. Failures are often attributed to atomic relaxation in the D…
Numerical Algorithm for Pólya Enumeration Theorem
Conrad W. Rosenbrock, Wiley S. Morgan, Gus L. W. Hart +2
Although the Pólya enumeration theorem has been used extensively for decades, an optimized, purely numerical algorithm for calculating its coefficients is not readily available. We…