2 citations · 2 across the 2 of their papers we have counts for
4 papers
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…