**About this Abstract** |

**Meeting** |
**2020 TMS Annual Meeting & Exhibition
** |

**Symposium
** |
**Algorithm Development in Materials Science and Engineering
** |

**Presentation Title** |
Machine-Learned Interatomic Potentials For Alloy Modeling and Phase Diagrams |

**Author(s)** |
Gus LW Hart, Conrad W Rosenbrock, Konstantin Gubaev, Alexander Shapeev, Livia B. Pártay, Noam Bernstein, Gábor Csányi |

**On-Site Speaker (Planned)** |
Gus LW Hart |

**Abstract Scope** |
We demonstrate the power of machine-learned potentials to model multicomponent systems. We compare two different approaches: Moment tensor potentials (MTP) and he Gaussian Approximation Potential (GAP) framework (kernel regression + the Smooth Overlap of Atomic Positions (SOAP) representation). Both types of potentials give excellent accuracy for a wide range of compositions and rival the accuracy of cluster expansion. While both models are perform well, SOAP-GAP excels at transferability as shown by sensible transformation paths between configurations, and MTP allows, due to its lower computational cost, the calculation of compositional phase diagrams. Given the fact that both methods perform as well as cluster expansion would but yield off-lattice models, we expect them to open new avenues in computational materials modeling for alloys. We show compositional phase diagrams, phonon dispersions, new superalloy phases, all predicted using these two machine-learned interatomic potentials for binary and ternary alloys. |

**Proceedings Inclusion?** |
Planned: Supplemental Proceedings volume |