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FrOGS: Discrete Neural Sampler for Independent Alloy Configurations Across Chemical Conditions
Kyucheol Min, Elyssa Hofgard, Tess Smidt
Predicting the thermodynamic properties of an alloy requires sampling its configurations across many chemical conditions and recovering free energies on a common absolute scale. Ma…
PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials
Teddy Koker, Abhijeet Gangan, Mit Kotak +2
Many materials properties depend on higher-order derivatives of the potential energy surface, yet machine learned interatomic potentials (MLIPs) trained with a standard loss on ene…
Quantifying chemical short-range order in metallic alloys
Killian Sheriff, Yifan Cao, Tess Smidt +1
Metallic alloys often form phases - known as solid solutions - in which chemical elements are spread out on the same crystal lattice in an almost random manner. The tendency of cer…
Machine Learning on Neutron and X-Ray Scattering
Zhantao Chen, Nina Andrejevic, Nathan Drucker +8
Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials' structural and dynamical properties with high precision. T…
A new spin-anisotropic harmonic honeycomb iridate
Kimberly A. Modic, Tess E. Smidt, Itamar Kimchi +14
The physics of Mott insulators underlies diverse phenomena ranging from high temperature superconductivity to exotic magnetism. Although both the electron spin and the structure of…