203 citations · 359 across the 6 of their papers we have counts for
6 papers
A practical guide to machine learning interatomic potentials -- Status and future
Ryan Jacobs, Dane Morgan, Siamak Attarian +27
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not exper…
SuperSalt: Equivariant Neural Network Force Fields for Multicomponent Molten Salts System
Chen Shen, Siamak Attarian, Yixuan Zhang +4
Molten salts are crucial for clean energy applications, yet exploring their thermophysical properties across diverse chemical space remains challenging. We present the development…
Best Practices for Fitting Machine Learning Interatomic Potentials for Molten Salts: A Case Study Using NaCl-MgCl2
Siamak Attarian, Chen Shen, Dane Morgan +1
In this work, we developed a compositionally transferable machine learning interatomic potential using atomic cluster expansion potential and PBE-D3 method for (NaCl)1-x(MgCl2)x mo…
Studies of Ni-Cr complexation in FLiBe molten salt using machine learning interatomic potentials
Siamak Attarian, Dane Morgan, Izabela Szlufarska
In nuclear and/or solar applications that involve molten salts, impurities frequently enter the salt as either fission products or via corrosion. Impurities can interact and make c…
Amorphous shear bands in crystalline materials as drivers of plasticity
Xuanxin Hu, Nuohao Liu, Vrishank Jambur +7
Traditionally, the formation of amorphous shear bands (SBs) in crystalline materials has been undesirable, because SBs can nucleate voids and act as precursors to fracture. They al…
Thermophysical properties of FLiBe using moment tensor potentials
Siamak Attarian, Dane Morgan, Izabela Szlufarska
Fluoride salts are prospective materials for applications in some next generation nuclear reactors and their thermophysical properties at various conditions are of interest. Experi…