203 citations · 220 across the 5 of their papers we have counts for
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Thermal Transport in SiC with Intrinsic Defects and Mg Transmutation Products
Chen Shen, Yang Su, Maciej P. Polak +5
Silicon carbide is a leading candidate material for advanced nuclear energy systems, but irradiation-induced defects and transmutation products can severely degrade its thermal con…
How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?
Chen Shen, Muhammad Waqas Qureshi, Mark Asta +2
The CALPHAD framework provides a rigorous basis for thermodynamic modeling, yet its ability to predict new chemistries is restricted by limited data and by functional forms that re…
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…