90 citations · 898 across the 54 of their papers we have counts for
33 papers · 1 filter
Physics-Grounded Understanding of Thermal Boundary Conductance between GaO and SiC from a Feedforward Neural Network Potential
Nuohao Liu, Chen Shen, Yue Cao +7
GaO/SiC heterointegration is attractive for ultra-wide-bandgap power electronics, but interfacial thermal boundary conductance (TBC) remains a major heat-removal bottleneck…
Asymmetric Energy Landscapes Control Diffusion in Glasses
Ajay Annamareddy, Bu Wang, Paul M. Voyles +2
While diffusion in crystalline solids is quantitatively understood through defect-mediated atomic hops, no comparable quantitative framework exists for glasses. In these systems, t…
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…
Multi-principal element alloy discovery using directed energy deposition and machine learning
Phalgun Nelaturu, Jason R. Hattrick-Simpers, Michael Moorehead +4
Multi-principal element alloys open large composition spaces for alloy development. The large compositional space necessitates rapid synthesis and characterization to identify prom…