15 citations · 29 across the 9 of their papers we have counts for
9 papers
Morphology and Dynamics of Self-interstitial Clusters in Irradiated Nickel
Ajay Annamareddy, Ibrahim Momohjimoh, Hangyu Li +3
Self-interstitial atom (SIA) clustering is a key early step in radiation damage evolution in face-centered cubic (FCC) metals, governing defect transport, recombination, and the lo…
Accurate Evaluation of Nanoscale Spatiotemporal Dynamics with Electron Correlation Microscopy
Po-Cheng Kung, Ajay Annamareddy, Mark D. Ediger +2
Electron correlation microscopy (ECM) can measure materials dynamics with nanoscale spatial resolution from intensity correlation functions. However, adopting X-ray photon correlat…
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…
Improving Combined Detection and Classification of TEM Defects via Mask-Conditioned Latent Diffusion Augmentation
Ni Li, Nuohao Liu, Ryan Jacobs +5
Analyzing microstructural defects in transmission electron microscopy (TEM) images, particularly in irradiated metal alloys, is often limited by the availability of high-quality, l…
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…
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…