We aim to leverage computational methods to understand and predict materials behavior in extreme environments, and design exceptional radiation-resistant materials.
Project: The interplay between hydrogen and chemical short-range order in multiple principal element alloys
The concept of tuning CSRO in MPEAs has garnered extensive interest for its potential as an innovative parameter to facilitate enhanced properties. This characteristic is believed to impact mechanical performance, radiation damage resistance, and corrosion resistance in these alloys. However, how hydrogen interacts with the CSRO in MPEAs, and whether CSRO influences the HE resistance, remain unclear. In this project, we aim to bridge this knowledge gap by integrating advanced characterization experiments with atomistic computational modeling to understand the interplay between hydrogen and CSRO on the fundamental mechanical properties.
Status: On-going.
Category: Hydrogen, chemical short-range order, multiple principal element alloy.
Funding: DOE BES.
Project: Radiation damage in Gallium Nitride
The radiation hardness of wide bandgap (WBG) electronics such as gallium nitride transistors is affected by defects in the device structures. In this project, systematic studies of radiation effects utilizing various GaN structures with varying operating conditions are planned, which will provide a foundation for predicting and improving radiation hardness of GaN devices, and shed light on mechanisms governing radiation effects in devices made of other WBG materials, e.g., SiC, Ga2O3, and diamond. Within this scope, we are focusing on studying radiation-induced defects in GaN multilayer structures, with advanced modeling and simulation tools. These results will be validated against the experimental characterization efforts within the team.
Status: On-going.
Category: Radiation damage, GaN, Multi-scale modeling.
Funding: DOD AFOSR MURI.
Project: Thermal transport in ThO2 with defects
This project aligns the mission of TETI (Thermal Energy Transport under Irradiation) EFRC lead by Idaho National Laboratory to provide fundamental understanding in the thermal transport behavior in nuclear fuels. Thermal energy transport under irradiation is directly related to reactor efficiency as well as reactor safety. The aim of TETI is to develop a first principles understanding of electron and phonon transport in advanced nuclear fuels that will provide the necessary tools to enhance thermal transport by tailoring defects and microstructure. As a member of the TETI team, work at Penn State will focus on defects and their impact on the phonon transport and thermal conductivity in ThO2.
Refer to TETI EFRC for more details.
Schematic diagram of phonon-phonon, phonon-defect, phonon-boundary scattering in irradiated ThO2.
Status: On-going.
Category: Thermal transport, nuclear fuels, molecular dynamics.
Funding: DOE BES.
Project: Estimation of low temperature cladding failures during an RIA transient
The goal of this project is to evaluate the likelihood of low temperature failures during a reactivity initiated accident for near-term Accident Tolerant Fuel concepts, including coated cladding, high burnup fuel and higher enrichment pellets. This risk will be evaluated using coupled multiphysics simulations, including neutronics, thermal hydraulics and materials behavior. We will validate the results against existing integral and separate effects results to clearly connect these calculations to data and be able to identity data gaps for TREAT.
Status: On-going.
Category: Fuel performance modeling.
Funding: DOE NEUP.
Project: Fundamental understanding and modeling of molten salt corrosion
Corrosion of structural materials in the environment of molten salts is a major concern to the long-term reliability of molten salt reactors. The concurrent radiation condition complicates the corrosion process. These harsh environments make it extremely non-trivial to setup experiments. Therefore, a key advancement for corrosion control is to build an effective predictive model to describe long time-scale materials corrosion behavior. The construction of a comprehensive model would need to consider the complex surface and bulk processes. This corrosion model, combining atomistic calculations and kinetic Monte Carlo evolution of the system, is the aim of this project, and it is inevitably across multiple scales and multiple materials species.
Schematic diagram of processes for molten salt corrosion modeling
Status: On-going.
Category: Molten salt, corrosion, first-principles, kinetic Monte Carlo.
Funding: NSF CAREER.
Project: Radiation damage behavior in alloys with chemical short- range order
Fe-Ni-Cr alloys have found significant applications in nuclear industries as structural materials due to their good mechanical performance and resistance to corrosion. The composition and chemical short-range order can have a significant impact on the radiation behavior such as defect production, defect mobility, and defect morphology.
Ni-Cr-Co medium entropy alloys have shown favorable properties. The short-range order effect and its coupling with radiation damage behavior is under investigation.
Status: On-going.
Category: Radiation damage, short-range order, molecular dynamics.
Project: Machine-learning enabled classification of material defects in transmission electron microscopy images
Transmission electron microscopy (TEM) as an important and high-resolution (nanometer) imaging technique to characterize materials microstructure, various types of defects, and dynamic nanoscale processes (with in-situ option). One important bottleneck is that the identification and classification of observed objects is highly time-consuming, since the images can only cover very small sections of the material at a time, and there can be many possible defects involved. In recent years, the development of machine learning in image recognition can be used to expedite this process, which has been showcased by many researchers. In this project, we manually collect and label experimental images, and apply convolutional neural network (CNN) based machine learning models to identify line dislocations and radiation induced bubbles with reasonable test accuracy.
Prediction of bubbles from TEM images.
Video. In-situ identification of dislocation line.
Category: Machine learning, image recognition, TEM.
Project: NLP on nuclear materials research — Text-mining and literature-driven discovery
Over recent years, text-mining based on literature provides an enormous opportunity to gain collective insights from the growing availability of papers in a machine-readable format. Natural language processing (NLP) has been actively applied to scientific papers in various research fields such as bibliometrics, chemistry, and biomedicine. The information obtained from NLP can allow researchers to i) efficiently assimilate information being generated and accumulated in their community; ii) inspire new research ideas to push forward the application and understanding; and iii) develop interdisciplinary collaborations for similar problems. To this end, this project calls for NLP applied in the field of nuclear materials research (e.g. abstract or full-text analysis of literature), taking advantage of open-source NLP tools with necessary specialized improvement adapted to nuclear materials. Some examples include literature cluster analysis, categorization, topic modeling, and trend discovery.
Status: On-going; call for undergraduate researchers.
Category: Machine learning, NLP, nuclear materials.