Materials for Nuclear Energy – Morgan

We apply and extended tools from molecular and mesoscale simulation, as well as increasingly from machine learning, to help understand and predict behavior of a range of materials systems under conditions relevant for nuclear applications, with a particular focus on radiation effects. Some of our major results include:

1. Demonstration of the essential role of interstitials in Radiation Induced Segregation (RIS) in austenitic and ferritic steels. In close collaboration with Prof. Todd Allen (then at University of Wisconsin and now at University of Michigan) we helped pioneer the integration of ab initio calculations with rate theory models to determine mechanisms controlling RIS and provide more accurate RIS models³. Such understanding has influenced models that include RIS and supported more quantitative understanding of RIS effects that underly important changes in corrosion and microstructure.
2. Demonstration of the critical role of multiple mechanisms and improved physical and machine learning modeling of Reactor Pressure Vessel (RPV) Steel embrittlement. In close collaboration with Prof. Bob Odette of Santa Barbara we helped demonstrate the importance of multiple poorly understood physical mechanisms governing RPV microstructure evolution under irradiation, including cascade induced nucleation⁴, catalyzed precipitation of Mn-Ni-Si precipitates on Cu⁵, and mobile Cu clusters⁶. We integrated these mechanisms into multicomponent cluster dynamics for the first time and provided some of the most accurate mechanistic models for RPV embrittlement to date. We are also leaders in the development of machine learning based models for embrittlement, providing the first model that includes up to very high fluence (ATR1 irradiation data set) relevant for LWR life extension^2,7. An accessible review of RPV modeling approaches and challenges can be found in Ref.¹.
3. Development of approaches to extract quantitative data from ab initio molecular dynamics (AIMD) of molten salts, particularly for redox reactions. In a series of papers we demonstrated the practicality of using AIMD for many molten salt properties like density and local structure and discovered the critical importance of including corrections for Van der Waals interactions in such modeling⁸. This work included developing an approach for the calculation of standard potentials of dissolution of elements and demonstrating its efficacy in both Chloride and Fluoride salts⁹. We recently demonstrated that ab initio accuracy can be obtained very efficiently for certain classes of machine learning interatomic potentials, providing a practical path to quantum accuracy in prediction of complex properties largely inaccessible to direct AIMD like viscosity and thermal conductivity¹⁰.
4. Development of machine vision methods for automatic analysis of defect data in electron microscopy images. In a series of recent papers we, in close collaboration with Prof. Kevin Field of University of Michigan, have demonstrated that defects induced by irradiation (e.g., cavities and multiple types of dislocation loops) can be automatically extracted from electron microscopy images using deep learning methods with human level accuracy^11,12. These tools were used to examine over 40,000 defects to determine trends in loop diffusion with size during in situ irradiations with unprecedented fidelity¹². These methods are expected to provide qualitative change in our ability to characterize and understand defect behavior, in particular their evolution with time and in complex heterogeneous conditions.
5. Machine learning (ML) in nuclear materials. As can be noted from our work on machine learning models of RPV embrittlement², machine learning interatomic potentials for molten salts¹⁰, and machine learning for defect detection in electron microscopy^11,12, we actively engaged in the emerging field of machine learning for nuclear materials. Our recent review⁷, done in close collaboration with Prof Ju Li of MIT as well as other co-authors, is a valuable resource on this topic.

References

1              D. Morgan, R. Jacobs, G. R. Odette, and T. Yamamoto, Predicting Reactor Pressure Vessel Behaviour for Light-Water Reactors, Innovation News Network (2023).

2            Y. C. Liu, H. Wu, T. Mayeshiba, B. Afflerbach, R. Jacobs, J. Perry, J. George, J. Cordell, J. Y. Xia, H. Yuan, A. Lorenson, H. T. Wu, M. Parker, F. Doshi, A. Politowicz, L. D. Xiao, D. Morgan, P. Wells, N. Almirall, T. Yamamoto, and G. R. Odette, Machine Learning Predictions of Irradiation Embrittlement in Reactor Pressure Vessel Steels, Npj Computational Materials 8 (1) (2022).

3              J. D. Tucker, R. Najafabadi, T. R. Allen, and D. Morgan, Ab Initio-Based Diffusion Theory and Tracer Diffusion in Ni-Cr and Ni-Fe Alloys, Journal of Nuclear Materials 405 (3), 216-234 (2010);         S. Choudhury, L. Barnard, J. D. Tucker, T. R. Allen, B. D. Wirth, M. Asta, and D. Morgan, Ab-Initio Based Modeling of Diffusion in Dilute Bcc Fe-Ni and Fe-Cr Alloys and Implications for Radiation Induced Segregation, Journal of Nuclear Materials 411 (1-3), 1-14 (2011);        K. G. Field, L. M. Barnard, C. M. Parish, J. T. Busby, D. Morgan, and T. R. Allen, Dependence on Grain Boundary Structure of Radiation Induced Segregation in a 9 Wt.% Cr Model Ferritic/Martensitic Steel, Journal of Nuclear Materials 435 (1-3), 172-180 (2013).

4              H. B. Ke, P. Wells, P. D. Edmondson, N. Almirall, L. Barnard, G. R. Odette, and D. Morgan, Thermodynamic and Kinetic Modeling of Mn-Ni-Si Precipitates in Low-Cu Reactor Pressure Vessel Steels, ACTA MATERIALIA 138, 10-26 (2017).

5              M. Mamivand, P. Wells, H. B. Ke, S. P. Shu, G. R. Odette, and D. Morgan, Cumnnisi Precipitate Evolution in Irradiated Reactor Pressure Vessel Steels: Integrated Cluster Dynamics and Experiments, ACTA MATERIALIA 180, 199-217 (2019).

6              S. L. Cui, M. Mamivand, and D. Morgan, Simulation of Cu Precipitation in Fe-Cu Dilute Alloys with Cluster Mobility, MATERIALS & DESIGN 191 (2020).

7              D. Morgan, G. Pilania, A. Couet, B. P. Uberuaga, C. Sun, and J. Li, Machine Learning in Nuclear Materials Research, Current Opinion in Solid State & Materials Science 26 (2) (2022).

8              A. Bengtson, H. O. Nam, S. Saha, R. Sakidja, and D. Morgan, First-Principles Molecular Dynamics Modeling of the Licl-Kcl Molten Salt System, Computational Materials Science 83, 362-370 (2014);               H. O. Nam, A. Bengtson, K. Vortler, S. Saha, R. Sakidja, and D. Morgan, First-Principles Molecular Dynamics Modeling of the Molten Fluoride Salt with Cr Solute, Journal of Nuclear Materials 449 (1-3), 148-157 (2014).

9              H. O. Nam and D. Morgan, Redox Condition in Molten Salts and Solute Behavior: A First-Principles Molecular Dynamics Study, Journal of Nuclear Materials 465, 224-235 (2015).

10           D. M. Siamak Attarian, Izabela Szlufarska, Thermophysical Properties of Flibe Using Moment Tensor Potentials, Journal of Molecular Liquids 368, 120803 (2022).

11           W. Li, K. G. Field, and D. Morgan, Automated Defect Analysis in Electron Microscopic Images, Npj Computational Materials 4 (2018);          M. R. Shen, G. Z. Li, D. X. Wu, Y. H. Liu, J. R. C. Greaves, W. Hao, N. J. Krakauer, L. Krudy, J. Perez, V. Sreenivasan, B. Sanchez, O. Torres-Velazquez, W. Li, K. G. Field, and D. Morgan, Multi Defect Detection and Analysis of Electron Microscopy Images with Deep Learning, Computational Materials Science 199 (2021); R. Jacobs, M. R. Shen, Y. H. Liu, W. Hao, X. S. Li, R. Y. He, J. R. C. Greaves, D. L. Wang, Z. M. Xie, Z. T. Huang, C. Wang, K. G. Field, and D. Morgan, Performance and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs, Cell Reports Physical Science 3 (5) (2022).

12           M. R. Shen, G. Z. Li, D. X. Wu, Y. Yaguchi, J. C. Haley, K. G. Field, and D. Morgan, A Deep Learning Based Automatic Defect Analysis Framework for in-Situ Tem Ion Irradiations, Computational Materials Science 197 (2021).