Predicting and Controlling Corrosion

Project Overview

Corrosion incurs significant replacement costs in the transportation, utility, manufacturing, and infrastructure sectors. It also underpins several of LLNL's core missions in stockpile stewardship, defense, and energy security. Whereas most corrosion and aging models are empirically parameterized to describe well-defined conditions late in the reaction, the factors that determine the early stages of corrosion (during which mitigation could be most impactful) are poorly understood. This project addressed the critical need for new approaches to predict the kinetics of corrosion initiation based on firm physical and chemical understanding. The activities encompassed degradation of relevant metals in both hydrogen-rich and environmental corrosion scenarios. The team integrated state-of-the-art multiscale simulation, in situ characterization, and data science within three technical thrusts: hydriding of Ti alloys; aqueous corrosion of Al and Ni-Cr alloys; and degradation of additively manufactured 316L stainless steel. In each case, novel capabilities were developed to identify and track the impacts of key atomistic, compositional, and microstructural features on the metal systems. For hydriding, protocols were developed to tightly integrate multiscale models, advanced multimodal characterization, and machine learning to determine how hydrogen interacts with native passivating surface oxides and nucleates new undesired phases, shedding new light on the critical role of grain boundaries, interfaces, and atomically disordered regions. For aqueous corrosion, the project demonstrated methods to predict dissolution rates of metal surfaces in corrosive solutions to measure and understand microstructural and grain-orientation effects on corrosion susceptibility, and to investigate competing growth and dissolution kinetics of surface oxides. For additively manufactured metals, analysis using state-of-the-art microscopy techniques revealed the role of specific heterogeneities invoked during laser processing, including cellular structure, dislocations, and precipitates on corrosion susceptibility. In addition to new capabilities and understanding, the project provided an avenue for workforce development, as well as key partnerships with stakeholders in corrosion science.

Mission Impact

Corrosion is a crosscutting issue with broad applicability to a variety of national priorities. The foundational tools proposed here are general and can therefore be easily adapted to support multiple internal and external sponsor needs. The project addresses critical LLNL needs by filling the primary gap in stockpile stewardship aging models, with additional relevance to materials for defense and counterterrorism, energy and grid security, and infrastructure. The capabilities and predictive understanding provided within the project also represent a first step toward developing more reliable codes and certification standards. More broadly, the ability to predict the factors that control the onset of corrosion can enable entirely new co-design paradigms for controlling kinetics from the very beginning, with far-reaching economic and safety implications. To support these objectives and other future mission priorities, the project established collaborations with seven universities and three industrial partners. The project also sponsored several postdoctoral hires and staff conversions, representing a substantial investment in a newly trained workforce in corrosion and aging. The project formed the basis of a new emphasis on degradation science within various LLNL directorates and strategic plans. Scientific activities were also spun out to other projects within the Department of Energy with targeted interests in corrosion and degradation science, including the Office of Energy Efficiency and Renewable Energy and the National Nuclear Security Administration.

Publications, Presentations, and Patents

Weitzner, S., et al. 2021. "Beyond Thermodynamics: Assessing the Dynamical Softness of Hydrated Ions from First Principles." Journal of Physical Chemistry Letters 12:11980 (2021). LLNL-JRNL-822413.

Hsu, T., et al. 2022. "Efficient and Interpretable Graph Network Representation for Angle-Dependent Properties Applied to Optical Spectroscopy." Computational Materials 8:151. LLNL-JRNL-826919.

Shi, R., et al. 2021. "Recent Advances in the Design of Novel Beta-Titanium Alloys Using Integrated Theory, Computer Simulation and Advanced Characterization." Advanced Engineering Materials 23:2100152. LLNL-JRNL-819469.

Gupta, V. K., et al. 2021. "Using DFTB to Model Photocatalytic Anatase-Rutile TiO2 Nanocrystalline Interfaces and their Band Gap Alignment." Journal of Chemical Theory and Computation 17:5239. LLNL-JRNL-821652.

Goldman, N., et al. 2021. "Semi-Automated Creation of Density Functional Tight Binding Models through Leveraging Chebyshev Polynomial-Based Force Fields." Journal of Chemical Theory and Computation 17:4435. LLNL-JRNL-819272.

Zhu, Y., et al. 2021. "Corrosion of Rebar in Concrete. Part II: Literature Survey and Analysis of Existing Data." Corrosion Science 185:109439. LLNL-JRNL-814988.

Zhu, Y., et al. 2021. "Corrosion of Rebar in Concrete. Part III. Artificial Neural Network Analysis of Chloride Threshold Data." Corrosion Science 185:109438. LLNL-JRNL-814989.

Macdonald, D. D. et al. 2021. "Corrosion of Rebar in Concrete. Part IV. On the Theoretical Basis of the Chloride Threshold." Corrosion Science 185:109460. LLNL-JRNL-821823.

Zhu, Y. and M. L. Free. 2021. "Chapter 1: Introduction to Surfactants" in Surfactants in Precision Cleaning pp. 1-53. LLNL-BOOK-816633.

Free, M. L. and Y. Zhu. 2021. "Chapter 3: The Use of Surfactants in Enhanced Particle Removal During Cleaning" in Surfactants in Precision Cleaning. pp. 129-159. LLNL-BOOK-816633.

Van den Bergh, W. S., et al. 2022. "Amorphization of Pseudocapacitive T−Nb2O5 Accelerates Lithium Diffusivity as Revealed Using Tunable Isomorphic Architectures." Batteries & Supercaps 5:e202200056. LLNL-JRNL-831801.

Chapman, J. et al. 2022. "Efficient and Universal Characterization of Atomic Structures through a Topological Graph Order Parameter." Computational Materials 8:37. LLNL-JRNL-823450.

Zhu, Y. et al. 2022. "Hydriding of Titanium: Recent Trends and Perspectives in Advanced Characterization and Multiscale Modeling." Current Opinion in Solid State and Materials Science. In press. LLNL-JRNL-823435.

Voisin, T. et al. 2021. "New Insights on Cellular Structures Strengthening Mechanisms and Thermal Stability of an Austenitic Stainless Steel Fabricated by Laser Powder-Bed-Fusion." Acta Materialia 203:116476. LLNL-JRNL-809833.

Park, J. et al. 2021. "Hydrogen Uptake and its Influence in Selective Laser Melted Austenitic Stainless Steel: A Nanoindentation Study." Scripta Materialia 194:113718. LLNL-JRNL-809832.

Voisin, T. et al. 2022. "Pitting Corrosion in 316L Stainless Steel Fabricated by Laser Powder Bed Fusion Additive Manufacturing: A Review and Perspective." JOM 74:1668. LLNL-PROC-825696.

Tian, M. et al. 2022. "Discovering the Nanoscale Origins of Localized Corrosion in Additive Manufactured Stainless Steel 316L by Liquid Cell Transmission Electron Microscope." Corrosion Science 208:110659. LLNL-JRNL-840515.

Pasquini, L. et al. 2022. "Magnesium- and Intermetallic Alloys-Based Hydrides for Energy Storage: Modelling, Synthesis and Properties." Progress in Energy 4:032007. LLNL-JRNL-833949.

Sen-Britain, S. et al. 2021. "Transformations of Ti-5Al-5V-5Cr-3Mo Powders due to Recycling in Laser Powder Bed Fusion: A Surface Analytical Approach." Applied Surface Science 564:150433. LLNL-JRNL-816728.

Antonov, S. et al. 2021. "Nucleation and Growth of α Phase in a Metastable β-Titanium Ti-5Al-5Mo-5V-3Cr Alloy: Influence from the Nano-Scale, Ordered-Orthorhombic O'' Phase and α Compositional Evolution." Scripta Materialia 194:113672. LLNL-JRNL-812611.

Ping, Y. and T. J. Smart. 2021. "Computational Design of Quantum Defects in Two-Dimensional Materials." Nature Computational Science 1:646. LLNL-JRNL-820403.

Smart, T. J. et al. 2021. "Intersystem Crossing and Exciton-Defect Coupling of Spin Defects in Hexagonal Boron Nitride." Computational Materials 7:59. LLNL-JRNL-820110.

Smart, T. J. et al. 2021. "The Doping Bottleneck in Hematite: Multipole Clustering by Small Polarons." Chemistry of Materials 33:4390. LLNL-JRNL-820109.

Smart, T. J., et al. 2021. "The Critical Role of Synthesis Conditions on Small Polaron Carrier Concentrations in Hematite—A First-Principles Study." Journal of Applied Physics 130:245705. LLNL-JRNL-840237.

Smart, T. J., et al. 2021. "Enhancing Defect Tolerance with Ligands at the Surface of Lead Halide Perovskites." Journal of Physical Chemistry Letters 12:6299.

Li, K., et al. 2022. "Carbon Trimer as a 2 eV Single-Photon Emitter Candidate in Hexagonal Boron Nitride: A First-Principles Study." Physical Review Materials 6:L042201. LLNL-JRNL-840246.

Bertsch, K. M., et al. 2021. "Hydrogen Embrittlement of Additively Manufactured Austenitic Stainless Steel 316L." Corrosion Science 192:109790. LLNL-JRNL-821384.

Heo, T. "Multiscale Modeling of Metal-Hydrogen Interactions: A Case Study of Hydride Formation in Titanium." TMS Annual Meeting. 2022. LLNL-PRES-832519, LLNL-VIDEO-832761.

Zhu, Y. "Grain Size Effect on Hydrogen Trapping, Hydrogen/Hydride Distribution and Associated Strain Mapping in Ultra-Pure Ti Metal." ECS Spring Meeting, May 29-June 2, 2022. Vancouver, B.C. LLNL-PRES-830320.

Heo, T. "Research Overview - Hydrogen and Catalysis." LLNL-KIST MOU Workshop. 2022. LLNL-PRES-838847.

Heo, T. "Multiscale Modeling of Metal-Hydrogen Interactions: A Case Study of Hydride Formation in Titanium." CWRU DMSE Seminar. 2022. LLNL-PRES-840166.

Zhu, Y. "Introduction to Hydrogen Permeation in TiOx." LLNL-MPIE Collaboration Initiative. 2021. LLNL-PRES-821592.

Zhang, Y. "Effect of Rapid-solidification Induced Cellular Structures on the Corrosion Behavior of an Additively Manufactured 316L Stainless Steel Investigated by High-speed AFM." MRS Fall Meeting. 2021. LLNL-ABS-824056

Hsu, T. "Efficient, Interpretable Atomistic Graph Representation for Angle-Dependent Spectroscopic Prediction." NSF Workshop on AI for 4D Materials Discovery. Virtual. September 2021. LLNL-PRES-828241.

Hsu, T. "Efficient, Interpretable Atomistic Graph Neural Network Representation for Angle-Dependent Properties and its Application to Optical Spectroscopy Prediction." TMS World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022). Pittsburg, PA. April 2022. LLNL-PRES-828241.

Goldman, N. "Development of Multi-scale Computational Methodologies for Reactive Materials." TSRC Workshop. 2022. LLNL-PRES-835552.

Chapman, J. "Understanding Hydrogen Diffusivity in Amorphous Titania: A Combined Density Functional Theory, Machine Learning, and Graph Theory Study." 1st Corrosion and Materials Degradation Web Conference., Virtual. July 5-7, 2022. LLNL-PRES-822271.

Goldman, N."Creating Semi-Automated DFTB Interaction Potentials Through Machine Learned Force Fields."DFTB-ML-2020 Workshop. 2020. LLNL-PRES-815274.

Keilbart, N. "Multiscale Understanding of Local Structure-Dependent Hydrogen Incorporation in TiO2." Gordon Aqueous Corrosion Research Conference. New London, NH. July 10-15, 2022. LLNL-POST-837301.

Keilbart, N. "Multiscale Understanding of Local Structure-Dependent . Hydrogen Incorporation in TiO2." ACS Fall Meeting. Vancouver, B.C. August 21-25, 2022. LLNL-POST-837301.

Keilbart, N. "Multiscale Understanding Hydrogen Incorporation in TiO2." ECS Spring Meeting.Vancouver, B.C. May 29 - June 2, 2022. LLNL-PRES-835739.

Pham, T. A. "Integrated Theory-Experiment Capabilities for Predicting Materials Corrosion." LLNL-Lockheed Martin Workshop. 2022. LLNL-PRES-838606.

Pham, T. A. "Integrated theory-experiment capabilities for predicting materials corrosion." LLNL-Lockheed Martin Workshop.2022. LLNL-PRES-838606.

Pham, T. A. "Understanding and Predicting Early-Stage Corrosion." CWRU Data Science Workshop. 2021. LLNL-PRES-840573.

Voisin, T. "Local Corrosion Behaviors of AM 316L Stainless Steels." ICAM 2021 ASTM International Conference on AM. November 1-5, 2021. LLNL-PRES-826261.

Voisin, T. "High Performance AM Stainless Steel 316L in Corrosive Environment." TMS21 Meeting. Philadelphia, PA. Sept. 18-21, 2021. LLNL-PRES-820317.

Voisin, T. "New Insights on Cellular Structures Strengthening Mechanisms and Thermal Stability of L-PBF Stainless Steel 316L." TMS21 Meeting. Philadelphia, PA. Sept. 18-21, 2021. LLNL-PRES-820374.

Voisin, T. "Local Corrosion Behaviors of AM 316L Stainless Steels." TMS22 Meeting. Philadelphia, PA. Sept. 18-21, 2021. LLNL-PRES-832115.

Bertsch, K. "Hydrogen Trapping at Microstructural Features in Additively-manufactured SS316L Evaluated via Combined SIMS and Electron Microscopy." TMS22 Meeting. Anaheim, CA. Feb. 27-March 3, 2022. LLNL-PRES-832102.

Sharma, S. "Understanding Kinetics of Metal Dissolution from Integrated Multiscale Simulations and Experiments." 241st ECS Meeting. Vancouver, B.C. May 29- June 2, 2022. LLNL-ABS-830307.

Wood, B. C. "Advanced Computational Modeling of Metal Hydrides for Vehicular and Stationary Hydrogen Storage Applications." Materials Research Society (MRS) Fall Meeting. Boston, MA. 2021. LLNL-ABS-823782.

Wood, B. C. "Corrosion and Degradation Modeling at LLNL." Army Research Presentation. 2020. LLNL-PRES-815094.

Wood, B. C. "Corrosion and Degradation Modeling at LLNL." NIMS-LLNL Workshop. 2020. LLNL-PRES-815094.

Xiao, P. "Oxide Scale Evolution on Binary Alloys: Kinetics from First Principles." ECS PRiME 2020. October 4-9, 2020. LLNL-PRES-814593.

Wood, B. C. "Understanding Corrosion Initiation from Physics-based Multiscale Modeling." NIST Materials Genome Initiative Virtual Seminar. 2020. LLNL-PRES-814388.

Wood, B. C. "Predicting Corrosion: A New LLNL Strategic Initiative." Office of Naval Research Program Review. 2020. LLNL-PRES-813650.

Wood, B. C. "Multiscale Modeling of the Microstructural Dependence of Degradation Initiation in Al and Ti." MS&T Meeting. 2022. LLNL-ABS-832917 probably https://www.matscitech.org/MST/MST22.

Wood, B. C. "Modeling and Simulation of Hydrogen Storage Materials & Carriers." International Energy Agency TCP Task 40 Programme Meeting. 2022. Madrid, Spain. September 21, 2022. LLNL-PRES-840005.