Timofey Frolov | 17-LW-012
Overview
We developed a computational tool that predicts the atomic structure of materials interfaces and the phase behavior of grain boundaries in several model systems. Grain boundaries can exist in multiple states (or phases) and exhibit first-order transitions between them. Grain boundaries govern many properties of polycrystalline materials, including the majority of engineering materials. While the investigation of the effects of grain-boundary phase transitions on materials properties is currently an active field of research, the atomic-scale structure of these phases remains unknown.
Materials interfaces strongly influence the mechanical, electronic, and optical properties of the materials. Atomistic computer simulations provide details of materials structure at the atomic scale; however, a robust computational tool for describing phase behavior of materials interfaces was needed. We developed such a tool by modifying an existing crystal-structure prediction code (USPEX) to model materials interfaces and demonstrated that, similar to bulk materials, the interfaces of polycrystalline materials exhibit phase behavior: They exist in multiple states and display first-order transitions between them. This capability makes it possible to better understand the way phase behavior of interfaces affects materials properties.
Background and Research Objectives
The properties of structural and functional materials are strongly influenced by the presence of internal interfaces called grain boundaries, which are inherited from materials synthesis and processing. Complex grain microstructures emerge in many manufacturing processes, such as additive manufacturing. Understanding the structure of these interfaces and the ways it influences material properties can be key to optimizing materials to meet the needs of advanced energy applications.
Grain boundaries can exist in multiple states (or phases) and exhibit first-order transitions between them. Even though the experimental investigation of the role of grain-boundary phase transitions on materials properties is currently an active field of research, the atomic-scale structure of these grain-boundary phases remains unknown. Atomistic computer simulations are designed to describe materials structure at the atomic scale. However, their ability to discover phase behavior of materials interfaces is challenged by the fact that no robust computational tool for predicting interface structure exists at present. A standard approach to modeling interfaces involves joining two perfect crystals with different orientations and performing dynamic simulations at elevated temperatures and varying compositions using molecular dynamics or Monte Carlo methods. The simulations employ a constant number of atoms and periodic boundary conditions. Despite decades of extensive research, such modeling efforts provided little evidence of structural transitions at grain boundaries.
Using a relatively simple single-component system, we recently demonstrated that the impediment to observations of such transformations in previous simulations was due to the inadequacy of the simulation methodology, which constrained the number of atoms in the grain boundary. We developed a new simulation methodology that led to the discovery of multiple grain-boundary phases and phase transitions triggered by changes in temperature, concentration of impurities, and defects. We demonstrated that new robust methods of interface-structure prediction are needed to pave the way to modeling transitions of interfaces in complex materials. Our goal was to build a robust computational tool capable of predicting structures of general interfaces in multicomponent systems. Using this tool, we predicted novel structures and proved that interfaces in model systems exhibit phase behavior. The objectives of the proposed work were to adapt the state-of-the-art computational structure-prediction methods to interfaces and to apply this tool to modeling grain boundaries in different materials.
To solve the problem of modeling interface structure, we adapted USPEX, an existing modeling code, to model interfaces. USPEX (short for “Universal Structure Predictor: Evolutionary Xtallography”) is a state-of-the-art, publicly available computational tool developed at Stony Brook University. It uses evolutionary algorithms (Zhu et al. 2018) to predict stable and metastable three-dimensional crystal structures of a specific chemical composition. Prior to its use for this project, USPEX was not designed to predict structures of grain boundaries. The adaptation and implementation of USPEX for our uses involved four steps: (1) We expanded the robustness of USPEX so that it ran on a single processor; (2) developed evolutionary algorithms (which use mechanisms inspired by biological evolution) using a new unsupervised machine-learning approach that automatically identifies all types of grain-boundary phases (Zhu et al. 2018); (3) validated USPEX’s predictive capabilities by using it to model known metallic (e.g., Cu, Ag, Au, and Ni) systems (Frolov et al. 2018a, 2018b); and (4) extended the existing code to model binary metallic systems (such as CuAg) and enable grain-boundary phase transitions due to segregation of the second component.
Impact on Mission
This research supports the DOE objectives of delivering major scientific tools that strengthen the connection between advances in fundamental science and technology innovation. It also advances the science, technology, and engineering competencies that are the foundation of the NNSA mission. Specifically, the development of continuum and mesoscale simulation methods to model phase, grain, and defect microstructure evolution enhances Lawrence Livermore National Laboratory’s core competencies in advanced materials and manufacturing. Our work addressed the need for accurate modeling of interface structure and enables quantitative predictions of equilibrium and kinetic properties of interfaces, which are essential to enhance the predictive capabilities of mesoscale models. This capability also advances the Laboratory's core competencies in high-performance computing, simulation, and data science through the innovations in machine learning and evolutionary search.
Conclusion
We developed a new, robust computational tool that predicts the atomic structure of materials interfaces and the phase behavior of grain boundaries in several model systems. We provided the new implementation of the USPEX modeling code to the USPEX team, which has agreed to release it for public use. We expect that a large community of scientists in physics, chemistry, and materials science will use the tool, impacting interface modeling and computer materials design. In addition, the results of our research in the phase behavior of grain boundaries will generate new knowledge about the fundamental properties of materials interfaces. Delivering these tools will give material scientists new capabilities to develop advanced metallic alloys and ceramics that can operate safely at high temperatures and in aggressive environments, properties that are essential to technologies for cleaner energy conversion and more efficient energy utilization.
References
Frolov, T., et al. 2018a. "Grain Boundary Phases in bcc Metals." Nanoscale 10(17). doi: 10.1039/C8NR00271A. LLNL-JRNL-896314.
——— . 2018b. "Structures and Transitions in bcc Tungsten Grain Boundaries and Their Role in the Absorption of Point Defects." Acta Materialia 159, 123–134. doi: 10.1016/j.actamat.2018.07.051. LLNL-JRNL-934273.
Zhu, Q., et al. 2018. "Predicting Phase Behavior of Grain Boundaries with Evolutionary Search and Machine Learning." Nature Communications 9(1). doi: 10.1038/s41467-018-02937-2. LLNL-JRNL-887780.
Publications and Presentations
Freitas, R., et al. 2018. "Free Energy of Grain Boundary Phases: Atomistic Calculations for 5(310)[001] Grain Boundary in Cu." Physical Review Materials 2(9), 093603. doi: 10.1103/PhysRevMaterials.2.093603. LLNL-JRNL-754459.
Frolov, T., et al. 2017. "Predicting Phase Behavior of Interfaces with Evolutionary Algorithms." TMS Annual Meeting & Exhibition, San Diego, CA, February 2017. LLNL-ABS- 896390.
——— . 2017. "Predicting Phase Behavior of Interfaces with Evolutionary Algorithms." PACRIM-12, Hawaii, HI, May 2017. LLNL-ABS-844566.
——— . 2017. "Predicting Phase Behavior of Interfaces with Evolutionary Algorithms." MS&T2017, Pittsburgh, PA, October 2017. LLNL-ABS-896389.
——— . 2018. "Grain Boundary Phases in bcc Metals." Nanoscale 10(17). doi: 10.1039/C8NR00271A. LLNL-JRNL-896314.
——— . 2018. "Structures and Transitions in bcc Tungsten Grain Boundaries and Their Role in the Absorption of Point Defects." Acta Materialia 159, 123–134. LLNL-JRNL-934273.
——— . 2018. "Atomistic Simulations of Grain Boundary Phase Transitions." Conference on Electronic and Advanced Materials, Orlando, FL, January 2018. LLNL-ABS- 891358.
——— . 2018. "Modeling Transitions at Interfaces." TMS Annual Meeting & Exhibition, Phoenix, AZ, March 2018. LLNL-ABS-886018.
——— . 2018. "Grain Boundary Phases and their Thermal Stability." TMS Annual Meeting & Exhibition, Phoenix, AZ, March 2018. LLNL-ABS-886020.
——— . 2018. "Structures and Transitions in bcc W Grain Boundaries." TMS Annual Meeting & Exhibition, Phoenix, AZ, March 2018. LLNL-ABS-886028.
——— . 2018. "Modeling Transitions at Grain Boundaries." Interfacing Machine Learning and Experimental Methods for Surface Structures (IMPRESS), Graz, Austria, July 2018. LLNL-ABS-946573.
——— . 2018. "Predicting Phase Behavior of Interfaces with Evolutionary Algorithms and Machine Learning." Max-Planck-Institut für Eisenforschung GmbH, Düsseldorf, Germany, July 2018. LLNL-ABS-946587.
——— . 2018. "Predicting Phase Behavior of Interfaces with Evolutionary Algorithms and Machine Learning." University of Munster, Munster, Germany, September 9, 2018. LLNL-ABS-946577.
——— . 2018. "Modeling Transitions at Interfaces." Material Science and Engineering Congress, Darmstadt, Germany, September 2018. LLNL-ABS-946570.
——— . 2018. "Structures and Transitions in Tungsten Grain Boundaries." 26th AACGE Western Section Conference on Crystal Growth & Epitaxy Fallen Leaf Lake, CA, June 2018. LLNL-ABS- 939333.
Nicolas, P. J., et al. 2018. "Segregation-Induced Nanofaceting Transition at an Asymmetric Tilt Grain Boundary in Copper." Phys. Rev. Ltr. 121 (25), 255502. doi: 10.1103/PhysRevLett.121.255502. LLNL-JRNL-936993.
Zhu, Q., et al. 2018. "Predicting Phase Behavior of Grain Boundaries with Evolutionary Search and Machine Learning." Nature Communications 9(1). doi: 10.1038/s41467-018-02937-2. LLNL-JRNL-887780.
