High Throughput Computational Framework of Materials Properties for Extreme Environments

Technical Report · Thu Dec 01 04:00:00 EST 2022

DOI:https://doi.org/10.2172/1907945· OSTI ID:1907945

Liu, Zi-Kui ^[1]; Shang, Shun-Li ^[2]; Beese, Allison M. ^[2]; Wang, Yi ^[2]; Eghtesad, Adnan ^[2]; Shimanek, John ^[2]; Qin, Shipin ^[2]; Lin, Shuang ^[2]; Sun, Hui ^[2]; Bocklund, Brandon ^[2]

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1. Pennsylvania State Univ., University Park, PA (United States); Pennsylvania State University
2. Pennsylvania State Univ., University Park, PA (United States)

This project aims to establish a framework capable of efficiently predicting the properties of structural materials for service in harsh environments over a wide range of temperatures and over long periods of time. The approach is to develop and integrate high throughput first-principles calculations in combination with machine learning (ML) methods, perform high throughput CALPHAD (calculations of phase diagrams) modeling, and carry out finite element method (FEM) simulations. Relevant to high temperature service in fossil power system, nickel-based superalloys such as Inconel 740 and Haynes 282 as well as the associated (Ni-Cr-Co)-Al-C-Fe-Mn-Mo-Nb-Si-Ti system, were investigated. The present framework was built on the concept of phase-based property data, in which properties of individual phases are modeled as a function of internal and external independent variables. This project established an open-source infrastructure with the following capabilities: (1) High throughput implementation of first-principles calculations at finite temperatures and variable compositions using both accurate phonon calculations and the efficient Debye model for thermodynamic properties, elastic constants, diffusion coefficients, vacancy formation, stacking and twin faults, and dislocation mobility; i.e., using the developed code DFTTK; (2) Machine learning capabilities to predict the above properties so that the number of first-principles calculations can be significantly reduced; e.g., using the developed code SIPFENN; (3) High throughput CALPHAD modeling of the above properties as a function of temperature and composition using our unique capability based on ESPEI and PyCalphad; (4) New capabilities to predict the stress-strain behavior of individual phases; and (5) New models for tensile strength prediction in common FEM software with the crystal plasticity finite element simulations (CPFEM).

• Research Organization: Pennsylvania State Univ., University Park, PA (United States)
• Sponsoring Organization: USDOE Office of Fossil Energy (FE)
• DOE Contract Number: FE0031553
• OSTI ID: 1907945
• Report Number(s): DOE-PennState-FE0031553
• Country of Publication: United States
• Language: English

Related Subjects

Ni-based superalloys
36 MATERIALS SCIENCE
97 MATHEMATICS AND COMPUTING
CALPHAD modeling
finite element method
first-principles calculations
harsh environments
machine learning
structural material