Anisotropic physics-regularized interpretable machine learning of microstructure evolution

Melville, Joseph; Yadav, Vishal; Yang, Lin; Krause, Amanda R.; Tonks, Michael R.; Harley, Joel B.

doi:10.1016/j.commatsci.2024.112941

Title: Anisotropic physics-regularized interpretable machine learning of microstructure evolution

Journal Article · 15 March 2024 · Computational Materials Science

DOI: https://doi.org/10.1016/j.commatsci.2024.112941 · OSTI ID:2339500

^[1];

^[2];

^[3];

^[2];

^[2]

Univ. of Florida, Gainesville, FL (United States); Carnegie Mellon University
Univ. of Florida, Gainesville, FL (United States)
Carnegie Mellon Univ., Pittsburgh, PA (United States)

Anisotropic Physics-Regularized Interpretable Machine Learning Microstructure Evolution (APRIMME) is a general-purpose machine learning solution for grain growth simulations. In prior work, PRIMME employed a deep neural network to predict site-specific migration as a function of its neighboring sites to model normal, isotropic, grain growth behavior. Here this work aims to extend this method by incorporating grain boundary misorientation-based grain growth behavior. APRIMME is trained on anisotropic simulations created using the Monte Carlo-Potts (MCP) model. The results of this work are compared statistically using grain radius, number of sides per grain, mean neighborhood misorientations, and the standard deviation of triple junction dihedral angles, and are found to match in most cases. The exceptions are small and seem to be related to two causes: 1) the deterministic model of APRIMME is learning from the stochastic simulations of MCP, which seems to accentuate triple junction behaviors; and, 2) a bias against very small grains is made evident in a quicker decrease in grains than expected at the beginning of an APRIMME simulation. APRIMME is also evaluated for its general ability to capture anisotropic grain growth behavior by first investigating different test case initial conditions, including a circle grain, three grain, and hexagonal grain microstructures.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Carnegie Mellon Univ., Pittsburgh, PA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0023380

OSTI ID:: 2339500

Alternate ID(s):: OSTI ID: 2324674

Journal Information:: Computational Materials Science, Journal Name: Computational Materials Science Vol. 238; ISSN 0927-0256

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (31)

Diffusion generated motion for grain growth in two and three dimensions Elsey, Matt; Esedog¯lu, Selim; Smereka, Peter Journal of Computational Physics, Vol. 228, Issue 21 https://doi.org/10.1016/j.jcp.2009.07.020	journal	November 2009
Triple-junction morphology classification and dihedral angle distribution during 2D grain growth Kim, Hyeonho; Chang, Kunok Results in Physics, Vol. 19 https://doi.org/10.1016/j.rinp.2020.103628	journal	December 2020
A new point of view to determine the simulation temperature for the Potts model simulation of grain growth Zöllner, D. Computational Materials Science, Vol. 86 https://doi.org/10.1016/j.commatsci.2014.01.044	journal	April 2014
Grain microstructural evolution in 2D and 3D polycrystals under triple junction energy and mobility control Zöllner, Dana Computational Materials Science, Vol. 118 https://doi.org/10.1016/j.commatsci.2016.03.031	journal	June 2016
The computer simulation of microstructural evolution Holm, Elizabeth A.; Battaile, Corbett C. JOM, Vol. 53, Issue 9 https://doi.org/10.1007/s11837-001-0063-2	journal	September 2001
Modeling the grain growth kinetics by cellular automaton Raghavan, S.; Sahay, Satyam S. Materials Science and Engineering: A, Vol. 445-446 https://doi.org/10.1016/j.msea.2006.09.023	journal	February 2007
Simulation of normal grain growth by cellular automata Liu, Y.; Baudin, T.; Penelle, R. Scripta Materialia, Vol. 34, Issue 11 https://doi.org/10.1016/1359-6462(96)00055-3	journal	June 1996
Simulation of recrystallization by cellular automata Hesselbarth, H. W.; Göbel, I. R. Acta Metallurgica et Materialia, Vol. 39, Issue 9 https://doi.org/10.1016/0956-7151(91)90183-2	journal	September 1991
Phase-field model for anisotropic grain growth Staublin, Philip; Mukherjee, Arnab; Warren, James A. Acta Materialia, Vol. 237 https://doi.org/10.1016/j.actamat.2022.118169	journal	September 2022
Computer simulation of grain growth—II. Grain size distribution, topology, and local dynamics Srolovitz, D. J.; Anderson, M. P.; Sahni, P. S. Acta Metallurgica, Vol. 32, Issue 5 https://doi.org/10.1016/0001-6160(84)90152-4	journal	May 1984
GrainNN: A neighbor-aware long short-term memory network for predicting microstructure evolution during polycrystalline grain formation Qin, Yigong; DeWitt, Stephen; Radhakrishnan, Balasubramaniam Computational Materials Science, Vol. 218 https://doi.org/10.1016/j.commatsci.2022.111927	journal	February 2023
A generalized field method for multiphase transformations using interface fields Steinbach, I.; Pezzolla, F. Physica D: Nonlinear Phenomena, Vol. 134, Issue 4 https://doi.org/10.1016/S0167-2789(99)00129-3	journal	December 1999
A unified theory of recovery, recrystallization and grain growth, based on the stability and growth of cellular microstructures—I. The basic model Humphreys, F. J. Acta Materialia, Vol. 45, Issue 10 https://doi.org/10.1016/S1359-6454(97)00070-0	journal	October 1997
A more accurate two-dimensional grain growth algorithm Lazar, Emanuel A.; MacPherson, Robert D.; Srolovitz, David J. Acta Materialia, Vol. 58, Issue 2 https://doi.org/10.1016/j.actamat.2009.09.008	journal	January 2010
Comparison of coarsening behaviour in non-conserved and volume-conserved isotropic two-phase grain structures Yadav, Vishal; Moelans, Nele Scripta Materialia, Vol. 146 https://doi.org/10.1016/j.scriptamat.2017.11.023	journal	March 2018
The Potts model Wu, F. Y. Reviews of Modern Physics, Vol. 54, Issue 1 https://doi.org/10.1103/RevModPhys.54.235	journal	January 1982
Observations of unexpected grain boundary migration in SrTiO3 Muralikrishnan, Vivekanand; Liu, He; Yang, Lin Scripta Materialia, Vol. 222 https://doi.org/10.1016/j.scriptamat.2022.115055	journal	January 2023
A more accurate three-dimensional grain growth algorithm Lazar, Emanuel A.; Mason, Jeremy K.; MacPherson, Robert D. Acta Materialia, Vol. 59, Issue 17 https://doi.org/10.1016/j.actamat.2011.07.052	journal	October 2011
Geometric and topological properties of the canonical grain-growth microstructure Mason, Jeremy K.; Lazar, Emanuel A.; MacPherson, Robert D. Physical Review E, Vol. 92, Issue 6 https://doi.org/10.1103/PhysRevE.92.063308	journal	December 2015
Grain boundary mobilities in polycrystals Zhang, Jin; Ludwig, Wolfgang; Zhang, Yubin Acta Materialia, Vol. 191 https://doi.org/10.1016/j.actamat.2020.03.044	journal	June 2020
Computer simulation of grain growth using a continuum field model Fan, D.; Chen, L. -Q. Acta Materialia, Vol. 45, Issue 2 https://doi.org/10.1016/S1359-6454(96)00200-5	journal	February 1997
New phase-field model for polycrystalline systems with anisotropic grain boundary properties Moelans, Nele Materials & Design, Vol. 217 https://doi.org/10.1016/j.matdes.2022.110592	journal	May 2022
Computer simulation of grain growth—I. Kinetics Anderson, M. P.; Srolovitz, D. J.; Grest, G. S. Acta Metallurgica, Vol. 32, Issue 5 https://doi.org/10.1016/0001-6160(84)90151-2	journal	May 1984
Dislocation Models of Crystal Grain Boundaries Read, W. T.; Shockley, W. Physical Review, Vol. 78, Issue 3 https://doi.org/10.1103/PhysRev.78.275	journal	May 1950
A novel physics-regularized interpretable machine learning model for grain growth Yan, Weishi; Melville, Joseph; Yadav, Vishal Materials & Design, Vol. 222 https://doi.org/10.1016/j.matdes.2022.111032	journal	October 2022
Grain boundary velocity and curvature are not correlated in Ni polycrystals Bhattacharya, Aditi; Shen, Yu-Feng; Hefferan, Christopher M. Science, Vol. 374, Issue 6564 https://doi.org/10.1126/science.abj3210	journal	October 2021
On misorientation distribution evolution during anisotropic grain growth Holm, E. Acta Materialia, Vol. 49, Issue 15 https://doi.org/10.1016/S1359-6454(01)00207-5	journal	September 2001
Simulation of grain coarsening in two dimensions by cellular-automaton Geiger, J.; Roósz, A.; Barkóczy, P. Acta Materialia, Vol. 49, Issue 4 https://doi.org/10.1016/S1359-6454(00)00352-9	journal	February 2001
Computer simulation of 2D grain growth using a cellular automata model based on the lowest energy principle He, Yizhu; Ding, Hanlin; Liu, Liufa Materials Science and Engineering: A, Vol. 429, Issue 1-2 https://doi.org/10.1016/j.msea.2006.05.070	journal	August 2006
Influence of grain boundary energy anisotropy on the evolution of grain boundary network structure during 3D anisotropic grain growth Niño, José D.; Johnson, Oliver K. Computational Materials Science, Vol. 217 https://doi.org/10.1016/j.commatsci.2022.111879	journal	January 2023
Quantitative analysis of grain boundary properties in a generalized phase field model for grain growth in anisotropic systems Moelans, N.; Blanpain, B.; Wollants, P. Physical Review B, Vol. 78, Issue 2 https://doi.org/10.1103/PhysRevB.78.024113	journal	July 2008

Similar Records

Elucidating Abnormal Grain Growth in Thermomagnetic Processed Materials with Transfer Learning and Reinforcement Learning

Technical Report · 2024 · OSTI ID:2472531

Krause, Amanda; Tonks, Michael; Harley, Joel

The effect of microstructure on the relationship between grain boundary sliding and slip transmission in high purity aluminum

Journal Article · 2020 · International Journal of Plasticity · OSTI ID:1770876

Linne, Marissa A.; Bieler, Thomas R.; Daly, Samantha

High pressure FAST of nanocrystalline barium titanate

Journal Article · 2016 · Ceramics International · OSTI ID:1341402

Fraga, Martin B.; Delplanque, Jean -Pierre; Yang, Nancy; +2 more

Related Subjects

36
grain growth
microstructure
deep learning
anisotropy
physics regularization

Title: Anisotropic physics-regularized interpretable machine learning of microstructure evolution

Citation Formats

References (31)

Similar Records

Related Subjects