Developing reliable machine learning interatomic potential for Fe–Cr–Ni austenitic alloys

Hao, Shiqiang; Singh, Prashant; Smirnov, A. V.; Johnson, Duane D.; Alman, David E.; Gao, Michael C.

doi:10.1063/5.0280935

Developing reliable machine learning interatomic potential for Fe–Cr–Ni austenitic alloys

Journal Article · Fri Aug 22 00:00:00 EDT 2025 · Journal of Applied Physics

DOI:https://doi.org/10.1063/5.0280935· OSTI ID:2586713

^[1]; ^[2]; ^[2]; ^[3]; ^[4]; ^[4]

National Energy Technology Lab. (NETL), Albany, OR (United States); NETL Support Contractor, Albany, OR (United States)
Ames Laboratory (AMES), Ames, IA (United States)
Ames Laboratory (AMES), Ames, IA (United States); Iowa State Univ., Ames, IA (United States)
National Energy Technology Lab. (NETL), Albany, OR (United States)

Gaining atomistic understanding of mechanical behavior of heat-resistant structural materials such as Fe–Cr–Ni-based alloys requires an approach with an accuracy close to density functional theory (DFT) that considers the intrinsic properties of the bulk lattice and important defects such as stacking faults, grain boundaries, and surfaces. This work aims to develop reliable machine learning interatomic potential (MLIAP) at cross-scale for Fe–Cr–Ni ternary alloys with a focus on the face-centered-cubic (fcc) solid solution structure. Leveraging the advantages of moment tensor potentials, which typically necessitate a relatively small training dataset and enable rapid calculations using the large-scale atomic/molecular massively parallel simulator package, we ensure the stability and accuracy of the trained potentials. Important defects such as stacking faults, grain boundaries, and surfaces for wide-range compositions are investigated. Structural, thermal, elastic, and defect properties are determined from molecular dynamics simulations comprising several thousand atoms, generated via canonical Monte Carlo simulations guided by the trained potential. The trained potential allows efficient atomic simulations of structural, thermal, and mechanical properties of fcc Fe–Cr–Ni solid solution alloys as a function of composition and temperature. Therefore, the MLIAP approach represents a major advancement from DFT calculations that are limited to small simulation sizes and traditional molecular dynamics simulations using relatively low accuracy potentials. Furthermore, this work outlines a practical foundation for further investigating the structural evolution and mechanical behavior of austenitic stainless steel and nickel-based alloys in a wide array of applications in extreme environments.

View Accepted Manuscript (DOE)

Research Organization:: Ames Laboratory (AMES), Ames, IA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-07CH11358; AC02-05CH11231

OSTI ID:: 2586713

Journal Information:: Journal of Applied Physics, Journal Name: Journal of Applied Physics Journal Issue: 8 Vol. 138; ISSN 0021-8979; ISSN 1089-7550

Publisher:: AIP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

References (67)

An Fe-Ni-Cr embedded atom method potential for austenitic and ferritic systems: An Fe-Ni-Cr embedded atom method potential for austenitic and ferritic systems Zhou, Xiaowang W.; Foster, Michael E.; Sills, Ryan B. Journal of Computational Chemistry, Vol. 39, Issue 29 https://doi.org/10.1002/jcc.25573	journal	October 2018
Atomistic simulations of grain boundary energies in austenitic steel Ratanaphan, Sutatch; Sarochawikasit, Rajchawit; Kumanuvong, Noppadol Journal of Materials Science, Vol. 54, Issue 7 https://doi.org/10.1007/s10853-018-03297-4	journal	January 2019
The influence of stacking fault energy on creep Burton, B. Acta Metallurgica, Vol. 30, Issue 5 https://doi.org/10.1016/0001-6160(82)90195-X	journal	May 1982
Energetics of point and planar defects in aluminium from first-principles calculations Denteneer, P. J. H.; Soler, J. M. Solid State Communications, Vol. 78, Issue 10 https://doi.org/10.1016/0038-1098(91)90243-O	journal	June 1991
Generalized cluster description of multicomponent systems Sanchez, J. M.; Ducastelle, F.; Gratias, D. Physica A: Statistical Mechanics and its Applications, Vol. 128, Issue 1-2 https://doi.org/10.1016/0378-4371(84)90096-7	journal	November 1984
The alloy theoretic automated toolkit: A user guide van de Walle, A.; Asta, M.; Ceder, G. Calphad, Vol. 26, Issue 4 https://doi.org/10.1016/S0364-5916(02)80006-2	journal	December 2002
Stacking fault energies in austenitic stainless steels Lu, Jun; Hultman, Lars; Holmström, Erik Acta Materialia, Vol. 111 https://doi.org/10.1016/j.actamat.2016.03.042	journal	June 2016
Grain boundary properties of elemental metals Zheng, Hui; Li, Xiang-Guo; Tran, Richard Acta Materialia, Vol. 186 https://doi.org/10.1016/j.actamat.2019.12.030	journal	March 2020
First-principles prediction of incipient order in arbitrary high-entropy alloys: exemplified in Ti0.25CrFeNiAl Singh, Prashant; Smirnov, A. V.; Alam, Aftab Acta Materialia, Vol. 189 https://doi.org/10.1016/j.actamat.2020.02.063	journal	May 2020
Towards stacking fault energy engineering in FCC high entropy alloys Khan, Tasneem Z.; Kirk, Tanner; Vazquez, Guillermo Acta Materialia, Vol. 224 https://doi.org/10.1016/j.actamat.2021.117472	journal	February 2022
Computational Design of High-Entropy Rare Earth Disilicates as Next-Generation Thermal/Environmental Barrier Coatings Hao, Shiqiang; Oleksak, Richard P.; Doğan, Ömer N. Acta Materialia, Vol. 258 https://doi.org/10.1016/j.actamat.2023.119225	journal	October 2023
Modified embedded-atom interatomic potential for Fe-Ni, Cr-Ni and Fe-Cr-Ni systems Wu, Changjun; Lee, Byeong-Joo; Su, Xuping Calphad, Vol. 57 https://doi.org/10.1016/j.calphad.2017.03.007	journal	June 2017
First-principles modeling of energetic and mechanical properties of Ni–Cr, Ni–Re and Cr–Re random alloys Breidi, A.; Fries, S. G.; Palumbo, M. Computational Materials Science, Vol. 117 https://doi.org/10.1016/j.commatsci.2016.01.020	journal	May 2016
Active learning of linearly parametrized interatomic potentials Podryabinkin, Evgeny V.; Shapeev, Alexander V. Computational Materials Science, Vol. 140 https://doi.org/10.1016/j.commatsci.2017.08.031	journal	December 2017
Accelerating high-throughput searches for new alloys with active learning of interatomic potentials Gubaev, Konstantin; Podryabinkin, Evgeny V.; Hart, Gus L. W. Computational Materials Science, Vol. 156 https://doi.org/10.1016/j.commatsci.2018.09.031	journal	January 2019
Moment tensor potentials as a promising tool to study diffusion processes Novoselov, I. I.; Yanilkin, A. V.; Shapeev, A. V. Computational Materials Science, Vol. 164 https://doi.org/10.1016/j.commatsci.2019.03.049	journal	June 2019
Low-cost thermal/environmental barrier coatings: A first-principles study Hao, Shiqiang; Oleksak, Richard P.; Doğan, Ömer N. Computational Materials Science, Vol. 230 https://doi.org/10.1016/j.commatsci.2023.112541	journal	October 2023
Developing interatomic potentials for complex concentrated alloys of Cu, Ti, Ni, Cr, Co, Al, Fe, and Mn Sharifi, Hamid; Wick, Collin D. Computational Materials Science, Vol. 248 https://doi.org/10.1016/j.commatsci.2024.113595	journal	February 2025
Libxc: A library of exchange and correlation functionals for density functional theory Marques, Miguel A. L.; Oliveira, Micael J. T.; Burnus, Tobias Computer Physics Communications, Vol. 183, Issue 10 https://doi.org/10.1016/j.cpc.2012.05.007	journal	October 2012
LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales Thompson, Aidan P.; Aktulga, H. Metin; Berger, Richard Computer Physics Communications, Vol. 271 https://doi.org/10.1016/j.cpc.2021.108171	journal	February 2022
Representing high-dimensional potential-energy surfaces for reactions at surfaces by neural networks Lorenz, Sönke; Groß, Axel; Scheffler, Matthias Chemical Physics Letters, Vol. 395, Issue 4-6 https://doi.org/10.1016/j.cplett.2004.07.076	journal	September 2004
An Fe–Ni–Cr–H interatomic potential and predictions of hydrogen-affected stacking fault energies in austenitic stainless steels Zhou, X. W.; Nowak, C.; Skelton, R. S. International Journal of Hydrogen Energy, Vol. 47, Issue 1 https://doi.org/10.1016/j.ijhydene.2021.09.261	journal	January 2022
Understanding the effect of refractory metal chemistry on the stacking fault energy and mechanical property of Cantor-based multi-principal element alloys Singh, Prashant; Trehern, William; Vela, Brent International Journal of Plasticity, Vol. 179 https://doi.org/10.1016/j.ijplas.2024.104020	journal	August 2024
Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials Thompson, A. P.; Swiler, L. P.; Trott, C. R. Journal of Computational Physics, Vol. 285 https://doi.org/10.1016/j.jcp.2014.12.018	journal	March 2015
Exploring stacking fault energy with the axial Ising model: A renewed approach Tang, Chunguang; Barnett, Matthew R. Scripta Materialia, Vol. 255 https://doi.org/10.1016/j.scriptamat.2024.116400	journal	January 2025
Performance and Cost Assessment of Machine Learning Interatomic Potentials Zuo, Yunxing; Chen, Chi; Li, Xiangguo The Journal of Physical Chemistry A, Vol. 124, Issue 4 https://doi.org/10.1021/acs.jpca.9b08723	journal	October 2019
Theoretical Prediction and Experimental Confirmation of Unusual Ternary Ordered Semiconductor Compounds in Sr–Pb–S System Hao, Shiqiang; Zhao, Li-Dong; Chen, Chang-Qiang Journal of the American Chemical Society, Vol. 136, Issue 4 https://doi.org/10.1021/ja411857y	journal	January 2014
E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials Batzner, Simon; Musaelian, Albert; Sun, Lixin Nature Communications, Vol. 13, Issue 1 https://doi.org/10.1038/s41467-022-29939-5	journal	May 2022
Machine-learned interatomic potentials for alloys and alloy phase diagrams Rosenbrock, Conrad W.; Gubaev, Konstantin; Shapeev, Alexander V. npj Computational Materials, Vol. 7, Issue 1 https://doi.org/10.1038/s41524-020-00477-2	journal	January 2021
Atomic Radii in Crystals Slater, J. C. The Journal of Chemical Physics, Vol. 41, Issue 10 https://doi.org/10.1063/1.1725697	journal	November 1964
Statistical ensembles and molecular dynamics studies of anisotropic solids Ray, John R.; Rahman, A. The Journal of Chemical Physics, Vol. 80, Issue 9 https://doi.org/10.1063/1.447221	journal	May 1984
Extending the accuracy of the SNAP interatomic potential form Wood, Mitchell A.; Thompson, Aidan P. The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5017641	journal	June 2018
Density functional theory calculations of generalized stacking fault energy surfaces for eight face-centered cubic transition metals Su, Yanqing; Xu, Shuozhi; Beyerlein, Irene J. Journal of Applied Physics, Vol. 126, Issue 10 https://doi.org/10.1063/1.5115282	journal	September 2019
The dependence of creep on stacking-fault energy Zhi'an, Yang; Zhongguang, Wang Philosophical Magazine A, Vol. 63, Issue 1 https://doi.org/10.1080/01418619108204594	journal	January 1991
Computation of elastic constants of solids using molecular simulation: comparison of constant volume and constant pressure ensemble methods Clavier, G.; Desbiens, N.; Bourasseau, E. Molecular Simulation, Vol. 43, Issue 17 https://doi.org/10.1080/08927022.2017.1313418	journal	May 2017
Stacking-fault energies in semiconductors from first-principles calculations Denteneer, P. J. H.; Haeringen, W. van Journal of Physics C: Solid State Physics, Vol. 20, Issue 32 https://doi.org/10.1088/0022-3719/20/32/001	journal	November 1987
Effect of vacancy defects on generalized stacking fault energy of fcc metals Asadi, Ebrahim; Zaeem, Mohsen Asle; Moitra, Amitava Journal of Physics: Condensed Matter, Vol. 26, Issue 11 https://doi.org/10.1088/0953-8984/26/11/115404	journal	March 2014
Generalized stacking fault energies for embedded atom FCC metals Zimmerman, Jonathan A.; Gao, Huajian; Abraham, Farid F. Modelling and Simulation in Materials Science and Engineering, Vol. 8, Issue 2 https://doi.org/10.1088/0965-0393/8/2/302	journal	February 2000
The MLIP package: Moment Tensor Potentials with MPI and Active Learning Novikov, Ivan S.; Gubaev, Konstantin; Podryabinkin, Evgeny Machine Learning: Science and Technology https://doi.org/10.1088/2632-2153/abc9fe	journal	November 2020
An Approximate Theory of Order in Alloys Cowley, J. M. Physical Review, Vol. 77, Issue 5 https://doi.org/10.1103/PhysRev.77.669	journal	March 1950
Total-energy and pressure calculations for random substitutional alloys Johnson, D. D.; Nicholson, D. M.; Pinski, F. J. Physical Review B, Vol. 41, Issue 14 https://doi.org/10.1103/PhysRevB.41.9701	journal	May 1990
Projector augmented-wave method Blöchl, P. E. Physical Review B, Vol. 50, Issue 24, p. 17953-17979 https://doi.org/10.1103/PhysRevB.50.17953	journal	December 1994
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set Kresse, G.; Furthmüller, J. Physical Review B, Vol. 54, Issue 16, p. 11169-11186 https://doi.org/10.1103/PhysRevB.54.11169	journal	October 1996
Rapid estimation of elastic constants by molecular dynamics simulation under constant stress Shinoda, Wataru; Shiga, Motoyuki; Mikami, Masuhiro Physical Review B, Vol. 69, Issue 13 https://doi.org/10.1103/PhysRevB.69.134103	journal	April 2004
High-dimensional neural network potentials for metal surfaces: A prototype study for copper Artrith, Nongnuch; Behler, Jörg Physical Review B, Vol. 85, Issue 4 https://doi.org/10.1103/PhysRevB.85.045439	journal	January 2012
Phase stability of ternary fcc and bcc Fe-Cr-Ni alloys Wróbel, Jan S.; Nguyen-Manh, Duc; Lavrentiev, Mikhail Yu. Physical Review B, Vol. 91, Issue 2 https://doi.org/10.1103/PhysRevB.91.024108	journal	January 2015
Atomic short-range order and incipient long-range order in high-entropy alloys Singh, Prashant; Smirnov, A. V.; Johnson, D. D. Physical Review B, Vol. 91, Issue 22 https://doi.org/10.1103/PhysRevB.91.224204	journal	June 2015
Statistical physics of multicomponent alloys using KKR-CPA Khan, Suffian N.; Staunton, J. B.; Stocks, G. M. Physical Review B, Vol. 93, Issue 5 https://doi.org/10.1103/PhysRevB.93.054206	journal	February 2016
Quaternary phase diagrams of spinel Li y □ 1 − y Mn x Ni 2 − x O 4 and composite cathode voltages for concentration gradient materials Hao, Shiqiang; Lu, Zhi; Wolverton, Christopher Physical Review B, Vol. 94, Issue 1 https://doi.org/10.1103/PhysRevB.94.014114	journal	July 2016
Atomic cluster expansion for accurate and transferable interatomic potentials Drautz, Ralf Physical Review B, Vol. 99, Issue 1 https://doi.org/10.1103/PhysRevB.99.014104	journal	January 2019
Calculation of elastic constants of embedded-atom-model potentials in the NVT ensemble Krief, Menahem; Ashkenazy, Yinon Physical Review E, Vol. 103, Issue 6 https://doi.org/10.1103/PhysRevE.103.063307	journal	June 2021
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons Bartók, Albert P.; Payne, Mike C.; Kondor, Risi Physical Review Letters, Vol. 104, Issue 13 https://doi.org/10.1103/PhysRevLett.104.136403	journal	April 2010
Martensitic Transformation in Fe x Mn 80 − x Co 10 Cr 10 High-Entropy Alloy Singh, P.; Picak, S.; Sharma, A. Physical Review Letters, Vol. 127, Issue 11 https://doi.org/10.1103/PhysRevLett.127.115704	journal	September 2021
Mechanism and Prediction of Hydrogen Embrittlement in fcc Stainless Steels and High Entropy Alloys Zhou, Xiao; Tehranchi, Ali; Curtin, William A. Physical Review Letters, Vol. 127, Issue 17 https://doi.org/10.1103/PhysRevLett.127.175501	journal	October 2021
Density-Functional Theory for Random Alloys: Total Energy within the Coherent-Potential Approximation Johnson, D. D.; Nicholson, D. M.; Pinski, F. J. Physical Review Letters, Vol. 56, Issue 19 https://doi.org/10.1103/PhysRevLett.56.2088	journal	May 1986
Generalized Gradient Approximation Made Simple Perdew, John P.; Burke, Kieron; Ernzerhof, Matthias Physical Review Letters, Vol. 77, Issue 18, p. 3865-3868 https://doi.org/10.1103/PhysRevLett.77.3865	journal	October 1996
Dimensionally driven crossover from semimetal to direct semiconductor in layered SbAs Hao, Shiqiang; He, Jiangang; Dravid, Vinayak P. Physical Review Materials, Vol. 3, Issue 10 https://doi.org/10.1103/PhysRevMaterials.3.106002	journal	October 2019
Machine learning for metallurgy I. A neural-network potential for Al-Cu Marchand, Daniel; Jain, Abhinav; Glensk, Albert Physical Review Materials, Vol. 4, Issue 10 https://doi.org/10.1103/PhysRevMaterials.4.103601	journal	October 2020
B2 ordering in body-centered-cubic AlNbTiV refractory high-entropy alloys Körmann, Fritz; Kostiuchenko, Tatiana; Shapeev, Alexander Physical Review Materials, Vol. 5, Issue 5 https://doi.org/10.1103/PhysRevMaterials.5.053803	journal	May 2021
Machine learning for metallurgy III: A neural network potential for Al-Mg-Si Jain, Abhinav C. P.; Marchand, Daniel; Glensk, Albert Physical Review Materials, Vol. 5, Issue 5 https://doi.org/10.1103/PhysRevMaterials.5.053805	journal	May 2021
The number of independent pair-correlation functions in multicomponent systems de Fontaine, D. Journal of Applied Crystallography, Vol. 4, Issue 1 https://doi.org/10.1107/S0021889871006174	journal	February 1971
Temperature diffuse scattering for cubic powder patterns Borie, B. Acta Crystallographica, Vol. 14, Issue 6 https://doi.org/10.1107/S0365110X61001820	journal	June 1961
Coincidence-site lattices Grimmer, H. Acta Crystallographica Section A, Vol. 32, Issue 5 https://doi.org/10.1107/S056773947601231X	journal	September 1976
A prediction of the thermodynamic, thermophysical, and mechanical properties of CrTaO₄ from first principles Hao, Shiqiang; Hong, Qi‐Jun; Gao, Michael C. Journal of the American Ceramic Society, Vol. 106, Issue 12 https://doi.org/10.1111/jace.19390	journal	August 2023
Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials Shapeev, Alexander V. Multiscale Modeling & Simulation, Vol. 14, Issue 3 https://doi.org/10.1137/15M1054183	journal	January 2016
Machine-learning potentials for crystal defects Freitas, Rodrigo; Cao, Yifan MRS Communications, Vol. 12, Issue 5 https://doi.org/10.1557/s43579-022-00221-5	journal	August 2022
Cartesian atomic cluster expansion for machine learning interatomic potentials Cheng, Bingqing arXiv https://doi.org/10.48550/arXiv.2402.07472	preprint	January 2024

Similar Records

Developing Machine Learning Interatomic Potential for Fe-Cr-Ni Alloys

Conference · Sun Mar 23 00:00:00 EDT 2025 · OSTI ID:2555821

Local lattice distortion mediated formation of stacking faults in Mg alloys

Journal Article · Sun Mar 24 00:00:00 EDT 2019 · Acta Materialia · OSTI ID:1558626

The chromium doping of Ni{sub 3}Fe alloy and restructuring of grain boundary ensemble at the phase transition A1→L1{sub 2}

Journal Article · Thu Jan 14 23:00:00 EST 2016 · AIP Conference Proceedings · OSTI ID:22494428

Related Subjects

Crystal structure
Crystallographic defects
Density functional theory
Elastic modulus
Interatomic potentials
Machine learning
Molecular dynamics
Monte Carlo methods

Developing reliable machine learning interatomic potential for Fe–Cr–Ni austenitic alloys

Citation Formats

References (67)

Similar Records

Related Subjects