Robust data-driven approach for predicting the configurational energy of high entropy alloys

Zhang, Jiaxin; Liu, Xianglin; Bi, Sirui; Yin, Junqi; Zhang, Guannan; Eisenbach, Markus

doi:10.1016/j.matdes.2019.108247

Title: Robust data-driven approach for predicting the configurational energy of high entropy alloys

Journal Article · 01 January 2020 · Materials & Design

DOI: https://doi.org/10.1016/j.matdes.2019.108247 · OSTI ID:1570041

Zhang, Jiaxin; Liu, Xianglin; Bi, Sirui; Yin, Junqi; Zhang, Guannan; Eisenbach, Markus

Not Available

View Journal Article

Cite

Export

Save

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1570041

Journal Information:: Materials & Design, Journal Name: Materials & Design Journal Issue: C Vol. 185; ISSN 0264-1275

Publisher:: ElsevierCopyright Statement

Country of Publication:: United Kingdom

Language:: English

References (59)

Nanostructured High-Entropy Alloys with Multiple Principal Elements: Novel Alloy Design Concepts and Outcomes Yeh, J.-W.; Chen, S.-K.; Lin, S.-J. Advanced Engineering Materials, Vol. 6, Issue 5, p. 299-303 https://doi.org/10.1002/adem.200300567	journal	May 2004
Hybrid Monte Carlo/Molecular Dynamics Simulation of a Refractory Metal High Entropy Alloy Widom, Michael; Huhn, W. P.; Maiti, S. Metallurgical and Materials Transactions A, Vol. 45, Issue 1 https://doi.org/10.1007/s11661-013-2000-8	journal	October 2013
Short-Range Order in High Entropy Alloys: Theoretical Formulation and Application to Mo-Nb-Ta-V-W System Fernández-Caballero, A.; Wróbel, J. S.; Mummery, P. M. Journal of Phase Equilibria and Diffusion, Vol. 38, Issue 4 https://doi.org/10.1007/s11669-017-0582-3	journal	July 2017
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
Ab initio thermodynamics of the CoCrFeMnNi high entropy alloy: Importance of entropy contributions beyond the configurational one Ma, Duancheng; Grabowski, Blazej; Körmann, Fritz Acta Materialia, Vol. 100 https://doi.org/10.1016/j.actamat.2015.08.050	journal	November 2015
A critical review of high entropy alloys and related concepts Miracle, D. B.; Senkov, O. N. Acta Materialia, Vol. 122 https://doi.org/10.1016/j.actamat.2016.08.081	journal	January 2017
Machine-learning phase prediction of high-entropy alloys Huang, Wenjiang; Martin, Pedro; Zhuang, Houlong L. Acta Materialia, Vol. 169 https://doi.org/10.1016/j.actamat.2019.03.012	journal	May 2019
The effect of prior probabilities on quantification and propagation of imprecise probabilities resulting from small datasets Zhang, Jiaxin; Shields, Michael D. Computer Methods in Applied Mechanics and Engineering, Vol. 334 https://doi.org/10.1016/j.cma.2018.01.045	journal	June 2018
Bayesian uncertainty quantification in the evaluation of alloy properties with the cluster expansion method Kristensen, Jesper; Zabaras, Nicholas J. Computer Physics Communications, Vol. 185, Issue 11 https://doi.org/10.1016/j.cpc.2014.07.013	journal	November 2014
Mechanical properties of Nb25Mo25Ta25W25 and V20Nb20Mo20Ta20W20 refractory high entropy alloys Senkov, O. N.; Wilks, G. B.; Scott, J. M. Intermetallics, Vol. 19, Issue 5 https://doi.org/10.1016/j.intermet.2011.01.004	journal	May 2011
Quantifying uncertainties in first-principles alloy thermodynamics using cluster expansions Aldegunde, Manuel; Zabaras, Nicholas; Kristensen, Jesper Journal of Computational Physics, Vol. 323 https://doi.org/10.1016/j.jcp.2016.07.016	journal	October 2016
Ab initio phase stabilities and mechanical properties of multicomponent alloys: A comprehensive review for high entropy alloys and compositionally complex alloys Ikeda, Yuji; Grabowski, Blazej; Körmann, Fritz Materials Characterization, Vol. 147 https://doi.org/10.1016/j.matchar.2018.06.019	journal	January 2019
Investigating phase formations in cast AlFeCoNiCu high entropy alloys by combination of computational modeling and experiments Beyramali Kivy, M.; Asle Zaeem, M.; Lekakh, S. Materials & Design, Vol. 127 https://doi.org/10.1016/j.matdes.2017.04.086	journal	August 2017
Estimating mechanical properties from spherical indentation using Bayesian approaches Fernandez-Zelaia, Patxi; Roshan Joseph, V.; Kalidindi, Surya R. Materials & Design, Vol. 147 https://doi.org/10.1016/j.matdes.2018.03.037	journal	June 2018
Data-driven reduced-order models for rank-ordering the high cycle fatigue performance of polycrystalline microstructures Paulson, Noah H.; Priddy, Matthew W.; McDowell, David L. Materials & Design, Vol. 154 https://doi.org/10.1016/j.matdes.2018.05.009	journal	September 2018
Local lattice distortion in NiCoCr, FeCoNiCr and FeCoNiCrMn concentrated alloys investigated by synchrotron X-ray diffraction Tong, Y.; Jin, K.; Bei, H. Materials & Design, Vol. 155 https://doi.org/10.1016/j.matdes.2018.05.056	journal	October 2018
Strength enhancement and density reduction by the addition of Al in CrFeMoV based high-entropy alloy fabricated through powder metallurgy Raza, Ahmad; Ryu, Ho Jin; Hong, Soon Hyung Materials & Design, Vol. 157 https://doi.org/10.1016/j.matdes.2018.07.023	journal	November 2018
Multi-objective Bayesian materials discovery: Application on the discovery of precipitation strengthened NiTi shape memory alloys through micromechanical modeling Solomou, Alexandros; Zhao, Guang; Boluki, Shahin Materials & Design, Vol. 160 https://doi.org/10.1016/j.matdes.2018.10.014	journal	December 2018
Using deep neural network with small dataset to predict material defects Feng, Shuo; Zhou, Huiyu; Dong, Hongbiao Materials & Design, Vol. 162 https://doi.org/10.1016/j.matdes.2018.11.060	journal	January 2019
Microstructural origins of the high mechanical damage tolerance of NbTaMoW refractory high-entropy alloy thin films Tunes, Matheus A.; Vishnyakov, Vladimir M. Materials & Design, Vol. 170 https://doi.org/10.1016/j.matdes.2019.107692	journal	May 2019
Effective design space exploration of gradient nanostructured materials using active learning based surrogate models Chen, Xin; Zhou, Haofei; Li, Yumeng Materials & Design, Vol. 183 https://doi.org/10.1016/j.matdes.2019.108085	journal	December 2019
High-entropy alloy: challenges and prospects Ye, Y. F.; Wang, Q.; Lu, J. Materials Today, Vol. 19, Issue 6 https://doi.org/10.1016/j.mattod.2015.11.026	journal	July 2016
A high-bias, low-variance introduction to Machine Learning for physicists Mehta, Pankaj; Bukov, Marin; Wang, Ching-Hao Physics Reports, Vol. 810 https://doi.org/10.1016/j.physrep.2019.03.001	journal	May 2019
Microstructures and properties of high-entropy alloys Zhang, Yong; Zuo, Ting Ting; Tang, Zhi Progress in Materials Science, Vol. 61 https://doi.org/10.1016/j.pmatsci.2013.10.001	journal	April 2014
Efficient Monte Carlo resampling for probability measure changes from Bayesian updating Zhang, Jiaxin; Shields, Michael D. Probabilistic Engineering Mechanics, Vol. 55 https://doi.org/10.1016/j.probengmech.2018.10.002	journal	January 2019
The generalization of Latin hypercube sampling Shields, Michael D.; Zhang, Jiaxin Reliability Engineering & System Safety, Vol. 148 https://doi.org/10.1016/j.ress.2015.12.002	journal	April 2016
On the quantification and efficient propagation of imprecise probabilities resulting from small datasets Zhang, Jiaxin; Shields, Michael D. Mechanical Systems and Signal Processing, Vol. 98 https://doi.org/10.1016/j.ymssp.2017.04.042	journal	January 2018
From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown Kim, Chiho; Pilania, Ghanshyam; Ramprasad, Ramamurthy Chemistry of Materials, Vol. 28, Issue 5 https://doi.org/10.1021/acs.chemmater.5b04109	journal	February 2016
Machine-learning-assisted materials discovery using failed experiments Raccuglia, Paul; Elbert, Katherine C.; Adler, Philip D. F. Nature, Vol. 533, Issue 7601 https://doi.org/10.1038/nature17439	journal	May 2016
Metastable high-entropy dual-phase alloys overcome the strength–ductility trade-off Li, Zhiming; Pradeep, Konda Gokuldoss; Deng, Yun Nature, Vol. 534, Issue 7606 https://doi.org/10.1038/nature17981	journal	May 2016
Evolutionary approach for determining first-principles hamiltonians Hart, Gus L. W.; Blum, Volker; Walorski, Michael J. Nature Materials, Vol. 4, Issue 5 https://doi.org/10.1038/nmat1374	journal	April 2005
Machine learning phases of matter Carrasquilla, Juan; Melko, Roger G. Nature Physics, Vol. 13, Issue 5 https://doi.org/10.1038/nphys4035	journal	February 2017
A general-purpose machine learning framework for predicting properties of inorganic materials Ward, Logan; Agrawal, Ankit; Choudhary, Alok npj Computational Materials, Vol. 2, Issue 1 https://doi.org/10.1038/npjcompumats.2016.28	journal	August 2016
Construction of ground-state preserving sparse lattice models for predictive materials simulations Huang, Wenxuan; Urban, Alexander; Rong, Ziqin npj Computational Materials, Vol. 3, Issue 1 https://doi.org/10.1038/s41524-017-0032-0	journal	August 2017
Machine-learning the configurational energy of multicomponent crystalline solids Natarajan, Anirudh Raju; Van der Ven, Anton npj Computational Materials, Vol. 4, Issue 1 https://doi.org/10.1038/s41524-018-0110-y	journal	November 2018
Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials Kostiuchenko, Tatiana; Körmann, Fritz; Neugebauer, Jörg npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0195-y	journal	May 2019
Machine learning for molecular and materials science Butler, Keith T.; Davies, Daniel W.; Cartwright, Hugh Nature, Vol. 559, Issue 7715 https://doi.org/10.1038/s41586-018-0337-2	journal	July 2018
Accelerating materials property predictions using machine learning Pilania, Ghanshyam; Wang, Chenchen; Jiang, Xun Scientific Reports, Vol. 3, Issue 1 https://doi.org/10.1038/srep02810	journal	September 2013
High-Entropy Alloys: A Critical Review Tsai, Ming-Hung; Yeh, Jien-Wei Materials Research Letters, Vol. 2, Issue 3 https://doi.org/10.1080/21663831.2014.912690	journal	April 2014
First-principles study of order-disorder transitions in multicomponent solid-solution alloys Eisenbach, Markus; Pei, Zongrui; Liu, Xianglin Journal of Physics: Condensed Matter, Vol. 31, Issue 27 https://doi.org/10.1088/1361-648X/ab13d8	journal	April 2019
CLEASE: a versatile and user-friendly implementation of cluster expansion method Chang, Jin Hyun; Kleiven, David; Melander, Marko Journal of Physics: Condensed Matter, Vol. 31, Issue 32 https://doi.org/10.1088/1361-648X/ab1bbc	journal	May 2019
A Theory of Cooperative Phenomena Kikuchi, Ryoichi Physical Review, Vol. 81, Issue 6 https://doi.org/10.1103/PhysRev.81.988	journal	March 1951
Using genetic algorithms to map first-principles results to model Hamiltonians: Application to the generalized Ising model for alloys Blum, Volker; Hart, Gus L. W.; Walorski, Michael J. Physical Review B, Vol. 72, Issue 16 https://doi.org/10.1103/PhysRevB.72.165113	journal	October 2005
Bayesian approach to cluster expansions Mueller, Tim; Ceder, Gerbrand Physical Review B, Vol. 80, Issue 2 https://doi.org/10.1103/PhysRevB.80.024103	journal	July 2009
Cluster expansion method for multicomponent systems based on optimal selection of structures for density-functional theory calculations Seko, Atsuto; Koyama, Yukinori; Tanaka, Isao Physical Review B, Vol. 80, Issue 16 https://doi.org/10.1103/PhysRevB.80.165122	journal	October 2009
Compressive sensing as a paradigm for building physics models Nelson, Lance J.; Hart, Gus L. W.; Zhou, Fei Physical Review B, Vol. 87, Issue 3 https://doi.org/10.1103/PhysRevB.87.035125	journal	January 2013
Density-functional Monte-Carlo simulation of CuZn order-disorder transition Khan, S. N.; Eisenbach, Markus Physical Review B, Vol. 93, Issue 2 https://doi.org/10.1103/PhysRevB.93.024203	journal	January 2016
Machine learning based interatomic potential for amorphous carbon Deringer, Volker L.; Csányi, Gábor Physical Review B, Vol. 95, Issue 9 https://doi.org/10.1103/PhysRevB.95.094203	journal	March 2017
Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces Li, Zhenwei; Kermode, James R.; De Vita, Alessandro Physical Review Letters, Vol. 114, Issue 9 https://doi.org/10.1103/PhysRevLett.114.096405	journal	March 2015
Efficient Ab initio Modeling of Random Multicomponent Alloys Jiang, Chao; Uberuaga, Blas P. Physical Review Letters, Vol. 116, Issue 10 https://doi.org/10.1103/PhysRevLett.116.105501	journal	March 2016
Local Structure and Short-Range Order in a NiCoCr Solid Solution Alloy Zhang, F. X.; Zhao, Shijun; Jin, Ke Physical Review Letters, Vol. 118, Issue 20 https://doi.org/10.1103/PhysRevLett.118.205501	journal	May 2017
Order- N Multiple Scattering Approach to Electronic Structure Calculations Wang, Yang; Stocks, G. M.; Shelton, W. A. Physical Review Letters, Vol. 75, Issue 15 https://doi.org/10.1103/PhysRevLett.75.2867	journal	October 1995
Machine learning of accurate energy-conserving molecular force fields Chmiela, Stefan; Tkatchenko, Alexandre; Sauceda, Huziel E. Science Advances, Vol. 3, Issue 5 https://doi.org/10.1126/sciadv.1603015	journal	May 2017
Outstanding radiation resistance of tungsten-based high-entropy alloys El-Atwani, O.; Li, N.; Li, M. Science Advances, Vol. 5, Issue 3 https://doi.org/10.1126/sciadv.aav2002	journal	March 2019
Machine learning: Trends, perspectives, and prospects Jordan, M. I.; Mitchell, T. M. Science, Vol. 349, Issue 6245 https://doi.org/10.1126/science.aaa8415	journal	July 2015
Inverse molecular design using machine learning: Generative models for matter engineering Sanchez-Lengeling, Benjamin; Aspuru-Guzik, Alán Science, Vol. 361, Issue 6400 https://doi.org/10.1126/science.aat2663	journal	July 2018
Automating first-principles phase diagram calculations Walle, A.; Ceder, G. Journal of Phase Equilibria, Vol. 23, Issue 4 https://doi.org/10.1361/105497102770331596	journal	August 2002
Computational modeling of high-entropy alloys: Structures, thermodynamics and elasticity Gao, Michael C.; Gao, Pan; Hawk, Jeffrey A. Journal of Materials Research, Vol. 32, Issue 19 https://doi.org/10.1557/jmr.2017.366	journal	October 2017
Modeling the structure and thermodynamics of high-entropy alloys Widom, Michael Journal of Materials Research, Vol. 33, Issue 19 https://doi.org/10.1557/jmr.2018.222	journal	July 2018

Similar Records

An experimentally driven high-throughput approach to design refractory high-entropy alloys

Journal Article · 2022 · Materials & Design · OSTI ID:1894144

Lee, Chanho; Xie, Dongyue; Kyle Derby, Benjamin; +7 more

Unraveling Reactivity Origin of Oxygen Reduction at High-Entropy Alloy Electrocatalysts with a Computational and Data-Driven Approach

Journal Article · 2024 · Journal of Physical Chemistry. C · OSTI ID:2396993

Huang, Yang; Wang, Shih-Han; Wang, Xiangrui; +4 more

Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: A data-driven approach

Journal Article · 2020 · Computational Materials Science · OSTI ID:1775228

Liu, Xianglin; Zhang, Jiaxin; Yin, Junqi; +3 more

Title: Robust data-driven approach for predicting the configurational energy of high entropy alloys

Citation Formats

References (59)

Similar Records

Related Subjects