Zhu, Siya; Sarítürk, Doğuhan; Arróyave, Raymundo (2025) Machine learning potentials for alloys: a detailed workflow to predict phase diagrams and benchmark accuracy. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01814-z

References Listed

These are the references the publisher has listed as being connected to the article. Please check the article itself for the full list of references which may differ. Not all references are currently linkable within the Digital Library.

	Not Yet Imported: Acta Materialia - journal-article : 10.1016/j.actamat.2016.08.081 If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	Senkov, O.N., Wilks, G.B., Miracle, D.B., Chuang, C.P., Liaw, P.K. (2010) Refractory high-entropy alloys. Intermetallics, 18 (9). 1758-1765 doi:10.1016/j.intermet.2010.05.014
	Not Yet Imported: - journal-article : 10.3389/fmats.2020.00290 If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	Not Yet Imported: - monograph : 10.1017/CBO9780511804137 If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	Villars, P. Handbook of ternary alloy phase diagrams. ASM Int. 7, 8754–8755 (1995).
	Zunger, Alex, Wei, S.-H., Ferreira, L. G., Bernard, James E. (1990) Special quasirandom structures. Physical Review Letters, 65 (3). 353-356 doi:10.1103/physrevlett.65.353
	van de Walle, Axel, Sun, Ruoshi, Hong, Qi-Jun, Kadkhodaei, Sara (2017) Software tools for high-throughput CALPHAD from first-principles data. Calphad, 58. 70-81 doi:10.1016/j.calphad.2017.05.005
	Samanta, Sayan, van de Walle, Axel (2024) Software Tools for Integrating Special Quasirandom Structures and the Cluster Variation Method into the CALPHAD Formalism. Journal of Phase Equilibria and Diffusion, 45 (6). doi:10.1007/s11669-024-01151-6
	Zhu, Siya, van de Walle, Axel (2021) Computational Assessment of Novel Predicted Compounds in Ni-Re Alloy System. Journal of Phase Equilibria and Diffusion, 42 (2) 315-320 doi:10.1007/s11669-021-00884-y
	Broughton, J. Q., Li, X. P. (1987) Phase diagram of silicon by molecular dynamics. Physical Review B, 35 (17) 9120-9127 doi:10.1103/physrevb.35.9120
	Not Yet Imported: - journal-article : 10.1016/j.actamat.2022.118512 If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	Not Yet Imported: Acta Materialia - journal-article : 10.1016/j.actamat.2023.119062 If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	Not Yet Imported: Acta Materialia - journal-article : 10.1016/j.actamat.2021.117269 If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	Rosenbrock, Conrad W., Gubaev, Konstantin, Shapeev, Alexander V., Pártay, Livia B., Bernstein, Noam, Csányi, Gábor, Hart, Gus L. W. (2021) Machine-learned interatomic potentials for alloys and alloy phase diagrams. npj Computational Materials, 7. doi:10.1038/s41524-020-00477-2
	Zhu, S., Sarítürk, D. & Arróyave, R. Accelerating CALPHAD-based phase diagram predictions in complex alloys using universal machine learning potentials: opportunities and challenges. Acta Mater. 286, 120747 (2025).
	van de Walle, A., Asta, M., Ceder, G. (2002) The alloy theoretic automated toolkit: A user guide. Calphad, 26 (4) 539-553 doi:10.1016/s0364-5916(02)80006-2
	Yaqoob, Khurram, Joubert, Jean-Marc (2012) Experimental determination and thermodynamic modeling of the Ni–Re binary system. Journal of Solid State Chemistry, 196. 320-325 doi:10.1016/j.jssc.2012.06.036
	Pogodin, S. & Skryabina, M. Nickel-rhenium system. Isz Sekt. Fiz. Khim Anal. 25, 81–88 (1954).
	Savitskii, E., Tylkina, M. & Povarova, K. Rhenium-based alloys. Izd. Nauka Moskva 335 (1965).
	Not Yet Imported: - book-chapter : 10.1007/978-1-4684-1572-8_6 If you would like this item imported into the Digital Library, please contact us quoting Book ID 9781468415742
	Neubauer, C.M., Mari, D., Dunand, D.C. (1994) Diffusion in the nickel-rhenium system. Scripta Metallurgica et Materialia, 31 (1). 99-104 doi:10.1016/0956-716x(94)90102-3
	Saito, Shigeru, Kurokawa, Kazuya, Hayashi, Shigenari, Takashima, Toshiyuki, Narita, Toshio (2007) Phase Equilibria and Tie-Lined Compositions in a Ternary Ni-Al-Re System at 1423 K. Journal of the Japan Institute of Metals, 71 (9). 793-800 doi:10.2320/jinstmet.71.793
	Okamoto, H. (2012) Ni-Re (Nickel-Rhenium) Journal of Phase Equilibria and Diffusion, 33 (4) 346 doi:10.1007/s11669-012-0053-9
	Narita, S. Master’s thesis. Graduate School of Hokkaido University, as quoted in [66] and [67] (2003).
	Hardcastle, C., O’Mullan, R., Arroyave, R. & Vela, B. Physics-informed Gaussian process classification for constraint-aware alloy design. Digit. Discovery. 4, 1884-1900 (2025).
	Choi, Won-Mi, Jo, Yong Hee, Kim, Dong Geun, Sohn, Seok Su, Lee, Sunghak, Lee, Byeong-Joo (2019) A thermodynamic description of the Co-Cr-Fe-Ni-V system for high-entropy alloy design. Calphad, 66. 101624 doi:10.1016/j.calphad.2019.05.001
	Not Yet Imported: Scripta Materialia - journal-article : 10.1016/j.scriptamat.2016.12.033 If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	Ding, J., Inoue, A., Han, Y., Kong, F.L., Zhu, S.L., Wang, Z., Shalaan, E., Al-Marzouki, F. (2017) High entropy effect on structure and properties of (Fe,Co,Ni,Cr)-B amorphous alloys. Journal of Alloys and Compounds, 696. 345-352 doi:10.1016/j.jallcom.2016.11.223
	Bochkarev, A., Lysogorskiy, Y. & Drautz, R. Graph atomic cluster expansion for semilocal interactions beyond equivariant message passing. Phys. Rev. X 14, 021036 (2024).
	Chen, S.-L., Daniel, S., Zhang, F., Chang, Y.A., Yan, X.-Y., Xie, F.-Y., Schmid-Fetzer, R., Oates, W.A. (2002) The PANDAT software package and its applications. Calphad, 26 (2) 175-188 doi:10.1016/s0364-5916(02)00034-2
	Nishizawa, T., Ishida, K. (1983) The Co−Ni (Cobalt-Nickel) system. Bulletin of Alloy Phase Diagrams, 4 (4) 390-395 doi:10.1007/bf02868090
	Smith, J. F., Carlson, O. N., Nash, P. G. (1982) The Ni−V (Nickel−Vanadium) system. Bulletin of Alloy Phase Diagrams, 3 (3) 342-348 doi:10.1007/bf02869306
	Okamoto, H. (2006) Fe-V (Iron-Vanadium) Journal of Phase Equilibria and Diffusion, 27 (5) 542 doi:10.1007/bf02736471
	Swartzendruber, L. J., Itkin, V. P., Alcock, C. B. (1991) The Fe-Ni (iron-nickel) system. Journal of Phase Equilibria, 12 (3) 288-312 doi:10.1007/bf02649918
	Ghosh, G. (2002) Thermodynamic and kinetic modeling of the Cr-Ti-V system. Journal of Phase Equilibria, 23 (4) 310-328 doi:10.1361/105497102770331569
	Byeong-Joo, Lee (1993) Revision of thermodynamic descriptions of the Fe-Cr & Fe-Ni liquid phases. Calphad, 17 (3) 251-268 doi:10.1016/0364-5916(93)90004-u
	Okamoto, H. (2007) Co-V (Cobalt-Vanadium) Journal of Phase Equilibria and Diffusion, 28 (3) 314 doi:10.1007/s11669-007-9049-2
	Not Yet Imported: - journal-article : 10.1016/S1359-6454(01)00337-8 If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	Okamoto, H. (2003) Co-Cr (Cobalt-Chromium) Journal of Phase Equilibria, 24 (4) 377-378 doi:10.1361/105497103770330460
	van de Walle, Axel (2018) Invited paper: Reconciling SGTE and ab initio enthalpies of the elements. Calphad, 60. 1-6 doi:10.1016/j.calphad.2017.10.008
	Kusne, A. Gilad, Yu, Heshan, Wu, Changming, Zhang, Huairuo, Hattrick-Simpers, Jason, DeCost, Brian, Sarker, Suchismita, Oses, Corey, Toher, Cormac, Curtarolo, Stefano, Davydov, Albert V., Agarwal, Ritesh, Bendersky, Leonid A., Li, Mo, Mehta, Apurva, Takeuchi, Ichiro (2020) On-the-fly closed-loop materials discovery via Bayesian active learning. Nature Communications, 11 (1) doi:10.1038/s41467-020-19597-w
	Not Yet Imported: - journal-article : 10.1126/sciadv.abg4930 If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	Dai, Chengyu, Glotzer, Sharon C. (2020) Efficient Phase Diagram Sampling by Active Learning. The Journal of Physical Chemistry B, 124 (7). 1275-1284 doi:10.1021/acs.jpcb.9b09202
	Hillert, Mats (2001) The compound energy formalism. Journal of Alloys and Compounds, 320. 161-176 doi:10.1016/s0925-8388(00)01481-x
	Dinsdale, A.T. (1991) SGTE data for pure elements. Calphad, 15 (4) 317-425 doi:10.1016/0364-5916(91)90030-n
	Hjorth Larsen, Ask, Jørgen Mortensen, Jens, Blomqvist, Jakob, Castelli, Ivano E, Christensen, Rune, Dułak, Marcin, Friis, Jesper, Groves, Michael N, Hammer, Bjørk, Hargus, Cory, Hermes, Eric D, Jennings, Paul C, Bjerre Jensen, Peter, Kermode, James, Kitchin, John R, Leonhard Kolsbjerg, Esben, Kubal, Joseph, Kaasbjerg, Kristen, Lysgaard, Steen, Bergmann Maronsson, Jón, Maxson, Tristan, Olsen, Thomas, Pastewka, Lars, Peterson, Andrew, Rostgaard, Carsten, Schiøtz, Jakob, Schütt, Ole, Strange, Mikkel, Thygesen, Kristian S, Vegge, Tejs, Vilhelmsen, Lasse, Walter, Michael, Zeng, Zhenhua, Jacobsen, Karsten W (2017) The atomic simulation environment—a Python library for working with atoms. Journal of Physics: Condensed Matter, 29 (27). 273002pp. doi:10.1088/1361-648x/aa680e
	Melchionna, Simone, Ciccotti, Giovanni, Lee Holian, Brad (1993) Hoover NPT dynamics for systems varying in shape and size. Molecular Physics, 78. 533-544 doi:10.1080/00268979300100371
	Plimpton, Steve (1995) Fast Parallel Algorithms for Short-Range Molecular Dynamics. Journal of Computational Physics, 117 (1) 1-19 doi:10.1006/jcph.1995.1039
	Thompson, Aidan P., Aktulga, H. Metin, Berger, Richard, Bolintineanu, Dan S., Brown, W. Michael, Crozier, Paul S., in 't Veld, Pieter J., Kohlmeyer, Axel, Moore, Stan G., Nguyen, Trung Dac, Shan, Ray, Stevens, Mark J., Tranchida, Julien, Trott, Christian, Plimpton, Steven J. (2022) LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Computer Physics Communications, 271. 108171pp. doi:10.1016/j.cpc.2021.108171
	Walle, A., Ceder, G. (2002) Automating first-principles phase diagram calculations. Journal of Phase Equilibria, 23 (4) 348-359 doi:10.1361/105497102770331596
	van de Walle, Axel (2009) Multicomponent multisublattice alloys, nonconfigurational entropy and other additions to the Alloy Theoretic Automated Toolkit. Calphad, 33 (2) 266-278 doi:10.1016/j.calphad.2008.12.005
	Kresse, G., Hafner, J. (1993) Ab initiomolecular dynamics for liquid metals. Physical Review B, 47 (1) 558-561 doi:10.1103/physrevb.47.558
	Kresse, G., Hafner, J. (1994) Ab initiomolecular-dynamics simulation of the liquid-metal–amorphous-semiconductor transition in germanium. Physical Review B, 49 (20) 14251-14269 doi:10.1103/physrevb.49.14251
	Kresse, G., Furthmüller, J. (1996) Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Computational Materials Science, 6 (1). 15-50 doi:10.1016/0927-0256(96)00008-0
	Kresse, G., Furthmüller, J. (1996) Efficient iterative schemes forab initiototal-energy calculations using a plane-wave basis set. Physical Review B, 54 (16) 11169-11186 doi:10.1103/physrevb.54.11169
	Perdew, John P., Burke, Kieron, Ernzerhof, Matthias (1996) Generalized Gradient Approximation Made Simple. Physical Review Letters, 77 (18). 3865-3868 doi:10.1103/physrevlett.77.3865
	Blöchl, P. E. (1994) Projector augmented-wave method. Physical Review B, 50 (24) 17953-17979 doi:10.1103/physrevb.50.17953
	Kadkhodaei, Sara, Hong, Qi-Jun, van de Walle, Axel (2017) Free energy calculation of mechanically unstable but dynamically stabilized bcc titanium. Physical Review B, 95 (6) doi:10.1103/physrevb.95.064101
	Kadkhodaei, Sara, van de Walle, Axel (2020) Software tools for thermodynamic calculation of mechanically unstable phases from first-principles data. Computer Physics Communications, 246. 106712pp. doi:10.1016/j.cpc.2019.01.008
	Chen, Hantong; Hong, Qi-Jun; Navrotsky, Alexandra; van de Walle, Axel (2025) A computational free energy reference for mechanically unstable phases. Calphad, 91. doi:10.1016/j.calphad.2025.102869
	Kikuchi, Ryoichi (1951) A Theory of Cooperative Phenomena. Physical Review, 81 (6). 988-1003 doi:10.1103/physrev.81.988
	Morral, J.E, Gupta, H (1991) Phase boundary, ZPF, and topological lines on phase diagrams. Scripta Metallurgica et Materialia, 25 (6). 1393-1396 doi:10.1016/0956-716x(91)90420-6
	Morral, J. E., Gupta, H. (1992) A figure of merit for predicted phase diagrams. Journal of Phase Equilibria, 13 (4) 373-376 doi:10.1007/bf02674982
	Sundman, Bo, Jansson, Bo, Andersson, Jan-Olof (1985) The Thermo-Calc databank system. Calphad, 9 (2) 153-190 doi:10.1016/0364-5916(85)90021-5
	Andersson, J-O, Helander, Thomas, Höglund, Lars, Shi, Pingfang, Sundman, Bo (2002) Thermo-Calc & DICTRA, computational tools for materials science. Calphad, 26 (2) 273-312 doi:10.1016/s0364-5916(02)00037-8
	Bale, C.W., Bélisle, E., Chartrand, P., Decterov, S.A., Eriksson, G., Gheribi, A.E., Hack, K., Jung, I.-H., Kang, Y.-B., Melançon, J., Pelton, A.D., Petersen, S., Robelin, C., Sangster, J., Spencer, P., Van Ende, M-A. (2016) Reprint of: FactSage thermochemical software and databases, 2010–2016. Calphad, 55. 1-19 doi:10.1016/j.calphad.2016.07.004
	Not Yet Imported: - journal-article : 10.1186/s40192-014-0029-1 If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	Choudhary, Kamal, DeCost, Brian (2021) Atomistic Line Graph Neural Network for improved materials property predictions. npj Computational Materials, 7. doi:10.1038/s41524-021-00650-1
	Not Yet Imported: - journal-article : 10.1039/D2DD00096B If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	Yin, B. et al. AlphaNet: scaling up local-frame-based atomistic interatomic potential. https://doi.org/10.48550/arXiv.2501.07155 (2025).
	Deng, B. et al. CHGNet: pretrained universal neural network potential for charge-informed atomistic modeling. https://doi.org/10.48550/arXiv.2302.14231 (2023).
	Wang, Han, Zhang, Linfeng, Han, Jiequn, E, Weinan (2018) DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics. Computer Physics Communications, 228. 178-184 doi:10.1016/j.cpc.2018.03.016
	Zeng, Jinzhe, Zhang, Duo, Lu, Denghui, Mo, Pinghui, Li, Zeyu, Chen, Yixiao, Rynik, Marián, Huang, Li’ang, Li, Ziyao, Shi, Shaochen, Wang, Yingze, Ye, Haotian, Tuo, Ping, Yang, Jiabin, Ding, Ye, Li, Yifan, Tisi, Davide, Zeng, Qiyu, Bao, Han, Xia, Yu, Huang, Jiameng, Muraoka, Koki, Wang, Yibo, Chang, Junhan, Yuan, Fengbo, Bore, Sigbjørn Løland, Cai, Chun, Lin, Yinnian, Wang, Bo, Xu, Jiayan, Zhu, Jia-Xin, Luo, Chenxing, Zhang, Yuzhi, Goodall, Rhys E. A., Liang, Wenshuo, Singh, Anurag Kumar, Yao, Sikai, Zhang, Jingchao, Wentzcovitch, Renata, Han, Jiequn, Liu, Jie, Jia, Weile, York, Darrin M., E, Weinan, Car, Roberto, Zhang, Linfeng, Wang, Han (2023) DeePMD-kit v2: A software package for deep potential models. The Journal of Chemical Physics, 159 (5) AIP Publishing. doi:10.1063/5.0155600
	Zeng, J. et al. DeePMD-kit v3: a multiple-backend framework for machine learning potentials. J. Chem. Theory Comput. 21, 9, 4375–4385 (2025)
	Barroso-Luque, L. et al. Open materials 2024 (OMat24) inorganic materials dataset and models. https://doi.org/10.48550/arXiv.2410.12771 (2024).
	Fu, X. et al. Learning smooth and expressive interatomic potentials for physical property prediction. https://doi.org/10.48550/arXiv.2502.12147 (2025).
	Xie, Fankai, Lu, Tenglong, Meng, Sheng, Liu, Miao (2024) GPTFF: A high-accuracy out-of-the-box universal AI force field for arbitrary inorganic materials. Science Bulletin, 69 (22). doi:10.1016/j.scib.2024.08.039
	Yan, K. et al. A materials foundation model via hybrid invariant-equivariant architectures. https://doi.org/10.48550/arXiv.2503.05771 (2025).
	Not Yet Imported: - journal-article : 10.1038/s43588-022-00349-3 If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	Batatia, I. et al. A foundation model for atomistic materials chemistry. https://doi.org/10.48550/arXiv.2401.00096 (2024).
	Yang, H. et al. MatterSim: a deep learning atomistic model across elements, temperatures and pressures. https://doi.org/10.48550/arXiv.2405.04967 (2024).
	Not Yet Imported: - journal-article : 10.1039/D2DD00008C If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	Neumann, M. et al. Orb: a fast, scalable neural network potential. https://doi.org/10.48550/arXiv.2410.22570 (2024).
	Rhodes, B. et al. Orb-v3: atomistic simulation at scale. https://doi.org/10.48550/arXiv.2504.06231 (2025).
	Mazitov, A. et al. PET-MAD, a universal interatomic potential for advanced materials modeling https://doi.org/10.48550/arXiv.2503.14118 (2025).
	IBM Research. pos-egnn: position-based equivariant graph neural network for chemistry and materials. https://huggingface.co/ibm-research/materials.pos-egnn (2025).
	Park, Y., Kim, J., Hwang, S. & Han, S. Scalable parallel algorithm for graph neural network interatomic potentials in molecular dynamics simulations. J. Chem. Theory Comput. 20, 4857–4868 (2024).
	Kim, Jaesun, Kim, Jisu, Kim, Jaehoon, Lee, Jiho, Park, Yutack, Kang, Youngho, Han, Seungwu (2025) Data-Efficient Multifidelity Training for High-Fidelity Machine Learning Interatomic Potentials. Journal of the American Chemical Society, 147 (1). doi:10.1021/jacs.4c14455

See Also

These are possibly similar items as determined by title/reference text matching only.

	Zhu, Siya; Sarıtürk, Doğuhan; Arróyave, Raymundo (2025) Publisher Correction: Machine learning potentials for alloys: a detailed workflow to predict phase diagrams and benchmark accuracy. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01913-x
	Poul, Marvin; Huber, Liam; Neugebauer, Jörg (2025) Automated generation of structure datasets for machine learning potentials and alloys. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01669-4
	Zhu, Siya; Arróyave, Raymundo (2026) Ground-state structure search of defective high-entropy alloys using machine-learning potentials and Monte Carlo sampling. Computational Materials Science, 270. doi:10.1016/j.commatsci.2026.114752
	Zhang, Chenbo; Chen, Xian (2025) FerroAI: a deep learning model for predicting phase diagrams of ferroelectric materials. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01778-0
	Cao, Yifan; Sheriff, Killian; Freitas, Rodrigo (2025) Capturing short-range order in high-entropy alloys with machine learning potentials. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01722-2
	Corradini, Andrea; Marini, Giovanni; Calandra, Matteo (2025) Scalable machine learning approach to light induced order disorder phase transitions with ab initio accuracy. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01614-5
	Briganti, Valerio, Lunghi, Alessandro (2025) A machine-learning framework for accelerating spin-lattice relaxation simulations. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01547-z
	Vondrák, Martin; Reuter, Karsten; Margraf, Johannes T. (2025) Pushing charge equilibration-based machine learning potentials to their limits. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01791-3
	Corradini, Andrea; Marini, Giovanni; Calandra, Matteo (2025) Author Correction: Scalable machine learning approach to light induced order disorder phase transitions with ab initio accuracy. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01748-6
	Belli, Francesco; Zurek, Eva (2025) Efficient modelling of anharmonicity and quantum effects in PdCuH2 with machine learning potentials. npj Computational Materials, 11 (1). doi:10.1038/s41524-025-01553-1
	Zhu, Siya; Eckert, Hagen; Curtarolo, Stefano; Schroers, Jan; Arróyave, Raymundo; van de Walle, Axel (2026) Computational study of density fluctuation-facilitated shear band formation in bulk metallic glasses. npj Computational Materials, 12 (1). doi:10.1038/s41524-026-02031-y