Miryashkin, Timofei; Klimanova, Olga; Shapeev, Alexander (2026) Clarifying the Ti–V phase diagram using first-principles calculations and Bayesian learning. Computational Materials Science, 261. doi:10.1016/j.commatsci.2025.114269

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: - journal-article : 10.1137/15M1054183 If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	Bartók, Albert P., Payne, Mike C., Kondor, Risi, Csányi, Gábor (2010) Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons. Physical Review Letters, 104 (13). doi:10.1103/physrevlett.104.136403
	Not Yet Imported: - journal-article : 10.1002/qua.24927 If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	Drautz, Ralf (2019) Atomic cluster expansion for accurate and transferable interatomic potentials. Physical Review B, 99 (1) doi:10.1103/physrevb.99.014104
	Not Yet Imported: - journal-article : 10.1038/s41467-022-29939-5 If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	Musaelian, Albert, Batzner, Simon, Johansson, Anders, Sun, Lixin, Owen, Cameron J., Kornbluth, Mordechai, Kozinsky, Boris (2023) Learning local equivariant representations for large-scale atomistic dynamics. Nature Communications, 14 (1) doi:10.1038/s41467-023-36329-y
	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
	Zhang (2023) J. Chem. Phys. Deepmd-kit v2: A software package for deep learning-based atomistic simulations 159
	Batatia (2022) Mace: Higher order equivariant message passing neural networks for fast and accurate force fields vol. 35
	Yang (2024)
	Frenkel (2001)
	Bereznikova, L. A., Propad, Y. V., Kruglov, I. A. (2023) Nitrogen Phase Diagram at High P–T Conditions by the T-USPEX Method. The Journal of Physical Chemistry C, 127 (12) 5683-5688 doi:10.1021/acs.jpcc.2c09008
	Grabowski, Blazej, Ikeda, Yuji, Srinivasan, Prashanth, Körmann, Fritz, Freysoldt, Christoph, Duff, Andrew Ian, Shapeev, Alexander, Neugebauer, Jörg (2019) Ab initio vibrational free energies including anharmonicity for multicomponent alloys. npj Computational Materials, 5. doi:10.1038/s41524-019-0218-8
	Bartók (2018) Phys. Rev. X Machine learning a general-purpose interatomic potential for silicon 8
	Pedersen, Ulf R., Hummel, Felix, Kresse, Georg, Kahl, Gerhard, Dellago, Christoph (2013) Computing Gibbs free energy differences by interface pinning. Physical Review B, 88 (9) doi:10.1103/physrevb.88.094101
	Imbalzano (2021) Phys. Rev. Mater. Modeling the ga/as binary system across temperatures and compositions from first principles 5
	Not Yet Imported: Physical Review B - journal-article : 10.1103/PhysRevB.104.104102 If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	Not Yet Imported: - journal-article : 10.1103/PhysRevB.108.174103 If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	Nakano (1980) Phase separation in ti–v alloys , 2889
	Murray (1989) Vanadium Allloys Phase diagrams bin , 297
	Khaled, T., Narayanan, G. H., Copley, S. M. (1978) Phase Equilibria and Interstitial Effects in the Ti-V-Mo Alloy System. Metallurgical Transactions A, 9 (12). 1883-1890 doi:10.1007/bf02663423
	Khaled, T., Narayanan, G. H., Copley, S. M. (1981) The effect of interstitials on phase equilibria in P[Ti, Zr, Hf]-X[β-stabilizer (s)] alloy systems. Metallurgical Transactions A, 12 (2). 293-298 doi:10.1007/bf02655202
	Fuming, Wei, Flower, H. M. (1989) Phase separation reactions in Ti–50V alloys. Materials Science and Technology, 5. 1172-1177 doi:10.1179/mst.1989.5.12.1172
	Saunders (1998)
	Not Yet Imported: - journal-article : 10.1007/s11837-018-3008-8 If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	Sluiter, M., Turchi, P. E. A. (1991) Phase stability in Ti-V and Ti-Cr alloys: A theoretical investigation. Physical Review B, 43 (15) 12251-12266 doi:10.1103/physrevb.43.12251
	Chinnappan, Ravi, Panigrahi, B.K., van de Walle, Axel (2016) First-principles study of phase equilibrium in Ti–V, Ti–Nb, and Ti–Ta alloys. Calphad, 54. 125-133 doi:10.1016/j.calphad.2016.07.001
	Novikov (2020) Mach. Learn.: Sci. Technol. The mlip package: Moment tensor potentials with mpi and active learning 2
	Yang, Yang, Mao, Huahai, Chen, Hai-Lin, Selleby, Malin (2017) An assessment of the Ti-V-O system. Journal of Alloys and Compounds, 722. 365-374 doi:10.1016/j.jallcom.2017.05.326
	Smith (2017) J. Phys.: Condens. Matter. On the high-pressure phase stability and elastic properties of β -titanium alloys 29
	MacLeod (2021) J. Phys.: Condens. Matter. The phase diagram of ti-6al-4v at high-pressures and high-temperatures 33
	Williams (2006) vol. 2
	Bishop (2006)
	Not Yet Imported: - journal-article : 10.1103/PhysRevB.108.184103 If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	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
	Kresse, G., Joubert, D. (1999) From ultrasoft pseudopotentials to the projector augmented-wave method. Physical Review B, 59 (3) 1758-1775 doi:10.1103/physrevb.59.1758
	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
	Podryabinkin, Evgeny V., Tikhonov, Evgeny V., Shapeev, Alexander V., Oganov, Artem R. (2019) Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning. Physical Review B, 99 (6) doi:10.1103/physrevb.99.064114
	Podryabinkin, Evgeny V., Shapeev, Alexander V. (2017) Active learning of linearly parametrized interatomic potentials. Computational Materials Science, 140. 171-180 doi:10.1016/j.commatsci.2017.08.031
	Pedregosa (2011) J. Mach. Learn. Res. Scikit-learn: Machine learning in python 12, 2825
	Not Yet Imported: - journal-article : 10.1088/0965-0393/24/5/055007 If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	Smith (1981) Bull. Alloy. Phase Diagr. Vanadium assessment 2
	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
	Andolina (2024) Digit. Discov. Predicting melting temperatures across the periodic table with machine learning atomistic potentials 3, 1421
	Adenstedt (1952) Trans. Am. Soc. Met. The titanium-vanadium system 44, 990
	Pietrokowsky (1952) Trans. Am. Inst. Min. Met. Eng. Partial titanium-vanadium phase diagram 194, 627
	Fedotov (1968) Phys. Met. Met. Disintegration of titanium-vanadium martensite on continuous heating 25, 104
	Not Yet Imported: Metal Science and Heat Treatment - journal-article : 10.1007/BF00673866 If you would like this item imported into the Digital Library, please contact us quoting Journal ID
	Rudy (1969)
	Miryashkin (2025)

See Also

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

	Podryabinkin, Evgeny V., Shapeev, Alexander V. (2017) Active learning of linearly parametrized interatomic potentials. Computational Materials Science, 140. 171-180 doi:10.1016/j.commatsci.2017.08.031
	Klimanova, Olga; Rybin, Nikita; Shapeev, Alexander (2025) Accelerating the global search of adsorbate molecule positions using machine-learning interatomic potentials with active learning. Physical Chemistry Chemical Physics, 27 (17). doi:10.1039/d5cp00532a
	Ikeda, Yukinari; Ishii, Akio (2026) Calculation of the Ti–Mo phase diagram using density functional theory and crystal symmetry. Computational Materials Science, 267. doi:10.1016/j.commatsci.2026.114594
	Batnyam, N.; Odkhuu, D. (2026) Enhancing intrinsic magnetic properties and phase stability in SmFe11Ti through elemental substitution: A first-principles approach. Computational Materials Science, 261. doi:10.1016/j.commatsci.2025.114268
	Matsuura, Hiromu; Hoshizaki, Kotaro; Ida, Shuntaro; Yoshimi, Kyosuke (2026) Investigation of the effect of structural vacancies on the physical and mechanical properties of multi-anion oxycabonitride Ti(C,N,O) using first-principles calculations. Computational Materials Science, 263. doi:10.1016/j.commatsci.2025.114461
	Colinet, Catherine, Tedenac, Jean-Claude (2014) First principles calculations in V–Si system. Defects in A15-V3Si phase. Computational Materials Science, 85. 94-101 doi:10.1016/j.commatsci.2013.12.044
	Shapeev, Alexander V., Bocharov, Dmitry, Kuzmin, Alexei (2022) Validation of moment tensor potentials for fcc and bcc metals using EXAFS spectra. Computational Materials Science, 210. 111028 doi:10.1016/j.commatsci.2021.111028
	Yoshida, Tomohiro (2023) Unraveling the relationships between heat-shielding properties and crystal structure using first-principles calculations. Computational Materials Science, 230. 112484 doi:10.1016/j.commatsci.2023.112484
	Gubaev, Konstantin, Podryabinkin, Evgeny V., Hart, Gus L.W., Shapeev, Alexander V. (2019) Accelerating high-throughput searches for new alloys with active learning of interatomic potentials. Computational Materials Science, 156. 148-156 doi:10.1016/j.commatsci.2018.09.031
	Mizuno, Masataka; Araki, Hideki (2026) Elastic properties of Cu-Ni and Cu-Zn alloys based on first-principles calculations. Computational Materials Science, 263. doi:10.1016/j.commatsci.2025.114469
	Shao, Wei, Liu, Sha, LLorca, Javier (2023) First principles prediction of the Al-Li phase diagram including configurational and vibrational entropic contributions. Computational Materials Science, 217. 111898 doi:10.1016/j.commatsci.2022.111898