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First principles phonon calculations in materials science
Open accessAbstract
Phonon plays essential roles in dynamical behaviors and thermal properties, which are central topics in fundamental issues of materials science. The importance of first principles phonon calculations cannot be overly emphasized. Phonopy is an open source code for such calculations launched by the present authors, which has been world-widely used. Here we demonstrate phonon properties with fundamental equations and show examples how the phonon calculations are applied in materials science.
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Cites
Generalized Gradient Approximation Made Simple
Perdew, J. P., Burke, K., & Ernzerhof, M. (1996) · Physical Review Letters · doi:10.1103/physrevlett.77.3865 · Cited by 215,824
Projector augmented-wave method
Blöchl, P. E. (1994) · Physical Review B · doi:10.1103/physrevb.50.17953 · Cited by 92,732
From ultrasoft pseudopotentials to the projector augmented-wave method
Kresse, G., & Joubert, D. (1999) · Physical Review B · doi:10.1103/physrevb.59.1758 · Cited by 85,307
Kresse, G., & Furthmüller, J. (1996) · Computational Materials Science · doi:10.1016/0927-0256(96)00008-0 · Cited by 75,638
High-precision sampling for Brillouin-zone integration in metals
Methfessel, M., & Paxton, A. T. (1989) · Physical Review B · doi:10.1103/physrevb.40.3616 · Cited by 7,774
Togo, A., Oba, F., & Tanaka, I. (2008) · Physical Review B · doi:10.1103/physrevb.78.134106 · Cited by 5,486
Self-citationGonze, X., & Lee, C. (1997) · Physical Review B · doi:10.1103/physrevb.55.10355 · Cited by 3,412
Ziman, J. M., & Levy, P. W. (1961) · Physics Today · doi:10.1063/1.3057244 · Cited by 3,368
NIST-JANAF Thermochemical Tables
Chase, M. W. (1998) · Medical Entomology and Zoology · Cited by 3,312
First-Principles Determination of the Soft Mode in CubicZrO2
Parlinski, K., Li, Z. Q., & Kawazoe, Y. (1997) · Physical Review Letters · doi:10.1103/physrevlett.78.4063 · Cited by 2,840
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WIEN2k: An APW+lo program for calculating the properties of solids
Blaha, P., Schwarz, K., Tran, F., Laskowski, R., Madsen, G. K. H., & Marks, L. D. (2020) · The Journal of Chemical Physics · doi:10.1063/1.5143061 · Cited by 2,696
First-principles Phonon Calculations with Phonopy and Phono3py
Togo, A. (2023) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.92.012001 · Cited by 1,865
Self-citationA universal graph deep learning interatomic potential for the periodic table
Chen, C., & Ong, S. P. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00349-3 · Cited by 1,140
Ising-Type Magnetic Ordering in Atomically Thin FePS3
Lee, J.-U., Lee, S., Ryoo, J. H., Kang, S., Kim, T. Y., Kim, P., Park, C.-H., Park, J.-G., & Cheong, H. (2016) · Nano Letters · doi:10.1021/acs.nanolett.6b03052 · Cited by 1,074
The thermodynamic scale of inorganic crystalline metastability
Sun, W., Dacek, S. T., Ong, S. P., Hautier, G., Jain, A., Richards, W. D., Gamst, A. C., Persson, K. A., & Ceder, G. (2016) · Science Advances · doi:10.1126/sciadv.1600225 · Cited by 961
From DFT to machine learning: recent approaches to materials science–a review
Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M., & Fazzio, A. (2019) · Journal of Physics: Materials · doi:10.1088/2515-7639/ab084b · Cited by 852
Magnesium based materials for hydrogen based energy storage: Past, present and future
Yartys, V. A., Lototskyy, M. V., Akiba, E., Albert, R., Antonov, V. E., Ares, J. R., Baricco, M., Bourgeois, N., Buckley, C. E., Bellosta von Colbe, J. M., et al. (2019) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2018.12.212 · Cited by 770
Roton pair density wave in a strong-coupling kagome superconductor
Chen, H., Yang, H., Hu, B., Zhao, Z., Yuan, J., Xing, Y., Qian, G., Huang, Z., Li, G., Ye, Y., et al. (2021) · Nature · doi:10.1038/s41586-021-03983-5 · Cited by 622
High thermal conductivity of high-quality monolayer boron nitride and its thermal expansion
Cai, Q., Scullion, D., Gan, W., Falin, A., Zhang, S., Watanabe, K., Taniguchi, T., Chen, Y., Santos, E. J. G., & Li, L. H. (2019) · Science Advances · doi:10.1126/sciadv.aav0129 · Cited by 607
Tamaki, H., Sato, H. K., & Kanno, T. (2016) · Advanced Materials · doi:10.1002/adma.201603955 · Cited by 592
Raman Spectra of Polycrystalline CeO2: A Density Functional Theory Study
Schilling, C., Hofmann, A., Hess, C., & Ganduglia-Pirovano, M. V. (2017) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.7b06643 · Cited by 513
Charge Density Waves and Electronic Properties of Superconducting Kagome Metals
Tan, H., Liu, Y., Wang, Z., & Yan, B. (2021) · Physical Review Letters · doi:10.1103/physrevlett.127.046401 · Cited by 477
Machine Learning a General-Purpose Interatomic Potential for Silicon
Bartók, A. P., Kermode, J., Bernstein, N., & Csányi, G. (2018) · Physical Review X · doi:10.1103/physrevx.8.041048 · Cited by 470
Wang, L., Shi, Y., Liu, M., Zhang, A., Hong, Y.-L., Li, R., Gao, Q., Chen, M., Ren, W., Cheng, H.-M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22324-8 · Cited by 466
Suppression of magnetic ordering in XXZ-type antiferromagnetic monolayer NiPS3
Kim, K., Lim, S. Y., Lee, J.-U., Lee, S., Kim, T. Y., Park, K., Jeon, G. S., Park, C.-H., Park, J.-G., & Cheong, H. (2019) · Nature Communications · doi:10.1038/s41467-018-08284-6 · Cited by 452
Peng, B., Zhang, H., Shao, H., Xu, Y., Zhang, R., & Zhu, H. (2016) · Journal of Materials Chemistry C · doi:10.1039/c6tc00115g · Cited by 438
Discovery of TaFeSb-based half-Heuslers with high thermoelectric performance
Zhu, H., Mao, J., Li, Y., Sun, J., Wang, Y., Zhu, Q., Li, G., Song, Q., Zhou, J., Fu, Y., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-08223-5 · Cited by 386
Aktürk, E., Aktürk, O. Ü., & Ciraci, S. (2016) · Physical Review B · doi:10.1103/physrevb.94.014115 · Cited by 382
CRYSTAL23: A Program for Computational Solid State Physics and Chemistry
Erba, A., Desmarais, J. K., Casassa, S., Civalleri, B., Donà, L., Bush, I. J., Searle, B., Maschio, L., Edith-Daga, L., Cossard, A., et al. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00958 · Cited by 367
Representation of compounds for machine-learning prediction of physical properties
Seko, A., Hayashi, H., Nakayama, K., Takahashi, A., & Tanaka, I. (2017) · Physical Review B · doi:10.1103/physrevb.95.144110 · Cited by 304
Self-citationCarrete, J., Vermeersch, B., Katre, A., van Roekeghem, A., Wang, T., Madsen, G. K. H., & Mingo, N. (2017) · Computer Physics Communications · doi:10.1016/j.cpc.2017.06.023 · Cited by 300
Huang, L.-F., Hutchison, M. J., Santucci, R. J., Scully, J. R., & Rondinelli, J. M. (2017) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.7b02771 · Cited by 300
Balasubramanian, K., Khare, S. V., & Gall, D. (2018) · Acta Materialia · doi:10.1016/j.actamat.2018.04.033 · Cited by 282
Zhang, Y., Sun, J., Perdew, J. P., & Wu, X. (2017) · Physical Review B · doi:10.1103/physrevb.96.035143 · Cited by 258
Jain, A., Hautier, G., Ong, S. P., & Persson, K. (2016) · Journal of Materials Research · doi:10.1557/jmr.2016.80 · Cited by 244
Juraschek, D. M., Fechner, M., Balatsky, A. V., & Spaldin, N. A. (2017) · Physical Review Materials · doi:10.1103/physrevmaterials.1.014401 · Cited by 219
Orbital magnetic moments of phonons
Juraschek, D. M., & Spaldin, N. A. (2019) · Physical Review Materials · doi:10.1103/physrevmaterials.3.064405 · Cited by 182
Giant effective magnetic fields from optically driven chiral phonons in 4 f paramagnets
Juraschek, D. M., Neuman, T., & Narang, P. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.013129 · Cited by 113
Phono-magnetic analogs to opto-magnetic effects
Juraschek, D. M., Narang, P., & Spaldin, N. A. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.043035 · Cited by 94
Guo, Z., & Lin, B. (2021) · Solar Energy · doi:10.1016/j.solener.2021.09.030 · Cited by 95
Gao, C., Cao, X., Liu, Q., Yang, Y., Zhang, S., He, Y., Tang, M., & Liu, Y. (2018) · Chemical Geology · doi:10.1016/j.chemgeo.2018.04.005 · Cited by 66
Lattice Thermal Conductivity: An Accelerated Discovery Guided by Machine Learning
Jaafreh, R., Kang, Y. S., & Hamad, K. (2021) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c17378 · Cited by 66
Accurate large-scale simulations of siliceous zeolites by neural network potentials
Erlebach, A., Nachtigall, P., & Grajciar, L. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00865-w · Cited by 58
Dynamically induced magnetism in KTaO3
Geilhufe, R. M., Juričić, V., Bonetti, S., Zhu, J.-X., & Balatsky, A. V. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.l022011 · Cited by 53
Rahm, M., Lunine, J. I., Usher, D. A., & Shalloway, D. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1606634113 · Cited by 47
Combining phonon accuracy with high transferability in Gaussian approximation potential models
George, J., Hautier, G., Bartók, A. P., Csányi, G., & Deringer, V. L. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0013826 · Cited by 44
Maeng, I., Lee, S., Han, E. Q., Zhang, Y., Oh, S. J., Nakamura, M., Yun, J.-H., Wang, L., Kwon, Y.-K., & Jung, M.-C. (2021) · NPG Asia Materials · doi:10.1038/s41427-021-00343-7 · Cited by 24
Performance-Based Screening of Porous Materials for Carbon Capture
Farmahini, A. H., Krishnamurthy, S., Friedrich, D., Brandani, S., & Sarkisov, L. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01266 · Cited by 7
Metadata
- Authors
- Togo, A., & Tanaka, I.
- Venue
- Scripta Materialia
- Publisher
- Elsevier BV
- Published
- 2015-11
- Volume
- 108
- Pages
- 1-5
- ISSN
- 1359-6462 (ISSN-L)
- OpenAlex
- W832976576
- Cited by
- 11,549
- References
- 35
- FWCI
- 146.95
- Type
- journal article
- Language
- en
- Open access
- hybrid
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