発表論文

  1. A. Seko, ,Polynomial machine learning potential and its application to global structure search in the ternary Cu-Ag-Au alloy, arXiv preprint arXiv:2407.20630 (2024).

  2. A. Seko, A. Togo, Projector-based efficient estimation of force constants, arXiv preprint arXiv:2403.03588 (2024).

  3. A. Seko, Globally-stable and metastable crystal structure enumeration using polynomial machine learning potentials in elemental As, Bi, Ga, In, La, P, Sb, Sn, and Te, arXiv preprint arXiv:2403.02570 (2024).

  4. A. Togo, A. Seko, On-the-fly training of polynomial machine learning potentials in computing lattice thermal conductivity, J. Chem. Phys. 160, 211001 (2024). [arXiv:2401.17531]

  5. H. Wakai, A. Seko, H. Izuta, T. Nishiyama, I. Tanaka, Predictive power of polynomial machine learning potentials for liquid states in 22 elemental systems, Phys. Rev. B 109, 214207. [arXiv:2401.14877]

  6. H. Wakai, A. Seko, I. Tanaka, Efficient global crystal structure prediction using polynomial machine learning potential in the binary Al-Cu alloy system, J. Ceram. Soc. Japan 131, 762-766 (2023).

  7. T. Naruse, A. Seko, I. Tanaka, Global structure optimization following imaginary phonon modes accelerated by machine learning potentials in Cu, Ag, and Au, J. Ceram. Soc. Japan 131, 746-750 (2023).

  8. K. Kanayama, A. Seko, K. Toyoura, Structure search method for atomic clusters based on the dividing rectangles algorithm, Phys. Rev. E 108, 035303 (2023).

  9. A. Seko, Tutorial: Systematic development of polynomial machine learning potentials for elemental and alloy systems, J. Appl. Phys. 133, 011101 (2023).

  10. H. Hayashi, A. Seko, I. Tanaka, Recommender system for discovery of inorganic compounds, npj Comput. Mater. 8, 217 (2022).

  11. S. Fujii, A. Seko, Structure and lattice thermal conductivity of grain boundaries in silicon by using machine learning potential and molecular dynamics, Comput. Mater. Sci. 204, 111137 (2022)

  12. K. Shinohara, A. Seko, T. Horiyama, I. Tanaka, Finding well-optimized special quasirandom structures with decision diagram, Phys. Rev. Materials 5, 113803 (2021)

  13. Y. Koyama, A. Seko, I. Tanaka, S. Funahashi, N. Hirosaki, Combination of recommender system and single-particle diagnosis for accelerated discovery of novel nitrides, J. Chem. Phys. 154, 224117 (2021)

  14. A. Seko, Machine learning potential repository, [arXiv:2007.14206]

  15. A. Seko, Machine learning potentials for multicomponent systems: The Ti-Al binary system, Phys. Rev. B 102, 174104 (2020) [arXiv:2008.09750]

  16. T. Nishiyama, A. Seko, and I. Tanaka, Application of machine learning potentials to predict grain boundary properties in fcc elemental metals, Phys. Rev. Materials 4, 123607 (2020) [arXiv:2007.15944]

  17. K. Shinohara, A. Seko, T. Horiyama, M. Ishihata, J. Honda and I. Tanaka, Enumeration of nonequivalent substitutional structures using advanced data structure of binary decision diagram, J. Chem. Phys. 153, 104109 (2020) [arXiv:2002.12603]

  18. K. Suzuki, K. Ohura, A. Seko, Y. Iwamizu, G. Zhao, M. Hirayama, I. Tanaka and R. Kanno, Fast material search of lithium ion conducting oxides using a recommender system, J. Mater. Chem. A 8, 11582-11588 (2020)

  19. A. Seko and S. Ishiwata, Prediction of perovskite-related structures in ACuO3-x(A = Ca, Sr, Ba, Sc, Y, La) using density functional theory and Bayesian optimization, Phys. Rev. B 101, 134101 (2020) (Editors' Suggestion) [arXiv:2001.09312]

  20. H. Hayashi, K. Hayashi, K. Kouzai, A. Seko and I. Tanaka, Recommender System of Successful Processing Conditions for New Compounds Based on a Parallel Experimental Data Set, Chem. Mater. 31 9984--9992 (2019)

  21. A. Seko, A. Togo and I. Tanaka, Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential, Phys. Rev. B 99, 214108 (2019) [arXiv:1901.02118]

  22. A. Seko, K. Toyoura, S. Muto, T. Mizoguchi and S. Broderick, Progress in nanoinformatics and informational materials science, MRS Bulletin 43, 690--695 (2018).

  23. A. Takahashi, A. Seko and I. Tanaka, Linearized machine-learning interatomic potentials for non-magnetic elemental metals: Limitation of pairwise descriptors and trend of predictive power, J. Chem. Phys. 148, 234106 (2018). [arXiv:1710.05677]

  24. A. Seko, H. Hayashi, and I. Tanaka, Compositional descriptor-based recommender system for the materials discovery, J. Chem. Phys. 148, 241719 (2018). [arXiv:1711.06387]

  25. K. Kanamori, K. Toyoura, J. Honda, K. Hattori, A. Seko, M. Karasuyama, K. Shitara, M. Shiga, A. Kuwabara, and I. Takeuchi, Exploring Potential Energy Surface by Machine Learning for Characterizing Atomic Transport, Phys. Rev. B 97, 125124 (2018). [arXiv:1710.03468]

  26. A. Seko, H. Hayashi, H. Kashima and I. Tanaka, Matrix- and tensor-based recommender systems for the discovery of currently unknown inorganic compounds, Phys. Rev. Materials 2, 013805 (2018). [arXiv:1710.00659]

  27. Y. Ikeda, F. Kormann, B. Dutta, A. Carreras, A. Seko, J. Neugebauer, I. Tanaka, Temperature-dependent phonon spectra of magnetic random solid solutions, npj Comput. Mater. 4, 7 (2018). [arXiv:1702.02389]

  28. A. Takahashi, A. Seko and I. Tanaka, Conceptual and practical bases for the high accuracy of machine learning interatomic potentials: Application to elemental titanium, Phys. Rev. Materials 1, 063801 (2017). [arXiv:1708.02741]

  29. N. Otani, A. Kuwabara, T. Ogawa, J. Matsuda, A. Seko, I. Tanaka and E. Akiba, Theoretical investigation of solid solution states of Ti1-xVxH2, Acta. Mater. 134, 274-282 (2017).

  30. A. Seko, H. Hayashi, K. Nakayama, A. Takahashi, I. Tanaka, Representation of compounds for machine-learning prediction of physical properties, Phys. Rev. B 95, 144110 (2017).

  31. Y. Ikeda, A. Carreras, A. Seko, A. Togo and I. Tanaka Mode decomposition based on crystallographic symmetry in the band-unfolding method, Phys. Rev. B 95, 024305 (2017).

  32. K. Shitara, T. Moriasa, A. Sumitani, A. Seko, H. Hayashi, Y. Koyama, R. Huang, D. Han, H. Moriwake and I. Tanaka First-Principles Selection of Solute Elements for Er- stabilized Bi2O3Oxide-ion Conductor with Improved Long- term Stability at Moderate Temperatures, Chem. Mater. 29, 3763-3768 (2017).

  33. J. Lee, A. Seko, K. Shitara, K. Nakayama and I. Tanaka, Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques, Phys. Rev. B 93, 115104 (2016).

  34. K. Toyoura, D. Hirano, A. Seko, M. Shiga, A. Kuwabara, M. Karasuyama, K. Shitara and I. Takeuchi, Machine-learning-based selective sampling procedure for identifying the low-energy region in a potential energy surface: A case study on proton conduction in oxides, Phys. Rev. B 93, 054112 (2016).

  35. A. Seko, A. Togo, H. Hayashi, K. Tsuda, L. Chaput and I. Tanaka, Prediction of low-thermal-conductivity compounds with first-principles anharmonic lattice-dynamics calculations and Bayesian optimization, Phys. Rev. Lett. 115, 205901 (2015).

  36. A. Seko, A. Takahashi and I. Tanaka, First-principles interatomic potentials for ten elemental metals via compressed sensing, Phys. Rev. B 92, 054113 (2015).

  37. A. Seko and I. Tanaka, Special quasirandom structure in heterovalent ionic systems, Phys. Rev. B 91, 024106 (2015). [arXiv:1408.6875]

  38. A. Seko, K. Shitara and I. Tanaka, Efficient determination of alloy ground-state structures, Phys. Rev. B 90, 174104 (2014). [arXiv:1407.1734]

  39. Y. Ikeda, A. Seko, A. Togo and I. Tanaka, Phonon softening in paramagnetic body-centered cubic iron and relationship with phase transition, Phys. Rev. B 90, 134106 (2014).

  40. A. Seko, A. Takahashi and I. Tanaka, Sparse representation for a potential energy surface, Phys. Rev. B 90, 024101 (2014). [arXiv:1403.7995]

  41. A. Seko, T. Maekawa, K. Tsuda and I. Tanaka, Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single and binary component solids, Phys. Rev. B 89, 054303 (2014). [arXiv:1310.1546]

  42. A. Seko and I. Tanaka, Cluster expansion of multicomponent ionic systems with controlled accuracy: Importance of long-range interactions in heterovalent ionic system, J. Phys.: Condens. Matter 26 115403 (2014). [arXiv:1309.2516]

  43. T. Yokoyama, F. Oba, A. Seko, H. Hayashi, Y. Nose, and I. Tanaka, Theoretical photovoltaic conversion efficiencies of ZnSnP2, CdSnP2, and Zn1-xCdxSnP2alloys, Appl. Phys. Express 6, 061201-1-3 (2013).

  44. K. Fujimura, A. Seko, Y. Koyama, A. Kuwabara, I. Kishida, K. Shitara, C. A. J. Fisher, H. Moriwake and I. Tanaka, Accelerated materials design of lithium superionic conductors based on first-principles calculations and machine learning algorithms, Adv. Energy Mater. 3, 980-985 (2013).

  45. B. Liu, A. Seko and I. Tanaka, Cluster expansion with controlled accuracy for the MgO/ZnO pseudobinary system via first-principles calculations, Phys. Rev. B 86, 245202 (2012).

  46. A. Seko, Y. Koyama, A. Matsumoto and I. Tanaka, First-principles molecular dynamics study for average structure and oxygen diffusivity at high temperature in cubic Bi2O3, J. Phys.: Condens. Matter 24, 475402 (2012).

  47. Y. Kumagai, Y. Soda, F. Oba, A. Seko and I. Tanaka, First-principles calculations of the phase diagrams and band gaps in CuInSe2-CuGaSe2 and CuInSe2-CuAlSe2 pseudobinary systems, Phys. Rev. B 85, 033203 (2012).

  48. Y. Kumagai, A. Seko, F. Oba and I. Tanaka, Ground-state search in multicomponent magnetic systems, Phys. Rev. B 85, 012401 (2012).

  49. A. Seko and I. Tanaka, Grouping of structures for cluster expansion of multicomponent systems with controlled accuracy, Phys. Rev. B 83, 224111 (2011).

  50. I. Tanaka, A. Togo, A. Seko, F. Oba, Y. Koyama and A. Kuwabara, Thermodynamics and structures of oxide crystals by a systematic set of first principles calculations, J. Mater. Chem, 20, 10335-10344 (2010).

  51. F. Oba, M. Choi, A. Togo, A. Seko and I. Tanaka, Native defects in oxide semiconductors: a density functional approach, J. Phys.: Condens. Matter 22, 384211 (2010).

  52. I. Tanaka, A. Seko, A. Togo, Y. Koyama and F. Oba, Phase relationships and structures of inorganic crystals by a combination of the cluster expansion method and first principles calculations, J. Phys.: Condens. Matter 22, 384207 (2010).

  53. A. Seko, Exploring structures and phase relationships of ceramics from first principles, J. Am. Ceram. Soc. 93, 1201 (2010) (feature article).

  54. A. Seko, F. Oba and I. Tanaka, Classification of spinel structures based on first-principles cluster expansion analysis, Phys. Rev. B 81, 054114 (2010).

  55. A. Seko, Y. Koyama and I. Tanaka, Cluster expansion method for multicomponent systems based on optimal selection of structures for density-functional theory calculations, Phys. Rev. B 80, 165122 (2009). (Editors' Suggestion)

  56. I. Tanaka, A. Kuwabara, K. Yuge, A. Seko, F. Oba and K. Matsunaga, First principles calculations of advanced nitrides, oxides and alloys, Key Eng. Mat. 403, 73-76 (2009).

  57. A. Seko, A. Togo, F. Oba and I. Tanaka, Structure and stability of a homologous series of tin oxides, Phys. Rev. Lett. 100, 045702 (2008).

  58. K. Yuge, A. Seko, Y. Koyama, F. Oba and I. Tanaka, First-principles-based phase diagram of the cubic BNC ternary system, Phys. Rev. B 77, 094121 (2008).

  59. T. Mizoguchi, A. Seko, M. Yoshiya, H. Yoshida, T. Yoshida, W. Y. Ching and I. Tanaka, X-ray absorption near edge structures of disordered Mg1-xZnxO solid solutions, Phys. Rev. B 76, 195125 (2007).

  60. K. Yuge, A. Seko, A. Kuwabara, F. Oba and I. Tanaka, Ordering and segregation of a Cu75Pt25 (111) surface: A first-principles cluster expansion study, Phys. Rev. B 76, 045407 (2007).

  61. K. Yuge, A. Seko, A. Kuwabara, F. Oba and I. Tanaka, First-principles study of bulk ordering and surface segregation in Pt-Rh binary alloys, Phys. Rev. B 74, 174202 (2006).

  62. S. R. Nishitani, A. Seko, K. Yuge and I. Tanaka, Free Energy Calculations of Precipitate Nucleation, Materials Science Forum 539-543, 2395-2400 (2006).

  63. A. Seko, K. Yuge, F. Oba, A. Kuwabara and I. Tanaka, Prediction of ground-state structures and order-disorder phase transitions in II-III spinel oxides: A combined cluster-expansion method and first-principles study, Phys. Rev. B 73, 184117 (2006).

  64. A. Seko, K. Yuge, F. Oba, A. Kuwabara, I. Tanaka and T. Yamamoto, First-principles study of cation disordering in MgAl2O4 spinel with cluster expansion and Monte Carlo simulation, Phys. Rev. B 73, 094116 (2006).

  65. A. Seko, F. Oba, A. Kuwabara and I. Tanaka, Pressure-induced phase transition in ZnO and ZnO-MgO pseudo-binary system: A first principles lattice dynamics study, Phys. Rev. B 72, 024107 (2005).

  66. K. Yuge, A. Seko, I. Tanaka and S. R. Nishitani, First-principles study of the effect of lattice vibrations on Cu nucleation free energy in Fe-Cu alloys, Phys. Rev. B 72, 174201 (2005).

  67. S. R. Nishitani, A. Seko, I. Tanaka, H. Adachi and E. F. Fujita, First principle calculations of nucleation free energy change for bcc Cu precipitates in Fe-Cu system, Solid--Solid Phase Transformations in Inorganic Materials 2005 2, 669-674 (2005).

  68. A. Seko, S. R. Nishitani, I. Tanaka, H. Adachi and E. F. Fujita, First-principle Calculation on Free Energy of Precipitate Nucleation, Calphad 28, 173-176 (2004).

  69. A. Seko, S. R. Nishitani, I. Tanaka and H. Adachi, Precise Calculation of Free Energy on Precipitate Nucleation, J. Japan Inst. Metals, 68, 973-976 (2004).

  70. A. Seko, N. Odagaki, S. R. Nishitani, I. Tanaka and H. Adachi, Free-Energy Calculation of Precipitate Nucleation in an Fe-Cu-Ni Alloy, Mater. Trans. 45, 1978-1981 (2004).

  71. K. Yuge, A. Seko, K. Kobayashi, T. Tatsuoka, S. R. Nishitani and H. Adachi, Vibrational Contribution on Nucleation Free Energy of Cu Precipitates in Fe-Cu System, Mater. Trans. 45, 1473-1477 (2004).