# First-principles lattice thermal conductivity calculation for SrNd2O4 / Pnma (62) / materials id 755405 https://mdr.nims.go.jp/datasets/fde7ec55-293a-40a8-aaee-ba3cd87f3ca6 ## Files - [FORCES_FC3.xz](https://mdr.nims.go.jp/filesets/fd7cc9c2-3112-437c-9651-1f8506d44aa8/download) ([Detail](https://mdr.nims.go.jp/filesets/fd7cc9c2-3112-437c-9651-1f8506d44aa8.md)) - [LTC-calc.log](https://mdr.nims.go.jp/filesets/8c732491-1846-41d0-8a08-4604c09489e4/download) ([Detail](https://mdr.nims.go.jp/filesets/8c732491-1846-41d0-8a08-4604c09489e4.md)) - [phono3py_mlp_eval_fc3_disp.yaml.xz](https://mdr.nims.go.jp/filesets/cae21c47-e5f1-43dd-8348-a5041786e473/download) ([Detail](https://mdr.nims.go.jp/filesets/cae21c47-e5f1-43dd-8348-a5041786e473.md)) - [phonopy_mlp_eval_fc2_dataset.yaml.xz](https://mdr.nims.go.jp/filesets/16b0b3ae-522b-4d4e-8f29-b5bcfdca794e/download) ([Detail](https://mdr.nims.go.jp/filesets/16b0b3ae-522b-4d4e-8f29-b5bcfdca794e.md)) - [phonopy_training_dataset.yaml.xz](https://mdr.nims.go.jp/filesets/d7e38780-b09d-4038-bd80-3d067175df17/download) ([Detail](https://mdr.nims.go.jp/filesets/d7e38780-b09d-4038-bd80-3d067175df17.md)) - [polymlp.yaml.xz](https://mdr.nims.go.jp/filesets/872b98a4-0d47-44ed-bbff-af7974140320/download) ([Detail](https://mdr.nims.go.jp/filesets/872b98a4-0d47-44ed-bbff-af7974140320.md)) - [vasp-settings.tar.xz](https://mdr.nims.go.jp/filesets/22d0ed37-48f8-4780-b2ba-ebd0188ae8fe/download) ([Detail](https://mdr.nims.go.jp/filesets/22d0ed37-48f8-4780-b2ba-ebd0188ae8fe.md)) - [band_pdos.png](https://mdr.nims.go.jp/filesets/f2a41af8-767b-43eb-8e3d-7542db39076d/download) ([Detail](https://mdr.nims.go.jp/filesets/f2a41af8-767b-43eb-8e3d-7542db39076d.md)) ## Id fde7ec55-293a-40a8-aaee-ba3cd87f3ca6 ## Local identifier identifier: MDR-LTC-2026Jan9/mp-755405 ## Visibility open_to_public ## State published ## Created at 2026-01-15T06:37:15.000079Z ## Updated at 2026-01-24T06:11:13.878190Z ## Published at 2026-01-24T01:53:29.200940Z ## Doi ## First published url ## Date published ## Recorded date published ## Resource type dataset ## Manuscript type na ## Collection - id: 0113dccc-ec45-42ed-86db-f455f9b63fb1 identifier: https://mdr.nims.go.jp/pid/0113dccc-ec45-42ed-86db-f455f9b63fb1 title: MDR lattice thermal conductivity calculation database ## Title - title: First-principles lattice thermal conductivity calculation for SrNd2O4 / Pnma (62) / materials id 755405 title_type: original lang: en ## Description - description: | Input data used to calculate the lattice thermal conductivities of SrNd2O4. description_type: abstract lang: en - description: | Initial geometry optimization of the conventional unit cell, standardized by the spglib code, was performed using the VASP code with the PBEsol exchange-correlation functional. Supercell forces and energies were calculated using the VASP code, and these data were used to develop polynomial machine learning potentials (MLPs) with the pypolymlp code. The generated MLPs are stored in polymlp.yaml.xz. Parameters required for the non-analytical term correction (Born effective charges and dielectric constants) were calculated using the VASP code with the primitive cell. These VASP results are provided in phonopy_training_dataset.yaml.xz, and the VASP input configurations can be found in vasp-settings.tar.xz. The primitive cell, unit cell, and supercell structures used for the VASP calculations are also provided in phonopy_training_dataset.yaml.xz. The internal atomic positions of the supercell were then optimized using the pypolymlp code under symmetry constraints; the relaxed structure can be found in phonopy_mlp_eval_fc2_dataset.yaml.xz (or phono3py_mlp_eval_fc3_disp.yaml.xz). Second-order force constants (fc2) can be calculated using the phonopy and symfc codes with the displacement–force dataset evaluated by the pypolymlp code, which is stored in phonopy_mlp_eval_fc2_dataset.yaml.xz. Third-order force constants (fc3) can be calculated using the built-in finite difference approach in the phono3py code with the displacement–force dataset stored in phono3py_mlp_eval_fc3_disp.yaml.xz (displacements) and FORCES_FC3.xz (forces). As an example, lattice thermal conductivities (LTCs) were calculated using the phono3py code with fc2 and fc3, and the calculation log is provided in LTC-calc.log. The harmonic phonon band structure and density of states are plotted in band_pdos.png. The band path was generated using the SeeK-path code. description_type: abstract lang: en - description: | Input data used to calculate the lattice thermal conductivities of SrNd2O4. description_type: abstract lang: en - description: | Initial geometry optimization of the conventional unit cell, standardized by the spglib code, was performed using the VASP code with the PBEsol exchange-correlation functional. Supercell forces and energies were calculated using the VASP code, and these data were used to develop polynomial machine learning potentials (MLPs) with the pypolymlp code. The generated MLPs are stored in polymlp.yaml.xz. Parameters required for the non-analytical term correction (Born effective charges and dielectric constants) were calculated using the VASP code with the primitive cell. These VASP results are provided in phonopy_training_dataset.yaml.xz, and the VASP input configurations can be found in vasp-settings.tar.xz. The primitive cell, unit cell, and supercell structures used for the VASP calculations are also provided in phonopy_training_dataset.yaml.xz. The internal atomic positions of the supercell were then optimized using the pypolymlp code under symmetry constraints; the relaxed structure can be found in phonopy_mlp_eval_fc2_dataset.yaml.xz (or phono3py_mlp_eval_fc3_disp.yaml.xz). Second-order force constants (fc2) can be calculated using the phonopy and symfc codes with the displacement–force dataset evaluated by the pypolymlp code, which is stored in phonopy_mlp_eval_fc2_dataset.yaml.xz. Third-order force constants (fc3) can be calculated using the built-in finite difference approach in the phono3py code with the displacement–force dataset stored in phono3py_mlp_eval_fc3_disp.yaml.xz (displacements) and FORCES_FC3.xz (forces). As an example, lattice thermal conductivities (LTCs) were calculated using the phono3py code with fc2 and fc3, and the calculation log is provided in LTC-calc.log. The harmonic phonon band structure and density of states are plotted in band_pdos.png. The band path was generated using the SeeK-path code. description_type: abstract lang: en ## Creator - name: Atsushi Togo role: author orcid: https://orcid.org/0000-0001-8393-9766 organization: National Institute for Materials Science department: Center for Basic Research on Materials ror: https://ror.org/026v1ze26 ## Contact agent - name: Atsushi Togo email: togo.atsushi@nims.go.jp organization: National Institute for Materials Science ror: https://ror.org/026v1ze26 ## Publisher organization: NIMS ror: https://ror.org/026v1ze26 ## Managing organization organization: National Institute for Materials Science department: CBRM ror: https://ror.org/026v1ze26 ## Keyword - subject: Lattice thermal conductivity schema: not_defined - subject: SrNd2O4 schema: not_defined ## Rights - description: Creative Commons Attribution 4.0 International identifier: https://creativecommons.org/licenses/by/4.0/ ## Other identifier(s) ## Data origin - data_origin_type: simulation ## Embargo ## Journal ## Conference ## Related item ## Funding ## Instrument ## Instrument operator ## Instrument managing organization ## Measurement method ## Specimen - name: SrNd2O4 description: SrNd2O4 ## Chemical composition - identifier: SrNd2O4 description: SrNd2O4 ## Structure for specimen - description: SrNd2O4 category_description: SrNd2O4 ## Structural feature for specimen ## Specific property for specimen ## Process for specimen treatment ## Computational method ## Energy level/transition state ## Software - name: phono3py identifier: https://github.com/phonopy/phono3py - name: phonopy identifier: https://github.com/phonopy/phonopy - name: spglib identifier: https://github.com/spglib/spglib - name: symfc identifier: https://github.com/symfc/symfc - name: pypolymlp identifier: https://github.com/sekocha/pypolymlp - name: VASP identifier: https://www.vasp.at/ - name: Seek-path identifier: https://github.com/giovannipizzi/seekpath ## Custom property ## Fileset - id: fd7cc9c2-3112-437c-9651-1f8506d44aa8 filename: FORCES_FC3.xz content_type: application/x-xz size: 11066684 md5: b333c73fd4049debbefb32a97902d27a - id: 8c732491-1846-41d0-8a08-4604c09489e4 filename: LTC-calc.log content_type: text/x-log size: 398549 md5: a34d069010ffbd1e77566fb8f4b1884a - id: cae21c47-e5f1-43dd-8348-a5041786e473 filename: phono3py_mlp_eval_fc3_disp.yaml.xz content_type: application/x-xz size: 28496 md5: 3fe1b09b0363d76ac9c806a94efca3d6 - id: 16b0b3ae-522b-4d4e-8f29-b5bcfdca794e filename: phonopy_mlp_eval_fc2_dataset.yaml.xz content_type: application/x-xz size: 1386940 md5: 7d86725300e41b70bf8e0760b696dd59 - id: d7e38780-b09d-4038-bd80-3d067175df17 filename: phonopy_training_dataset.yaml.xz content_type: application/x-xz size: 596296 md5: e4de36db512b133049c10cfb77111cc5 - id: 872b98a4-0d47-44ed-bbff-af7974140320 filename: polymlp.yaml.xz content_type: application/x-xz size: 298244 md5: afea9f89a16730ae34f6490aed06b7cc - id: 22d0ed37-48f8-4780-b2ba-ebd0188ae8fe filename: vasp-settings.tar.xz content_type: application/x-xz size: 588 md5: '091b00929965ac89e28c4304ef31189d' - id: f2a41af8-767b-43eb-8e3d-7542db39076d filename: band_pdos.png content_type: image/png size: 134122 md5: 5116353ee47360a283876f7811826dd9 ## Thumbnail fileset_id: f2a41af8-767b-43eb-8e3d-7542db39076d filename: band_pdos.png