# First-principles lattice thermal conductivity calculation for Sm(ErSe2)3 / P2_1/m (11) / materials id 13282 https://mdr.nims.go.jp/datasets/2cd3caa1-864a-41f5-a262-c9cff3cd40a8 ## File - [FORCES_FC3.xz](https://mdr.nims.go.jp/filesets/860850a8-1268-41b2-b9e1-c8d6503d7508/download) ([Detail](https://mdr.nims.go.jp/filesets/860850a8-1268-41b2-b9e1-c8d6503d7508.md)) - [LTC-calc.log](https://mdr.nims.go.jp/filesets/9db880cc-d6c4-46c0-b0e9-f3627a8fe6ec/download) ([Detail](https://mdr.nims.go.jp/filesets/9db880cc-d6c4-46c0-b0e9-f3627a8fe6ec.md)) - [phono3py_mlp_eval_fc3_disp.yaml.xz](https://mdr.nims.go.jp/filesets/1e852528-1709-47e8-be62-13a7fdff9a69/download) ([Detail](https://mdr.nims.go.jp/filesets/1e852528-1709-47e8-be62-13a7fdff9a69.md)) - [phonopy_mlp_eval_fc2_dataset.yaml.xz](https://mdr.nims.go.jp/filesets/51e31a1e-7b0c-4daa-a304-585e8e47daf0/download) ([Detail](https://mdr.nims.go.jp/filesets/51e31a1e-7b0c-4daa-a304-585e8e47daf0.md)) - [phonopy_training_dataset.yaml.xz](https://mdr.nims.go.jp/filesets/32f40e83-c3a0-417a-9d0d-2b26a4b30e26/download) ([Detail](https://mdr.nims.go.jp/filesets/32f40e83-c3a0-417a-9d0d-2b26a4b30e26.md)) - [polymlp.yaml.xz](https://mdr.nims.go.jp/filesets/3d517508-330f-4516-9c00-7700fac14c04/download) ([Detail](https://mdr.nims.go.jp/filesets/3d517508-330f-4516-9c00-7700fac14c04.md)) - [vasp-settings.tar.xz](https://mdr.nims.go.jp/filesets/f7dc00f7-279f-41cc-bec5-9e6c12186973/download) ([Detail](https://mdr.nims.go.jp/filesets/f7dc00f7-279f-41cc-bec5-9e6c12186973.md)) - [band_pdos.png](https://mdr.nims.go.jp/filesets/63fb5df1-ebd6-4b58-99be-13f2735aa488/download) ([Detail](https://mdr.nims.go.jp/filesets/63fb5df1-ebd6-4b58-99be-13f2735aa488.md)) ## Id 2cd3caa1-864a-41f5-a262-c9cff3cd40a8 ## Local identifier identifier: MDR-LTC-2026Jan9/mp-13282 ## Visibility open_to_public ## State published ## Created at 2026-01-15T07:05:01.161016Z ## Updated at 2026-01-24T02:46:18.018385Z ## Published at 2026-01-24T01:52:07.486003Z ## 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 Sm(ErSe2)3 / P2_1/m (11) / materials id 13282 title_type: original lang: en ## Description - description: | Input data used to calculate the lattice thermal conductivities of Sm(ErSe2)3. 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 Sm(ErSe2)3. 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: Sm(ErSe2)3 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: Sm(ErSe2)3 description: Sm(ErSe2)3 ## Chemical composition - identifier: Sm(ErSe2)3 description: Sm(ErSe2)3 ## Structure for specimen - description: Sm(ErSe2)3 category_description: Sm(ErSe2)3 ## 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: 860850a8-1268-41b2-b9e1-c8d6503d7508 filename: FORCES_FC3.xz content_type: application/x-xz size: 31191800 md5: 12c22ba33fb5acfcd59f0bc1eaf25241 - id: 9db880cc-d6c4-46c0-b0e9-f3627a8fe6ec filename: LTC-calc.log content_type: text/x-log size: 378383 md5: cdf7c2165b59fce6b647907bf2f555c0 - id: 1e852528-1709-47e8-be62-13a7fdff9a69 filename: phono3py_mlp_eval_fc3_disp.yaml.xz content_type: application/x-xz size: 49328 md5: e537aa2b9f57fad7be8b305650f83e4b - id: 51e31a1e-7b0c-4daa-a304-585e8e47daf0 filename: phonopy_mlp_eval_fc2_dataset.yaml.xz content_type: application/x-xz size: 2613920 md5: cd486bdd8588892901675cb0123134c5 - id: 32f40e83-c3a0-417a-9d0d-2b26a4b30e26 filename: phonopy_training_dataset.yaml.xz content_type: application/x-xz size: 844052 md5: dcfecfc3e8f0e061654d36579fd493b9 - id: 3d517508-330f-4516-9c00-7700fac14c04 filename: polymlp.yaml.xz content_type: application/x-xz size: 282372 md5: ae83dc9bb9470d3924377a0c8b1fff5d - id: f7dc00f7-279f-41cc-bec5-9e6c12186973 filename: vasp-settings.tar.xz content_type: application/x-xz size: 564 md5: d04465362a9914170a9ea3efce2320a6 - id: 63fb5df1-ebd6-4b58-99be-13f2735aa488 filename: band_pdos.png content_type: image/png size: 124637 md5: c446aef1d06e0a7e5f41d8cee11a7afd ## Thumbnail fileset_id: 63fb5df1-ebd6-4b58-99be-13f2735aa488 filename: band_pdos.png