# First-principles lattice thermal conductivity calculation for DyCuPbSe3 / Pnma (62) / materials id 601257

https://mdr.nims.go.jp/datasets/5e73e8e8-7a8c-4ebf-b22c-3a01aec7ee09

## File

- [FORCES_FC3.xz](https://mdr.nims.go.jp/filesets/96889107-1cbe-4fa2-9ca1-fd14a0e3e2aa/download) ([Detail](https://mdr.nims.go.jp/filesets/96889107-1cbe-4fa2-9ca1-fd14a0e3e2aa.md))
- [LTC-calc.log](https://mdr.nims.go.jp/filesets/cbded08f-34a2-4182-b0c5-6b0dcf340c0f/download) ([Detail](https://mdr.nims.go.jp/filesets/cbded08f-34a2-4182-b0c5-6b0dcf340c0f.md))
- [phono3py_mlp_eval_fc3_disp.yaml.xz](https://mdr.nims.go.jp/filesets/e74705ff-4008-4b16-bd54-d0f654424957/download) ([Detail](https://mdr.nims.go.jp/filesets/e74705ff-4008-4b16-bd54-d0f654424957.md))
- [phonopy_mlp_eval_fc2_dataset.yaml.xz](https://mdr.nims.go.jp/filesets/d51890b3-c0a3-4cad-a022-6ee248f1b39d/download) ([Detail](https://mdr.nims.go.jp/filesets/d51890b3-c0a3-4cad-a022-6ee248f1b39d.md))
- [phonopy_training_dataset.yaml.xz](https://mdr.nims.go.jp/filesets/f97acbfd-e31c-4f8a-8a9a-d82677df5104/download) ([Detail](https://mdr.nims.go.jp/filesets/f97acbfd-e31c-4f8a-8a9a-d82677df5104.md))
- [polymlp.yaml.xz](https://mdr.nims.go.jp/filesets/0b19aff0-32f2-47c6-93fc-9556f16af1a0/download) ([Detail](https://mdr.nims.go.jp/filesets/0b19aff0-32f2-47c6-93fc-9556f16af1a0.md))
- [vasp-settings.tar.xz](https://mdr.nims.go.jp/filesets/40190d56-1fa7-4cbf-85a0-8867b1788ac9/download) ([Detail](https://mdr.nims.go.jp/filesets/40190d56-1fa7-4cbf-85a0-8867b1788ac9.md))
- [band_pdos.png](https://mdr.nims.go.jp/filesets/c09dde21-7e07-44d5-95d4-cad70132608a/download) ([Detail](https://mdr.nims.go.jp/filesets/c09dde21-7e07-44d5-95d4-cad70132608a.md))

## Id

5e73e8e8-7a8c-4ebf-b22c-3a01aec7ee09

## Local identifier

identifier: MDR-LTC-2026Jan9/mp-601257

## Visibility

open_to_public

## State

published

## Created at

2026-01-15T07:03:00.560281Z

## Updated at

2026-01-24T02:37:23.126440Z

## Published at

2026-01-24T01:52:37.582513Z

## 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 DyCuPbSe3 /
    Pnma (62) / materials id 601257
  title_type: original
  lang: en

## Description

- description: |
    Input data used to calculate the lattice thermal conductivities of
    DyCuPbSe3.
  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
    DyCuPbSe3.
  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: DyCuPbSe3
  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: DyCuPbSe3
  description: DyCuPbSe3

## Chemical composition

- identifier: DyCuPbSe3
  description: DyCuPbSe3

## Structure for specimen

- description: DyCuPbSe3
  category_description: DyCuPbSe3

## 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

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  filename: FORCES_FC3.xz
  content_type: application/x-xz
  size: 25218236
  md5: 6b359410a7b22c2daa398566ba2190c4
- id: cbded08f-34a2-4182-b0c5-6b0dcf340c0f
  filename: LTC-calc.log
  content_type: text/x-log
  size: 329021
  md5: ae2f6ce30d0a08e00d80c8e32ce903e0
- id: e74705ff-4008-4b16-bd54-d0f654424957
  filename: phono3py_mlp_eval_fc3_disp.yaml.xz
  content_type: application/x-xz
  size: 42252
  md5: 962fe917144b179c491986b9198faf35
- id: d51890b3-c0a3-4cad-a022-6ee248f1b39d
  filename: phonopy_mlp_eval_fc2_dataset.yaml.xz
  content_type: application/x-xz
  size: 1950424
  md5: dda803ace7be0677539cc0f08ab25d68
- id: f97acbfd-e31c-4f8a-8a9a-d82677df5104
  filename: phonopy_training_dataset.yaml.xz
  content_type: application/x-xz
  size: 1008984
  md5: efe09b122f762a284c2e78a97854acdd
- id: 0b19aff0-32f2-47c6-93fc-9556f16af1a0
  filename: polymlp.yaml.xz
  content_type: application/x-xz
  size: 850212
  md5: 1baca6c3b18fc37912e795da142a3728
- id: 40190d56-1fa7-4cbf-85a0-8867b1788ac9
  filename: vasp-settings.tar.xz
  content_type: application/x-xz
  size: 572
  md5: 72bd5441e3e51661e918c766b89c2f01
- id: c09dde21-7e07-44d5-95d4-cad70132608a
  filename: band_pdos.png
  content_type: image/png
  size: 126140
  md5: d28c2e2ca83fe85ef793238a627a7f57

## Thumbnail

fileset_id: c09dde21-7e07-44d5-95d4-cad70132608a
filename: band_pdos.png