# First-principles lattice thermal conductivity calculation for CsPr(SO4)2 / Pnna (52) / materials id 540686

https://mdr.nims.go.jp/datasets/8364ad05-1652-4e6d-8663-23441e95f204

## File

- [FORCES_FC3.xz](https://mdr.nims.go.jp/filesets/816c4d4d-b7cb-4a02-b294-67caa0f6081e/download) ([Detail](https://mdr.nims.go.jp/filesets/816c4d4d-b7cb-4a02-b294-67caa0f6081e.md))
- [LTC-calc.log](https://mdr.nims.go.jp/filesets/be8bd782-7ecf-4410-ab64-1efb3feada1d/download) ([Detail](https://mdr.nims.go.jp/filesets/be8bd782-7ecf-4410-ab64-1efb3feada1d.md))
- [phono3py_mlp_eval_fc3_disp.yaml.xz](https://mdr.nims.go.jp/filesets/a201e65a-0f4d-4d39-a078-ae2961102274/download) ([Detail](https://mdr.nims.go.jp/filesets/a201e65a-0f4d-4d39-a078-ae2961102274.md))
- [phonopy_mlp_eval_fc2_dataset.yaml.xz](https://mdr.nims.go.jp/filesets/47ba51ab-0cd4-452d-8531-3f9d55791913/download) ([Detail](https://mdr.nims.go.jp/filesets/47ba51ab-0cd4-452d-8531-3f9d55791913.md))
- [phonopy_training_dataset.yaml.xz](https://mdr.nims.go.jp/filesets/4b5f6676-7083-439e-9442-e692cc4adc3b/download) ([Detail](https://mdr.nims.go.jp/filesets/4b5f6676-7083-439e-9442-e692cc4adc3b.md))
- [polymlp.yaml.xz](https://mdr.nims.go.jp/filesets/e59f8e14-f7b3-4ec1-a591-e856b3881854/download) ([Detail](https://mdr.nims.go.jp/filesets/e59f8e14-f7b3-4ec1-a591-e856b3881854.md))
- [vasp-settings.tar.xz](https://mdr.nims.go.jp/filesets/f4f3c771-0649-40e4-85a8-c0fadd1643ca/download) ([Detail](https://mdr.nims.go.jp/filesets/f4f3c771-0649-40e4-85a8-c0fadd1643ca.md))
- [band_pdos.png](https://mdr.nims.go.jp/filesets/082795df-bf3c-4890-891a-8cd6dbe7e2b5/download) ([Detail](https://mdr.nims.go.jp/filesets/082795df-bf3c-4890-891a-8cd6dbe7e2b5.md))

## Id

8364ad05-1652-4e6d-8663-23441e95f204

## Local identifier

identifier: MDR-LTC-2026Jan9/mp-540686

## Visibility

open_to_public

## State

published

## Created at

2026-01-15T06:13:11.678628Z

## Updated at

2026-01-24T05:19:05.638272Z

## Published at

2026-01-24T01:52:58.887319Z

## 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 CsPr(SO4)2
    / Pnna (52) / materials id 540686
  title_type: original
  lang: en

## Description

- description: |
    Input data used to calculate the lattice thermal conductivities of
    CsPr(SO4)2.
  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
    CsPr(SO4)2.
  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: CsPr(SO4)2
  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: CsPr(SO4)2
  description: CsPr(SO4)2

## Chemical composition

- identifier: CsPr(SO4)2
  description: CsPr(SO4)2

## Structure for specimen

- description: CsPr(SO4)2
  category_description: CsPr(SO4)2

## 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: 23955204
  md5: ba1a17400cdd4d61805f0aa60283c606
- id: be8bd782-7ecf-4410-ab64-1efb3feada1d
  filename: LTC-calc.log
  content_type: text/x-log
  size: 433991
  md5: bbaef6589e8bde999a96f3c3487564af
- id: a201e65a-0f4d-4d39-a078-ae2961102274
  filename: phono3py_mlp_eval_fc3_disp.yaml.xz
  content_type: application/x-xz
  size: 35000
  md5: 2f7d65872ab24f4b97644bd7a268d269
- id: 47ba51ab-0cd4-452d-8531-3f9d55791913
  filename: phonopy_mlp_eval_fc2_dataset.yaml.xz
  content_type: application/x-xz
  size: 2417432
  md5: b2ba624eb0020a204f18a4875116a0fc
- id: 4b5f6676-7083-439e-9442-e692cc4adc3b
  filename: phonopy_training_dataset.yaml.xz
  content_type: application/x-xz
  size: 709176
  md5: 61c8f39cfa006d9acf72fad846c1289b
- id: e59f8e14-f7b3-4ec1-a591-e856b3881854
  filename: polymlp.yaml.xz
  content_type: application/x-xz
  size: 844120
  md5: ffb534b8ab1dc54d33514b1db266371d
- id: f4f3c771-0649-40e4-85a8-c0fadd1643ca
  filename: vasp-settings.tar.xz
  content_type: application/x-xz
  size: 592
  md5: 055c0ee262f072845bcf5af4350f5fd2
- id: '082795df-bf3c-4890-891a-8cd6dbe7e2b5'
  filename: band_pdos.png
  content_type: image/png
  size: 68970
  md5: 4a3dec9f790b67d196d879de66e4eb77

## Thumbnail

fileset_id: '082795df-bf3c-4890-891a-8cd6dbe7e2b5'
filename: band_pdos.png