# First-principles lattice thermal conductivity calculation for CaLaGaO4 / Pnma (62) / materials id 559969

https://mdr.nims.go.jp/datasets/8e66750c-86ac-4d8e-8da2-2cab64ed8558

## File

- [FORCES_FC3.xz](https://mdr.nims.go.jp/filesets/d0c3288e-4b27-45d8-a48f-ae36a6310afc/download) ([Detail](https://mdr.nims.go.jp/filesets/d0c3288e-4b27-45d8-a48f-ae36a6310afc.md))
- [LTC-calc.log](https://mdr.nims.go.jp/filesets/abf4b8b5-218f-47fa-a300-5ebd3989fec9/download) ([Detail](https://mdr.nims.go.jp/filesets/abf4b8b5-218f-47fa-a300-5ebd3989fec9.md))
- [phono3py_mlp_eval_fc3_disp.yaml.xz](https://mdr.nims.go.jp/filesets/0a3e8dcd-2308-4d58-b411-78c5664cb281/download) ([Detail](https://mdr.nims.go.jp/filesets/0a3e8dcd-2308-4d58-b411-78c5664cb281.md))
- [phonopy_mlp_eval_fc2_dataset.yaml.xz](https://mdr.nims.go.jp/filesets/911adf7e-a8e1-4d94-8c0b-801ad3f7b173/download) ([Detail](https://mdr.nims.go.jp/filesets/911adf7e-a8e1-4d94-8c0b-801ad3f7b173.md))
- [phonopy_training_dataset.yaml.xz](https://mdr.nims.go.jp/filesets/6475c972-ec1c-4e02-8d0b-6d5e38f3db43/download) ([Detail](https://mdr.nims.go.jp/filesets/6475c972-ec1c-4e02-8d0b-6d5e38f3db43.md))
- [polymlp.yaml.xz](https://mdr.nims.go.jp/filesets/dd90aa44-e1f7-4048-83e9-446798d7541a/download) ([Detail](https://mdr.nims.go.jp/filesets/dd90aa44-e1f7-4048-83e9-446798d7541a.md))
- [vasp-settings.tar.xz](https://mdr.nims.go.jp/filesets/6fb83bf6-d79d-4a49-a86a-29f676ef91de/download) ([Detail](https://mdr.nims.go.jp/filesets/6fb83bf6-d79d-4a49-a86a-29f676ef91de.md))
- [band_pdos.png](https://mdr.nims.go.jp/filesets/75a77c17-4e2b-4238-a747-612778b6e0a5/download) ([Detail](https://mdr.nims.go.jp/filesets/75a77c17-4e2b-4238-a747-612778b6e0a5.md))

## Id

8e66750c-86ac-4d8e-8da2-2cab64ed8558

## Local identifier

identifier: MDR-LTC-2026Jan9/mp-559969

## Visibility

open_to_public

## State

published

## Created at

2026-01-15T06:32:53.321940Z

## Updated at

2026-01-24T06:03:00.291476Z

## Published at

2026-01-24T01:53:04.424071Z

## 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 CaLaGaO4 /
    Pnma (62) / materials id 559969
  title_type: original
  lang: en

## Description

- description: |
    Input data used to calculate the lattice thermal conductivities of
    CaLaGaO4.
  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
    CaLaGaO4.
  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: CaLaGaO4
  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: CaLaGaO4
  description: CaLaGaO4

## Chemical composition

- identifier: CaLaGaO4
  description: CaLaGaO4

## Structure for specimen

- description: CaLaGaO4
  category_description: CaLaGaO4

## 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: 19857684
  md5: e3db9c04c5c76a56bae47236f66e8bd8
- id: abf4b8b5-218f-47fa-a300-5ebd3989fec9
  filename: LTC-calc.log
  content_type: text/x-log
  size: 348634
  md5: dc1e8178cb4f7aac25044749878af58d
- id: 0a3e8dcd-2308-4d58-b411-78c5664cb281
  filename: phono3py_mlp_eval_fc3_disp.yaml.xz
  content_type: application/x-xz
  size: 34020
  md5: 6de4753c5355bc5030265e68f2716a12
- id: 911adf7e-a8e1-4d94-8c0b-801ad3f7b173
  filename: phonopy_mlp_eval_fc2_dataset.yaml.xz
  content_type: application/x-xz
  size: 1856220
  md5: 56fc18209c4e3f0d172aa83546087b08
- id: 6475c972-ec1c-4e02-8d0b-6d5e38f3db43
  filename: phonopy_training_dataset.yaml.xz
  content_type: application/x-xz
  size: 797756
  md5: 728d705ac8135ec88b4e5be05a41531d
- id: dd90aa44-e1f7-4048-83e9-446798d7541a
  filename: polymlp.yaml.xz
  content_type: application/x-xz
  size: 896240
  md5: debd86e2374eb50592b9291077c243aa
- id: 6fb83bf6-d79d-4a49-a86a-29f676ef91de
  filename: vasp-settings.tar.xz
  content_type: application/x-xz
  size: 580
  md5: 917577b7fecb7c0c227d36ed282f2796
- id: 75a77c17-4e2b-4238-a747-612778b6e0a5
  filename: band_pdos.png
  content_type: image/png
  size: 121679
  md5: cba00cc7aa7337e0cc7fa2af49114e20

## Thumbnail

fileset_id: 75a77c17-4e2b-4238-a747-612778b6e0a5
filename: band_pdos.png