# First-principles lattice thermal conductivity calculation for K3AsS4 / Pnma (62) / materials id 3797

https://mdr.nims.go.jp/datasets/cc31fd96-6f48-4565-91f4-d6e10944927d

## File

- [FORCES_FC3.xz](https://mdr.nims.go.jp/filesets/3f723cd4-a410-4f3f-a316-fbc20c57f0a0/download) ([Detail](https://mdr.nims.go.jp/filesets/3f723cd4-a410-4f3f-a316-fbc20c57f0a0.md))
- [LTC-calc.log](https://mdr.nims.go.jp/filesets/71b4171c-f85d-4afa-93d1-1cf6f8422dd1/download) ([Detail](https://mdr.nims.go.jp/filesets/71b4171c-f85d-4afa-93d1-1cf6f8422dd1.md))
- [phono3py_mlp_eval_fc3_disp.yaml.xz](https://mdr.nims.go.jp/filesets/2b9d1642-a7b6-4507-afdb-eb63921e8bd2/download) ([Detail](https://mdr.nims.go.jp/filesets/2b9d1642-a7b6-4507-afdb-eb63921e8bd2.md))
- [phonopy_mlp_eval_fc2_dataset.yaml.xz](https://mdr.nims.go.jp/filesets/203fc070-d7d8-4a1d-b7fd-a1ea11f41908/download) ([Detail](https://mdr.nims.go.jp/filesets/203fc070-d7d8-4a1d-b7fd-a1ea11f41908.md))
- [phonopy_training_dataset.yaml.xz](https://mdr.nims.go.jp/filesets/fbb480c9-67f2-4041-9335-eca4988b7079/download) ([Detail](https://mdr.nims.go.jp/filesets/fbb480c9-67f2-4041-9335-eca4988b7079.md))
- [polymlp.yaml.xz](https://mdr.nims.go.jp/filesets/39992110-86e6-4857-8a0e-64ed8c29d985/download) ([Detail](https://mdr.nims.go.jp/filesets/39992110-86e6-4857-8a0e-64ed8c29d985.md))
- [vasp-settings.tar.xz](https://mdr.nims.go.jp/filesets/fc8c29e2-ade2-441c-9c8f-84b622f5479d/download) ([Detail](https://mdr.nims.go.jp/filesets/fc8c29e2-ade2-441c-9c8f-84b622f5479d.md))
- [band_pdos.png](https://mdr.nims.go.jp/filesets/0420bd97-ce1a-4529-8126-57c17230a4d7/download) ([Detail](https://mdr.nims.go.jp/filesets/0420bd97-ce1a-4529-8126-57c17230a4d7.md))

## Id

cc31fd96-6f48-4565-91f4-d6e10944927d

## Local identifier

identifier: MDR-LTC-2026Jan9/mp-3797

## Visibility

open_to_public

## State

published

## Created at

2026-01-15T05:50:18.850946Z

## Updated at

2026-01-24T03:33:39.396253Z

## Published at

2026-01-24T01:51:36.256411Z

## 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 K3AsS4 / Pnma
    (62) / materials id 3797
  title_type: original
  lang: en

## Description

- description: |
    Input data used to calculate the lattice thermal conductivities of
    K3AsS4.
  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
    K3AsS4.
  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: K3AsS4
  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: K3AsS4
  description: K3AsS4

## Chemical composition

- identifier: K3AsS4
  description: K3AsS4

## Structure for specimen

- description: K3AsS4
  category_description: K3AsS4

## 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: 3f723cd4-a410-4f3f-a316-fbc20c57f0a0
  filename: FORCES_FC3.xz
  content_type: application/x-xz
  size: 25694604
  md5: 0110aacffaa958a15b2f2b577f5489a8
- id: 71b4171c-f85d-4afa-93d1-1cf6f8422dd1
  filename: LTC-calc.log
  content_type: text/x-log
  size: 266947
  md5: 4e16d8ef0f556ab9827fe6ab78ef2621
- id: 2b9d1642-a7b6-4507-afdb-eb63921e8bd2
  filename: phono3py_mlp_eval_fc3_disp.yaml.xz
  content_type: application/x-xz
  size: 44372
  md5: 5ca7f902c43ce33927fc66e48f0f1cda
- id: 203fc070-d7d8-4a1d-b7fd-a1ea11f41908
  filename: phonopy_mlp_eval_fc2_dataset.yaml.xz
  content_type: application/x-xz
  size: 2091508
  md5: 532638f0defdfa2fbc451b72c228813c
- id: fbb480c9-67f2-4041-9335-eca4988b7079
  filename: phonopy_training_dataset.yaml.xz
  content_type: application/x-xz
  size: 899256
  md5: cf001ad3ba972ed001bc2401822220c8
- id: 39992110-86e6-4857-8a0e-64ed8c29d985
  filename: polymlp.yaml.xz
  content_type: application/x-xz
  size: 273588
  md5: 32a16bb6e3a5bcbc51a8a120f3f9cd84
- id: fc8c29e2-ade2-441c-9c8f-84b622f5479d
  filename: vasp-settings.tar.xz
  content_type: application/x-xz
  size: 572
  md5: dc58d345e84afb9eb2b4b3ef6554dd84
- id: 0420bd97-ce1a-4529-8126-57c17230a4d7
  filename: band_pdos.png
  content_type: image/png
  size: 86429
  md5: 0c61f164dcc40c0a1ce53b36053de585

## Thumbnail

fileset_id: 0420bd97-ce1a-4529-8126-57c17230a4d7
filename: band_pdos.png