# First-principles lattice thermal conductivity calculation for Al5C3N / P6_3/mmc (194) / materials id 5496

https://mdr.nims.go.jp/datasets/8e26bb44-0ec7-4cf6-9230-94ed92b02dd2

## File

- [FORCES_FC3.xz](https://mdr.nims.go.jp/filesets/ca8f388e-d994-43a1-ad27-2ff804442c78/download) ([Detail](https://mdr.nims.go.jp/filesets/ca8f388e-d994-43a1-ad27-2ff804442c78.md))
- [LTC-calc.log](https://mdr.nims.go.jp/filesets/37894c62-c5d5-4d92-9098-e17ef9151040/download) ([Detail](https://mdr.nims.go.jp/filesets/37894c62-c5d5-4d92-9098-e17ef9151040.md))
- [phono3py_mlp_eval_fc3_disp.yaml.xz](https://mdr.nims.go.jp/filesets/d9416509-1543-4660-9a67-b4218a2223c2/download) ([Detail](https://mdr.nims.go.jp/filesets/d9416509-1543-4660-9a67-b4218a2223c2.md))
- [phonopy_mlp_eval_fc2_dataset.yaml.xz](https://mdr.nims.go.jp/filesets/3bf7363e-1060-4ef5-aafb-4146264357b1/download) ([Detail](https://mdr.nims.go.jp/filesets/3bf7363e-1060-4ef5-aafb-4146264357b1.md))
- [phonopy_training_dataset.yaml.xz](https://mdr.nims.go.jp/filesets/6bf84f05-eda1-4b51-8195-24f0021dc22f/download) ([Detail](https://mdr.nims.go.jp/filesets/6bf84f05-eda1-4b51-8195-24f0021dc22f.md))
- [polymlp.yaml.xz](https://mdr.nims.go.jp/filesets/290ca51a-283d-4d05-b30d-9693e09771ef/download) ([Detail](https://mdr.nims.go.jp/filesets/290ca51a-283d-4d05-b30d-9693e09771ef.md))
- [vasp-settings.tar.xz](https://mdr.nims.go.jp/filesets/6c8d60d5-ce77-4a97-92d7-b4a6b06a7229/download) ([Detail](https://mdr.nims.go.jp/filesets/6c8d60d5-ce77-4a97-92d7-b4a6b06a7229.md))
- [band_pdos.png](https://mdr.nims.go.jp/filesets/ed5cf31e-2e76-44e7-8bda-7b646ab6feb7/download) ([Detail](https://mdr.nims.go.jp/filesets/ed5cf31e-2e76-44e7-8bda-7b646ab6feb7.md))

## Id

8e26bb44-0ec7-4cf6-9230-94ed92b02dd2

## Local identifier

identifier: MDR-LTC-2026Jan9/mp-5496

## Visibility

open_to_public

## State

published

## Created at

2026-01-15T06:51:49.952514Z

## Updated at

2026-01-24T01:58:35.925020Z

## Published at

2026-01-24T01:53:04.449586Z

## 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 Al5C3N / P6_3/mmc
    (194) / materials id 5496
  title_type: original
  lang: en

## Description

- description: |
    Input data used to calculate the lattice thermal conductivities of
    Al5C3N.
  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
    Al5C3N.
  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: Al5C3N
  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: Al5C3N
  description: Al5C3N

## Chemical composition

- identifier: Al5C3N
  description: Al5C3N

## Structure for specimen

- description: Al5C3N
  category_description: Al5C3N

## 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: ca8f388e-d994-43a1-ad27-2ff804442c78
  filename: FORCES_FC3.xz
  content_type: application/x-xz
  size: 2684636
  md5: 88fc085ab4a1f73c41c1a4661cff963f
- id: 37894c62-c5d5-4d92-9098-e17ef9151040
  filename: LTC-calc.log
  content_type: text/x-log
  size: 256287
  md5: 8c78dd04047d9aeda55964ecaeb2b24e
- id: d9416509-1543-4660-9a67-b4218a2223c2
  filename: phono3py_mlp_eval_fc3_disp.yaml.xz
  content_type: application/x-xz
  size: 12436
  md5: 7b246dd2301bcb3daf0c1bbf362b02b9
- id: 3bf7363e-1060-4ef5-aafb-4146264357b1
  filename: phonopy_mlp_eval_fc2_dataset.yaml.xz
  content_type: application/x-xz
  size: 403948
  md5: 5a5b90f97e7ed3d237ea49ea398c7d6b
- id: 6bf84f05-eda1-4b51-8195-24f0021dc22f
  filename: phonopy_training_dataset.yaml.xz
  content_type: application/x-xz
  size: 519016
  md5: 4ac12323acf5331205a8e7d1bb13252d
- id: 290ca51a-283d-4d05-b30d-9693e09771ef
  filename: polymlp.yaml.xz
  content_type: application/x-xz
  size: 314340
  md5: 485bae73e89d8f417336715650518d88
- id: 6c8d60d5-ce77-4a97-92d7-b4a6b06a7229
  filename: vasp-settings.tar.xz
  content_type: application/x-xz
  size: 580
  md5: 044e76157559089ee9916f6737a985f6
- id: ed5cf31e-2e76-44e7-8bda-7b646ab6feb7
  filename: band_pdos.png
  content_type: image/png
  size: 102829
  md5: 2ab26e299fc532c382199d7607fe1cc1

## Thumbnail

fileset_id: ed5cf31e-2e76-44e7-8bda-7b646ab6feb7
filename: band_pdos.png