# First-principles lattice thermal conductivity calculation for Ca3Si2SnO9 / P2_1/c (14) / materials id 15095

https://mdr.nims.go.jp/datasets/b1a6fd39-2a4a-4cfd-835e-b5a60bbda4a0

## File

- [FORCES_FC3.xz](https://mdr.nims.go.jp/filesets/ee2d48bd-6848-426a-8ba6-563b186381be/download) ([Detail](https://mdr.nims.go.jp/filesets/ee2d48bd-6848-426a-8ba6-563b186381be.md))
- [LTC-calc.log](https://mdr.nims.go.jp/filesets/51ac4b0f-16b7-4f8b-901f-1a0f12e2235e/download) ([Detail](https://mdr.nims.go.jp/filesets/51ac4b0f-16b7-4f8b-901f-1a0f12e2235e.md))
- [phono3py_mlp_eval_fc3_disp.yaml.xz](https://mdr.nims.go.jp/filesets/24c703d2-e771-4ae9-aec4-75f9b2b54a4e/download) ([Detail](https://mdr.nims.go.jp/filesets/24c703d2-e771-4ae9-aec4-75f9b2b54a4e.md))
- [phonopy_mlp_eval_fc2_dataset.yaml.xz](https://mdr.nims.go.jp/filesets/9b57487b-ac18-4dbe-a2e8-97824265d670/download) ([Detail](https://mdr.nims.go.jp/filesets/9b57487b-ac18-4dbe-a2e8-97824265d670.md))
- [phonopy_training_dataset.yaml.xz](https://mdr.nims.go.jp/filesets/3a854149-d2d2-4d15-856d-aa1aee027edc/download) ([Detail](https://mdr.nims.go.jp/filesets/3a854149-d2d2-4d15-856d-aa1aee027edc.md))
- [polymlp.yaml.xz](https://mdr.nims.go.jp/filesets/4de2b94a-8fdd-4499-b552-9d605836a782/download) ([Detail](https://mdr.nims.go.jp/filesets/4de2b94a-8fdd-4499-b552-9d605836a782.md))
- [vasp-settings.tar.xz](https://mdr.nims.go.jp/filesets/d7256722-3cc3-4fd2-a474-112a85a3b41c/download) ([Detail](https://mdr.nims.go.jp/filesets/d7256722-3cc3-4fd2-a474-112a85a3b41c.md))
- [band_pdos.png](https://mdr.nims.go.jp/filesets/f169faf0-f1ef-4dfe-b5f5-c0e53e81a0db/download) ([Detail](https://mdr.nims.go.jp/filesets/f169faf0-f1ef-4dfe-b5f5-c0e53e81a0db.md))

## Id

b1a6fd39-2a4a-4cfd-835e-b5a60bbda4a0

## Local identifier

identifier: MDR-LTC-2026Jan9/mp-15095

## Visibility

open_to_public

## State

published

## Created at

2026-01-15T06:52:01.883653Z

## Updated at

2026-01-24T06:38:32.424877Z

## Published at

2026-01-24T01:51:20.956798Z

## 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 Ca3Si2SnO9
    / P2_1/c (14) / materials id 15095
  title_type: original
  lang: en

## Description

- description: |
    Input data used to calculate the lattice thermal conductivities of
    Ca3Si2SnO9.
  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
    Ca3Si2SnO9.
  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: Ca3Si2SnO9
  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: Ca3Si2SnO9
  description: Ca3Si2SnO9

## Chemical composition

- identifier: Ca3Si2SnO9
  description: Ca3Si2SnO9

## Structure for specimen

- description: Ca3Si2SnO9
  category_description: Ca3Si2SnO9

## 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
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  size: 94974696
  md5: 3b6e1b15ffbdecb66f2e9554d2db90c8
- id: 51ac4b0f-16b7-4f8b-901f-1a0f12e2235e
  filename: LTC-calc.log
  content_type: text/x-log
  size: 631470
  md5: 612dc9c5aa24fa5adf588d5b5630d07f
- id: 24c703d2-e771-4ae9-aec4-75f9b2b54a4e
  filename: phono3py_mlp_eval_fc3_disp.yaml.xz
  content_type: application/x-xz
  size: 83568
  md5: c39e13bd4506f7617f5da38f7bf09e0c
- id: 9b57487b-ac18-4dbe-a2e8-97824265d670
  filename: phonopy_mlp_eval_fc2_dataset.yaml.xz
  content_type: application/x-xz
  size: 10017240
  md5: 3e9d969e019caf16136333b66b20d710
- id: 3a854149-d2d2-4d15-856d-aa1aee027edc
  filename: phonopy_training_dataset.yaml.xz
  content_type: application/x-xz
  size: 873388
  md5: 46ba77b8e8f214ffff6756c1ac0a4eb7
- id: 4de2b94a-8fdd-4499-b552-9d605836a782
  filename: polymlp.yaml.xz
  content_type: application/x-xz
  size: 918152
  md5: ec60f2a0c5a532bd78f5845d1a44e9c2
- id: d7256722-3cc3-4fd2-a474-112a85a3b41c
  filename: vasp-settings.tar.xz
  content_type: application/x-xz
  size: 596
  md5: 756996195377e7fb9acc045dc1512f96
- id: f169faf0-f1ef-4dfe-b5f5-c0e53e81a0db
  filename: band_pdos.png
  content_type: image/png
  size: 95001
  md5: b78cf6073d3c0b482ee923f1bc444f7a

## Thumbnail

fileset_id: f169faf0-f1ef-4dfe-b5f5-c0e53e81a0db
filename: band_pdos.png