# Computational material screening for electrode materials of BaSi2 solar cells

https://mdr.nims.go.jp/datasets/6e716375-d5f0-477b-b68e-c6c453c3b858

## File

- [Computational material screening for electrode materials of BaSi2 solar cells.pdf](https://mdr.nims.go.jp/filesets/8a316d81-4c14-4387-873f-e369b2ff6e31/download) ([Detail](https://mdr.nims.go.jp/filesets/8a316d81-4c14-4387-873f-e369b2ff6e31.md))
- [Supplemental_material_v4.pdf](https://mdr.nims.go.jp/filesets/366a1bcc-cd16-4fba-ae55-e1fb3ca7e869/download) ([Detail](https://mdr.nims.go.jp/filesets/366a1bcc-cd16-4fba-ae55-e1fb3ca7e869.md))
- [TSTM-2025-0057_data.zip](https://mdr.nims.go.jp/filesets/148d5368-f898-4f12-a485-4255cd04afde/download) ([Detail](https://mdr.nims.go.jp/filesets/148d5368-f898-4f12-a485-4255cd04afde.md))

## Id

6e716375-d5f0-477b-b68e-c6c453c3b858

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-03-06T05:51:51.342398Z

## Updated at

2026-03-07T03:30:04.136269Z

## Published at

2026-03-07T00:46:04.586354Z

## Doi

https://doi.org/10.48505/nims.6208

## First published url

https://doi.org/10.1080/27660400.2026.2617671

## Date published

2026-12-31

## Recorded date published

2026-12-31

## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: Computational material screening for electrode materials of BaSi2 solar cells
  title_type: original
  lang: en

## Description

- description: In this study, we developed a computational material screening workflow
    for metallic electrodes of BaSi 2 solar cells. Elemental and binary metallic materials
    in the Materials Project database were screened for four device models with different
    charge transport layer materials contacting the electrode. The screening criteria
    included chemical stability, melting point, and work function. In contrast to
    conventional screening approaches that rely solely on materials descriptors, the
    present workflow explicitly incorporates device-level performance constraints
    by using the relationship between the work function and the simulated power conversion
    efficiency. For melting point evaluation, a linear regression estimation from
    the cohesive energy and a machine learning model were compared to assess their
    accuracy, which revealed the higher accuracy of the machine learning model. For
    work function evaluation, first-principles calculation and another machine learning
    model were compared, which showed similar accuracies. Considering the computational
    cost, the machine learning model was used for screening. The threshold of work
    function screening was determined by device simulations. As a result, promising
    materials for metallic electrodes were successfully identified. Moreover, the
    developed screening workflow with high versatility will be applicable to material
    discovery for other solar cells and semiconductor devices.
  description_type: abstract
  lang: en

## Creator

- name: Tomoaki Yazaki
  role: author
  organization: University of Yamanashi
  department: a Center for Crystal Science and Technology
- name: Keisuke Arimoto
  role: author
- name: Junji Yamanaka
  role: author
- name: Kosuke O. Hara
  role: author

## Contact agent



## Publisher

organization: Taylor & Francis

## Managing organization



## Keyword

- subject: Solar cells
  schema: not_defined
- subject: metallic electrode
  schema: not_defined
- subject: high-throughput virtual screening
  schema: not_defined
- subject: work function
  schema: not_defined
- subject: melting point
  schema: not_defined
- subject: density functional theory
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Science and Technology of Advanced Materials
  issn: '27660400'
  volume: '6'
  issue: '1'
  article_number: '2617671 '

## Conference



## Related item



## Funding



## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: 8a316d81-4c14-4387-873f-e369b2ff6e31
  filename: Computational material screening for electrode materials of BaSi2 solar
    cells.pdf
  content_type: application/pdf
  size: 1492018
  md5: 71cee9d8b1eb90870cacbf3ea3981aee
- id: 366a1bcc-cd16-4fba-ae55-e1fb3ca7e869
  filename: Supplemental_material_v4.pdf
  content_type: application/pdf
  size: 302869
  md5: 9b9f32f76a96262769c166b2faca3c63
- id: 148d5368-f898-4f12-a485-4255cd04afde
  filename: TSTM-2025-0057_data.zip
  content_type: application/zip
  size: 26591
  md5: 3dad5a9f6a75f582eb24be062e1df2bc

## Thumbnail

fileset_id: 366a1bcc-cd16-4fba-ae55-e1fb3ca7e869
filename: Supplemental_material_v4.pdf