# Generative AI‐Driven Accelerated Discovery of Passivation Molecules for Perovskite Solar Cells

https://mdr.nims.go.jp/datasets/3f546f65-79be-4db8-b890-a661bcda7166

## File

- [Advanced Science - 2026 - Fajar - Generative AI‐Driven Accelerated Discovery of Passivation Molecules for Perovskite Solar (1).pdf](https://mdr.nims.go.jp/filesets/961cc7b1-dc12-4702-913f-bd37edafb373/download) ([Detail](https://mdr.nims.go.jp/filesets/961cc7b1-dc12-4702-913f-bd37edafb373.md))

## Id

3f546f65-79be-4db8-b890-a661bcda7166

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-07-02T04:26:47.027946Z

## Updated at

2026-07-02T05:43:51.465301Z

## Published at

2026-07-02T07:32:53.753125Z

## Doi



## First published url

https://doi.org/10.1002/advs.202523042

## Date published

2026-04-02

## Recorded date published

2026-6

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Generative AI‐Driven Accelerated Discovery of Passivation Molecules for Perovskite
    Solar Cells
  title_type: original
  lang: en

## Description

- description: Molecular passivation is an effective strategy to suppress interfacial
    defects in perovskite solar cells (PSCs), yet the discovery of new passivation
    molecules remains limited by empirical design and narrow chemical libraries. Here,
    for the first time, we present an AI-driven framework integrating discriminative
    and generative language models to accelerate the discovery of effective passivators.
    A SMILES-X classifier trained on literature data achieved high predictive performance
    (F1 = 0.80, ROC–AUC = 0.88), while a GPT-2-based generative model iteratively
    produced over 100 000 novel molecules, more than 80% of which were predicted to
    be effective. Multi-criteria filtering reduced this pool to ∼8000 high-quality
    candidates, from which clustering analysis identified ten diverse representatives.
    Three molecules, including a surrogate analog, were prioritized for experimental
    testing, and all exhibited a clear passivation effect. In particular, 4-maleimidobutyric
    acid increased the average open-circuit voltage from 1.08 to 1.12 V and improved
    the average power conversion efficiency from 19.3% to 22.2%, while markedly reducing
    hysteresis. This study demonstrates that generative AI can autonomously propose
    synthetically accessible, functionally effective molecules for PSC passivation,
    offering a powerful paradigm for accelerated materials discovery beyond conventional
    chemical space exploration.
  description_type: abstract
  lang: und

## Creator

- name: Adroit T. N. Fajar
  role: author
- name: Guillaume Lambard
  role: author
  orcid: https://orcid.org/0000-0003-0275-4079
  organization: National Institute for Materials Science
- name: Jessie Manopo
  role: author
- name: Ruili Guo
  role: author
- name: Kevin Septioga
  role: author
- name: Rizfi F. Pari
  role: author
- name: Toshinori Matsushima
  role: author
- name: Zhanglin Guo
  role: author

## Contact agent



## Publisher

organization: Wiley

## Managing organization



## Keyword

- subject: Generative AI
  schema: not_defined
- subject: Passivation Molecules
  schema: not_defined
- subject: Perovskite Solar Cells
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/
  date_licensed: 2026-04-02

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Advanced Science
  issn: '21983844'
  volume: '13'
  issue: '36'
  article_number: e23042

## Conference



## Related item



## Funding

- identifier: WPI‐I2CNER
  funder_name: International Institute for Carbon-Neutral Energy Research, Kyushu
    University

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



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## Process for specimen treatment



## Computational method



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## Custom property



## Fileset

- id: 961cc7b1-dc12-4702-913f-bd37edafb373
  filename: Advanced Science - 2026 - Fajar - Generative AI‐Driven Accelerated Discovery
    of Passivation Molecules for Perovskite Solar (1).pdf
  content_type: application/pdf
  size: 4582635
  md5: d34c465f51a84258e99d3e55ad3d850e

## Thumbnail

fileset_id: 961cc7b1-dc12-4702-913f-bd37edafb373
filename: Advanced Science - 2026 - Fajar - Generative AI‐Driven Accelerated Discovery
  of Passivation Molecules for Perovskite Solar (1).pdf