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by IQAC KIITKIIT Deemed to be University — Internal Quality Assurance Cell

Publication2026

Machine Learning Driven Strategies for Improving CsFASnI3 Perovskite Solar Cell Efficiency

The record

Type
Publication
Date
1 January 2026
Level
National
Journal
2026 5th International Conference on Power Control and Computing Technologies Icpc2t 2026
Pages
1040-1043
Published
1 January 2026
DOI
10.1109/icpc2t68221.2026.11646291
Scopus ID
105048821950
Authors
5
Open access
No
Citations
0
International co-authorship
No
Co-author countries
IN

Classification

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UN Sustainable Development Goals

  1. 7.2 Substantially increase the share of renewable energy in the global energy mix

    TargetPrimaryHigh confidenceJudged

    Machine learning predictions optimize perovskite solar cell parameters achieving 25.36% power conversion efficiency through materials engineering and AI.

    High confidence
    The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
    Judged
    Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
    Produced by
    claude-haiku-4-5-20251001, under kortex-publication-v1

Viksit Bharat @2047

  1. vb-2.5 Green economy, energy transition and climate resilience

    Sub-themePrimaryHigh confidenceJudged

    Advanced solar energy technology optimization improves renewable energy conversion efficiency and supports clean energy transition.

    Produced by
    claude-haiku-4-5-20251001, under kortex-publication-v1

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KIIT Deemed to be University. “Machine Learning Driven Strategies for Improving CsFASnI3 Perovskite Solar Cell Efficiency”. Kortex.

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