Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning

Pikee Priya, Narayana R. Aluru

npj Computational Materials 2021 article cited by 94

Source Open-access PDF More in Engineering & Materials

importance 6/10 ai assisted Machine Learning in Materials Science energy materials stage: research

Problem

Identifying ABO3-type perovskite oxides with high conductivity for energy applications is slow with traditional methods.

Why it matters

High-conductivity perovskites are needed for various energy applications.

What the AI did

Machine learning models trained on over 7000 literature data points predicted total conductivity and charge-carrier type, then screened large doped/undoped perovskite spaces for high-conductivity candidates.

Results

Models validated on compounds absent from training set; screened 1793 undoped and 95,832 doped perovskites to report high-conductivity candidates; identified key predictive features.

Impact

Computational screening produced candidate perovskites; no experimental synthesis or measurement is stated.

Record

Venue	npj Computational Materials
Published	2021
Work type	article
Authors	Pikee Priya, Narayana R. Aluru
Citations	94 (OpenAlex, at import time)
References	131
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1038/s41524-021-00551-3
Open access	gold
Keywords	machine learning, perovskites, conductivity prediction, materials screening, energy materials

Importance rating

6 / 10

significant — thousands to millions affected

ML screening of ~97k perovskites for energy applications; broad materials-discovery relevance, but abstract gives no quantified impact and no experimental validation.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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