High-Throughput Screening of Piezoelectric Hybrid Organic–Inorganic Perovskites via Density-Functional Theory and Machine Learning

Jianxin He; Tianle Yue; Yu Lim Kim; Ying Li

doi:10.1039/d6ta02101h

January 1, 2026Journal of Materials Chemistry A

High-Throughput Screening of Piezoelectric Hybrid Organic–Inorganic Perovskites via Density-Functional Theory and Machine Learning

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Key Points

This research aims to establish design principles for piezoelectric devices using hybrid organic–inorganic perovskites (HOIPs).
Utilized density-functional theory for computational modeling of HOIPs characteristics.
Applied machine learning algorithms to predict piezoelectric properties based on molecular composition.
Conducted high-throughput screening to evaluate the performance of various HOIPs.
Identified key molecular compositions that enhance piezoelectric performance.
Demonstrated a clear correlation between structural features and piezoelectricity.
Achieved improved predictive accuracy for HOIP designs through machine learning techniques.

Implication

Explores piezoelectric properties of hybrid organic–inorganic perovskites, suggesting better design principles.

Abstract

Hybrid organic–inorganic perovskites (HOIPs) are promising candidates for piezoelectric devices owing to their structural tunability, low density, and intrinsic mechanical compliance. However, rational design principles linking molecular composition to orientation-independent...

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Cite This Study

He et al. (2026) studied this question.

synapsesocial.com/papers/6a056824a550a87e60a2089e https://doi.org/10.1039/d6ta02101h

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High-Throughput Screening of Piezoelectric Hybrid Organic–Inorganic Perovskites via Density-Functional Theory and Machine Learning

Key Points

Implication

Abstract

Discussion

Cite This Study

High-Throughput Screening of Piezoelectric Hybrid Organic–Inorganic Perovskites via Density-Functional Theory and Machine Learning

Key Points

Implication

Abstract

Discussion

Cite This Study

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