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Updated: Jul 14, 2025

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Accelerated Multisolvent Prediction for Aqueous Stable Halide Perovskite Materials.

Yiru Huang1, Shenyue Li1, Lei Zhang1

  • 1Department of Materials Physics, School of Chemistry and Materials Science, Nanjing University of Information Science & Technology, 210044, Nanjing, China.

ACS Applied Materials & Interfaces
|October 9, 2023
PubMed
Summary

Machine learning predicts stable perovskite films using novel solvent mixtures. A quinary solvent system significantly enhances optoelectronic stability in water, overcoming industrial deployment challenges for perovskite materials.

Keywords:
data-drivenhalide perovskite materialmachine learningsolvent designstability

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Area of Science:

  • Materials Science
  • Chemical Engineering
  • Computational Chemistry

Background:

  • Halide perovskite materials offer promising optoelectronic properties but suffer from poor stability, hindering industrial applications.
  • Traditional trial-and-error methods are insufficient for exploring the vast design space of solvent modifiers for perovskite stabilization.

Purpose of the Study:

  • To employ machine learning for predicting stable, solvent-modified perovskite films under harsh conditions.
  • To identify novel quinary solvent systems that enhance the stability and performance of perovskite materials.

Main Methods:

  • Utilized an extra tree machine learning model for combinatorial solvent design.
  • Generated a dataset of 6720 quinary solvent/perovskite systems with predicted aqueous stability labels.
  • Validated machine learning predictions through photoelectrochemical experiments.

Main Results:

  • Identified a specific quinary solvent system (DMSO + DMF + toluene + NMP + GBL) that significantly improves the aqueous stability of methylammonium lead iodide (CH3NH3PbI3).
  • Achieved 80% experimental accuracy in verifying machine learning model predictions.
  • Demonstrated a 1000-fold increase in aqueous photocurrents for the optimized perovskite film under hostile conditions.

Conclusions:

  • Machine learning is an effective tool for accelerating solvent design in materials science.
  • The developed approach enables the discovery of stable halide perovskite materials for practical applications.
  • The identified quinary solvent system represents a significant advancement in enhancing perovskite stability and performance.