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Data-Quality-Navigated Machine Learning Strategy with Chemical Intuition to Improve Generalization.

Songran Yang1, Ming Sun1, Chaojie Shi1

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This study introduces a data-quality strategy for machine learning (ML) in organic semiconductors (OSCs). It improves reorganization energy (RE) prediction and generalization, offering a tool for screening efficient OSCs.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Machine Learning

Background:

  • Generalizing machine learning (ML) models to real-world data is a significant challenge.
  • Data quality in ML is often overlooked, hindering the development of robust evaluation and processing methods.
  • Accurate prediction of reorganization energy (RE) is crucial for charge mobility in organic semiconductors (OSCs).

Purpose of the Study:

  • To propose a data-quality-navigated strategy for improving ML generalization in the context of RE prediction for OSCs.
  • To develop methods for evaluating data diversity, reliability, and splitting strategies tailored to chemical data.
  • To create a robust ML framework for predicting RE and screening efficient OSC materials.

Main Methods:

  • Developed data diversity evaluation based on molecular structure characteristics.
  • Implemented reliability evaluation using prediction accuracy and data filtering based on K-fold uncertainty.
  • Employed clustering and stratified sampling for data splitting based on molecular descriptors and REs.
  • Proposed a complementary feature representation strategy considering chemical intuition and molecular structure.
  • Constructed an ensemble framework of two deep learning models.

Main Results:

  • Generated a representative RE dataset of 15,989 molecules with high reliability and diversity.
  • The proposed ML framework demonstrated strong robustness and generalization capabilities.
  • The model significantly outperformed eight adversarial control methods on diverse OSC molecules.

Conclusions:

  • The data-quality-navigated strategy enhances ML model generalization, particularly for complex chemical prediction tasks like RE.
  • The developed ensemble deep learning framework provides a reliable tool for screening efficient organic semiconductors.
  • This work offers methodological guidelines for improving data quality and ML generalization in materials science applications.