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March 19, 2026Discover Artificial IntelligenceOpen Access

Leveraging SHAP values for superior prediction and efficient Bayesian optimization in material chemistry

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Authors

TETakuya Ehiro

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Overview

Demonstrates improved predictive accuracy and efficiency in material exploration using SHAP values and Bayesian optimization.

Key Points

  • The aim is to enhance predictive accuracy in regression models for material chemistry using SHAP values.
  • Implemented feature extraction method using SHAP values for regression analysis.
  • Compared performance of various base models for SHAP-based feature extraction, focusing on random forest.
  • Investigated the efficiency of material exploration during Bayesian optimization.
  • SHAP values improved predictive accuracy for underfitting regression models.
  • Random forest outperformed other models in capturing complex non-linear relationships.
  • The proposed method enhanced the efficiency of material exploration in Bayesian optimization.

Cite This Study

Takuya Ehiro (2026) studied this question.

synapsesocial.com/papers/69bb9313496e729e62980e1ehttps://doi.org/10.1007/s44163-026-00966-1
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