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Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

📅 July 7, 2021 👤 John A. Keith, Valentín Vassilev-Galindo, Bingqing Cheng et al. 📖 Chemical Reviews 📊 808 citations

🤖 Plain-English Summary

Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved.

🔑 Key Findings

  • However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences.
  • This Review is written for new and experienced researchers working at the intersection of both fields.
  • We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved.

💡 Why This Matters

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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📋 Article Details

Category 🤖 Artificial Intelligence
Published Jul 07, 2021
Journal Chemical Reviews
Authors John A. Keith, Valentín Vassilev-Galindo, Bingqing Cheng, Stefan Chmiela, Michael Gastegger
DOI 10.1021/acs.chemrev.1c00107
Citations 808
Source OpenAlex

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