Machine Learning Reactivity in the Chemical Space Surrounding Vaska's Complex

Machine learning models, including neural networks, Bayesian optimization, gradient boosting and Gaussian processes, were trained with DFT data for the accurate, affordable and explainable prediction of hydrogen activation barriers in the chemical space surrounding Vaska's complex.

• Publication year

  2019

• Venue

  Unknown venue

• Publication date

  2019-11-27

• Fields of study

  Not labeled

• Identifiers
  DOI 10.26434/chemrxiv.10347566.v1
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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