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October 9, 2025Open Access

Are we there yet? Adventures on a road trip through machine learning as a computational chemist

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Authors

HKHeather J. KulikMassachusetts Institute of Technology

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Implication

Perspective shares experiences applying machine learning to catalysis and redox chemistry, highlighting challenges and outcomes.

Key Points

  • Machine learning has transformed computational chemistry, enabling significant advancements in discovery and research.
  • Personal experiences showcase the use of density functional theory alongside automated workflows in materials design.
  • Active learning and descriptor-based approaches overcame challenges of limited data in training machine learning models.
  • Experiments validate computational predictions, emphasizing the importance of data curation and method sensitivity.

Cite This Study

Heather J. Kulik (2025) studied this question.

synapsesocial.com/papers/68e80eb363e2e2f707877d2fhttps://doi.org/10.1063/5.0297853
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