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Related Concept Videos

Predicting Reaction Outcomes02:24

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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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Related Experiment Videos

Heck reaction prediction using a transformer model based on a transfer learning strategy.

Ling Wang1, Chengyun Zhang, Renren Bai

  • 1Artificial Intelligent Aided Drug Discovery Lab, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China. lijianjun@zjut.edu.cn hduan@zjut.edu.cn.

Chemical Communications (Cambridge, England)
|July 17, 2020
PubMed
Summary

Transfer learning significantly enhances chemical data prediction accuracy for small datasets. This approach, combining transfer learning with transformer models, boosts Heck reaction prediction performance substantially.

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Area of Science:

  • Chemical informatics
  • Machine learning applications in chemistry

Background:

  • Predicting chemical reactions is crucial for drug discovery and materials science.
  • Small chemical datasets pose challenges for traditional machine learning models.

Purpose of the Study:

  • To present a proof-of-concept methodology for chemical data analysis using transfer learning.
  • To apply transfer learning with transformer models to small-dataset reaction prediction.

Main Methods:

  • Utilized transfer learning in conjunction with a transformer model architecture.
  • Applied the combined model to predict outcomes for the Heck reaction using limited chemical data.

Main Results:

  • The transformer-transfer learning model achieved a prediction accuracy of 94.9%.
  • This represents a significant improvement over the baseline transformer model, which achieved 66.3% accuracy.

Conclusions:

  • Transfer learning is an effective strategy for improving predictive accuracy with small chemical datasets.
  • The developed methodology demonstrates the potential of combining transfer learning and transformer models for chemical data challenges.