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Related Concept Videos

Standard Entropy Change for a Reaction03:00

Standard Entropy Change for a Reaction

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Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
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Nucleophilic substitution in aromatic compounds is feasible in substrates bearing strong electron-withdrawing substituents positioned ortho or para to the leaving group. The reaction proceeds via two steps: the addition of the nucleophile and the elimination of the leaving group.
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A chemical reaction is a process by which the bonds in the atoms of substances are rearranged to generate new substances. Matter cannot be created or destroyed in a chemical reaction—the same type and number of atoms that make up the reactants are still present in the products. Merely, the rearrangement of chemical bonds produces new compounds.
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Benzylic halogenation takes place under conditions that favor radical reactions such as heat, light, or a free radical initiator like peroxide.
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The reactions of acid anhydrides are analogous to the reactions of acid chlorides and proceed via a nucleophilic acyl substitution. They only differ in the identity of the leaving group. During an acid chloride reaction, the leaving group is a chloride ion, and the by-product is hydrochloric acid. However, in an acid anhydride reaction, the leaving group is a carboxylate ion, and the by-product is a carboxylic acid.
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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A chemical reaction entity recognition method based on a natural language data augmentation strategy.

Xiaowen Zhang1, Yang Li2, Chaoyi Li1

  • 1School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, Guangdong, China. youhengzhi@hit.edu.cn.

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Summary

We developed DACRER, a novel artificial intelligence model, to automatically extract chemical reaction procedures for building large datasets. This method enhances data availability for chemical reaction prediction, improving AI applications in chemistry.

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Area of Science:

  • Computational chemistry
  • Artificial intelligence in chemistry
  • Data science for chemical informatics

Background:

  • Reliable datasets are crucial for artificial intelligence (AI) applications in chemical reaction prediction.
  • Automated extraction of chemical reaction procedures is essential for building structured chemical databases.
  • Current methods may face challenges in processing large-scale chemical texts efficiently.

Purpose of the Study:

  • To propose a novel model, DACRER, for large-scale reaction extraction from chemical texts.
  • To improve the efficiency and accuracy of building structured chemical databases.
  • To address the need for abundant, reliable datasets in AI-driven chemical research.

Main Methods:

  • Development of the DACRER model for automated reaction extraction.
  • Implementation of transfer learning techniques within the DACRER model.
  • Application of a data augmentation strategy to enhance model performance.
  • Evaluation of the model on diverse chemical datasets.

Main Results:

  • DACRER demonstrates good performance in identifying and processing chemical texts.
  • The model effectively extracts reaction procedures for database construction.
  • Transfer learning and data augmentation contribute to the model's effectiveness.
  • Successful evaluation on chemical datasets indicates robust performance.

Conclusions:

  • The proposed DACRER model offers a viable solution for large-scale reaction extraction.
  • DACRER facilitates the creation of structured chemical databases, supporting AI advancements.
  • The model's performance highlights the potential of AI in chemical informatics and data curation.