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Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.5K
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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Observational Learning01:12

Observational Learning

269
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Multi-Step Reactions02:31

Multi-Step Reactions

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Chemical reactions often occur in a stepwise fashion involving two or more distinct reactions taking place in a sequence. A balanced equation indicates the reacting species and the product species, but it reveals no details about how the reaction occurs at the molecular level. The reaction mechanism (or reaction path) provides details regarding the precise, step-by-step process by which a reaction occurs. Each of the steps in a reaction mechanism is called an elementary reaction. These...
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Measuring Reaction Rates03:09

Measuring Reaction Rates

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Polarimetry finds application in chemical kinetics to measure the concentration and reaction kinetics of optically active substances during a chemical reaction. Optically active substances have the capability of rotating the plane of polarization of linearly polarized light passing through them—a feature called optical rotation. Optical activity is attributed to the molecular structure of substances. Normal monochromatic light is unpolarized and possesses oscillations of the electrical...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

139
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
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Related Experiment Video

Updated: Aug 21, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.0K

Transformer-based multitask learning for reaction prediction under low-resource circumstances.

Haoran Qiao1, Yejian Wu2, Yun Zhang2

  • 1College of Mathematics and Physics, Shanghai University of Electric Power Shanghai 200090 China huiyuli@shiep.edu.cn.

RSC Advances
|November 16, 2022
PubMed
Summary

Deep learning models for chemical reaction prediction struggle with limited data. New multitask transformer models, RFRPT and MFRPT, significantly improve accuracy for low-resource reaction prediction and retrosynthesis.

Related Experiment Videos

Last Updated: Aug 21, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.0K

Area of Science:

  • Organic Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Deep learning excels at chemical reaction prediction but suffers from low accuracy on limited datasets.
  • Retrosynthesis and forward reaction prediction are crucial for organic chemistry and drug discovery.

Purpose of the Study:

  • To develop novel deep learning models that enhance chemical reaction prediction accuracy for low-resource datasets.
  • To address the limitations of existing models in handling insufficient chemical reaction data.

Main Methods:

  • Introduction of two multitask transformer models: retro-forward reaction prediction transformer (RFRPT) and multiforward reaction prediction transformer (MFRPT).
  • Integration of multitask learning with transformer architecture to improve prediction for both forward and retrosynthesis tasks.
  • Evaluation of model performance on low-resource chemical datasets.

Main Results:

  • Both RFRPT and MFRPT models significantly outperformed the baseline transformer model in reaction prediction accuracy.
  • RFRPT achieved an average top-1 accuracy of 76.9%, and MFRPT achieved 79.8%, compared to the baseline's 69.9%.
  • Multitask learning effectively mitigated the impact of limited training data by capturing essential chemical knowledge.

Conclusions:

  • Multitask learning frameworks, such as RFRPT and MFRPT, are effective in improving the predictive performance of deep learning models for chemical reactions.
  • These methods overcome the restriction of limited training data, advancing organic chemistry and drug discovery research.
  • The proposed models demonstrate a viable approach for accurate reaction prediction even with scarce chemical data.