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Related Concept Videos

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Transformers

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A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
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In 1931, physicist Ernst Ruska—building on the idea that magnetic fields can direct an electron beam just as lenses can direct a beam of light in an optical microscope—developed the first prototype of the electron microscope. This development led to the development of the field of electron microscopy. In the transmission electron microscope (TEM), electrons are produced by a hot tungsten element and accelerated by a potential difference in an electron gun, which gives them up to 400...
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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
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Microscale Vortex-assisted Electroporator for Sequential Molecular Delivery
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Transformer technology in molecular science.

Jian Jiang1,2, Lu Ke1, Long Chen1

  • 1Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, China.

Wiley Interdisciplinary Reviews. Computational Molecular Science
|December 26, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Transformer models, utilizing self-attention mechanisms, are powerful deep learning tools for molecular science. This review details transformer algorithms like BERT and GPT, highlighting their technical applications in processing complex molecular data.

Keywords:
Data Science > Artificial Intelligence/Machine LearningData Science > Chemoinformaticsbiologychemistrymachine learningmolecular sciencetransformer technology

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Area of Science:

  • Molecular Science
  • Artificial Intelligence
  • Deep Learning

Background:

  • Transformer architecture, with self-attention, excels at sequential data processing.
  • Deep learning models based on transformers are increasingly vital in molecular science.
  • These models capture intricate hierarchical dependencies in complex data.

Purpose of the Study:

  • To provide an in-depth technical investigation of transformer-based machine learning algorithms in molecular science.
  • To examine the inner workings and effectiveness of various transformer models for molecular data.
  • To discuss emerging trends and interdisciplinary research potential of transformers in this domain.

Main Methods:

  • Review and analysis of transformer architectures including GPT, BART, BERT, Graph Transformer, Transformer-XL, T5, ViT, DETR, Conformer, CLIP, Sparse Transformers, and Mobile/Efficient Transformers.
  • Focus on the technical aspects and algorithmic innovations of these models.
  • Examination of how architectural features enable processing of complex molecular data.

    • Main Results:

    • Transformers effectively process sequential and complex molecular data through self-attention.
    • Specific models like BERT, GPT, and Graph Transformers show significant promise.
    • Architectural innovations directly contribute to enhanced performance in molecular applications.

    Conclusions:

    • Transformer-based machine learning techniques are foundational for advancements in molecular science.
    • Understanding these technical aspects is crucial for future interdisciplinary research.
    • The review offers a comprehensive overview of transformer applications in the molecular domain.