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Harnessing Artificial Intelligence and Machine Learning in Chemistry

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This article provides an overview of the application of artificial intelligence (AI) and machine learning (ML) in the fields of chemistry and materials science. It covers fundamental concepts, methods, and metrics relevant to AI, including data processing, model training, and various algorithms. The content is structured into lectures and seminars, detailing practical applications and theoretical foundations.
  • main points
  • unique insights
  • practical applications
  • key topics
  • key insights
  • learning outcomes

  • • main points

    • 1
      Comprehensive coverage of AI and ML concepts in chemistry and materials science.
    • 2
      Structured format with clear divisions between lectures and seminars.
    • 3
      Inclusion of practical examples and applications relevant to the field.

  • • unique insights

    • 1
      The importance of data preprocessing and its impact on model performance.
    • 2
      Discussion on the applicability of models and the significance of cross-validation.

  • • practical applications

    • The article provides practical guidance for implementing AI techniques in chemistry, including data handling and model training.

  • • key topics

    • 1
      Machine Learning Fundamentals
    • 2
      Data Processing Techniques
    • 3
      Applications of AI in Chemistry

  • • key insights

    • 1
      Integration of AI methodologies in traditional chemistry practices.
    • 2
      Focus on real-world applications and case studies in materials science.
    • 3
      Detailed exploration of various machine learning algorithms and their relevance.

  • • learning outcomes

    • 1
      Understand the fundamental concepts of AI and machine learning in chemistry.
    • 2
      Learn practical data processing techniques for AI applications.
    • 3
      Gain insights into the applicability of various AI models in real-world scenarios.
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“ Introduction to AI in Chemistry

Machine learning (ML) is a subset of AI that focuses on algorithms that allow computers to learn from and make predictions based on data. This section covers the basic principles of ML, including supervised, unsupervised, and reinforcement learning.

“ Data Collection and Processing

Various algorithms are employed in machine learning to analyze chemical data. This section outlines popular algorithms such as decision trees, support vector machines, and neural networks, explaining their applications in chemistry.

“ Applications of AI in Chemistry

Python is a leading programming language in data science and machine learning. This section discusses its advantages, libraries, and tools that facilitate data analysis and model development in chemistry.

 Original link: https://test.teach-in.ru/file/synopsis/pdf/ai-in-chemistry-and-materials-science-M-3.pdf

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