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Title:           Applied Artificial Intelligence in Materials Science and Material Design
Subject:         Advanced Intelligent Systems 2025.7:2400986
Keywords:        artificial intelligence, electron microscope, material design, pharma, scanning probe microscopy, spectroscopy
Author:          Emigdio Chávez‐Angel
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      Title: Applied Artificial Intelligence in Materials Science and Material Design
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       Item: 
        Title: 1. Introduction
        Destination: temp:book:Section1_2
       Item: 
        Title: 2. Applied-AI in Raman and Infrared Spectroscopy
        Destination: temp:book:Section1_3
       Item: 
        Title: 3. Applied-AI in Synchrotron Facilities
        Destination: temp:book:Section1_4
        Children: 
         Item: 
          Title: 3.1. Applied-AI in Data Analysis in Synchrotron Experiments
          Destination: temp:book:Section2_5
         Item: 
          Title: 3.2. Applied-AI in Real-Time Applications in Synchrotron Experiments
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        Title: 4. Applied-AI in Scanning Probe Microscopy
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        Title: 5. Applied-AI in EM
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        Title: 6. Applied-AI in Material Design and Property Prediction
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         Item: 
          Title: 6.1. Applied-AI in Multiphysics Modeling: Metamaterials
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          Title: 6.2. Applied-AI in Atomistic Modeling
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          Title: 6.3. Applied-AI in Molecular Design: Examples in Pharmaceutical Drug Discovery
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          Title: 6.4. Applied-AI in Molecular Design: Example in Drug Discovery
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          Title: 6.5. Applied-AI in Designing Molecules with High Affinity to Specific Targets
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          Title: 6.6. Applied-AI to Drug Formulation
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          Title: 6.7. Applied-AI in Predicting Potential Drug Side Effects
          Destination: temp:book:Section2_16
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        Title: 7. Expectations of AI Methods
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       Item: 
        Title: 8. Perspectives
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  Checksum: ff2855f3
   Type: CRC32
  Checksum: 063149b8011f10053182ac532c6f60a4
   Type: MD5
  Checksum: 72d0d32df442bf8f20a3a2ad2eec6e1002795982
   Type: SHA-1
  Checksum: 174a6e739672226367f916d8eff71c1e0fc31d73561d45df4a35082a3c2d74ee
   Type: SHA-256