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Artificial Intelligence for Materials Informatics [Hardback]

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  • Formāts: Hardback, 283 pages, height x width: 235x155 mm, 51 Illustrations, color; 7 Illustrations, black and white; X, 283 p. 58 illus., 51 illus. in color., 1 Hardback
  • Sērija : Studies in Computational Intelligence 1213
  • Izdošanas datums: 04-Sep-2025
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031899822
  • ISBN-13: 9783031899829
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  • Hardback
  • Cena: 180,78 €*
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Artificial Intelligence for Materials InformaticsPalielināt
  • Formāts: Hardback, 283 pages, height x width: 235x155 mm, 51 Illustrations, color; 7 Illustrations, black and white; X, 283 p. 58 illus., 51 illus. in color., 1 Hardback
  • Sērija : Studies in Computational Intelligence 1213
  • Izdošanas datums: 04-Sep-2025
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031899822
  • ISBN-13: 9783031899829
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783031899829.html

This comprehensive book explores the transformative impact of AI on materials informatics, delving into machine learning/deep learning, and material knowledge representation. Embracing the transformative power of artificial intelligence (AI), the field of materials informatics has witnessed a remarkable revolution in its methodology and applications.  AI has revolutionized the field of materials informatics, enabling researchers to discover, design, and optimize materials with enhanced properties at an accelerated pace. It showcases how AI is accelerating materials discovery, property prediction, providing case studies, and a comprehensive bibliography for further exploration. This essential resource equips researchers, scientists, and engineers with the knowledge and tools to harness the power of AI for groundbreaking advancements in materials science.

Topological indices-based vector representation of graphs.- Toxicity
Prediction Using Convolutional Neural Networks: A Study of Deep Learning
Approach.- AI and ML in Polymer Science: Enhancing Material Informatics
through Predictive Modelling.- Transforming Carbon-Based Material: The Role
of AI and ML Regression Techniques in Material Science.- Physics Informed
Neural Networks: Fundamentals & Application to Phase Field Models.-
Application of AI to help leverage Density Functional Theory computations in
Materials Informatics.- XAI Approaches in Genetic Biomaterial Analysis.-
AI-Driven Robotic Solutions in Material Engineering.- Implications of
high-entropy energy materials in healthcare, environment and agriculture,
along with the applications of artificial intelligence.- Advancements in
Agricultural Materials: Machine Learning Models for Precision Fertilizer
Prediction.