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Artificial intelligence for materials informatics

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Artificial intelligence for materials informatics/ edited by S. Sachin Kumar ... [et al.].
其他作者:	Sachin Kumar, S.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xii, 247 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Materials science - Data processing. -
電子資源:	https://doi.org/10.1007/978-3-031-89983-6
ISBN:	9783031899836

Artificial intelligence for materials informatics
Artificial intelligence for materials informatics[electronic resource] /edited by S. Sachin Kumar ... [et al.]. - Cham :Springer Nature Switzerland :2025. - xii, 247 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v. 12131860-9503 ;. - Studies in computational intelligence ;volume 1213..

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.

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.

ISBN: 9783031899836

Standard No.: 10.1007/978-3-031-89983-6doiSubjects--Topical Terms:

2045815
Materials science
--Data processing.

LC Class. No.: TA404.23

Dewey Class. No.: 620.11028563

Artificial intelligence for materials informatics
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520 $a 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.
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