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Data-Driven Polymer Design: Machine Learning for Rapid Materials Discovery: From Foundational Concepts to Advanced Inverse Design Strategies
Description
In an era where innovation in materials science drives advancements in every sector—from sustainable packaging to aerospace engineering—Data-Driven Polymer Machine Learning for Rapid Materials Discovery stands as a guide to the future of polymer development. This resource bridges the gap between traditional polymer R&D and the transformative power of machine learning, offering a roadmap for scientists, engineers, and students looking to accelerate discovery cycles and unlock unprecedented materials performance.
Inside this book, you will
Foundational Build a solid grounding in polymer science and the fundamentals of machine learning. Understand how polymer structure, composition, and morphology influence properties like tensile strength, glass transition temperature, and barrier performance.
Data Handling & Feature Learn practical strategies for data acquisition, cleaning, and representation. Explore techniques to extract meaningful descriptors from complex polymer structures, ensuring that your models capture the nuances needed for accurate predictions.
Core & Advanced ML Master supervised learning methods for property prediction and unsupervised techniques for pattern discovery. Delve into advanced topics including uncertainty quantification, inverse design, and integration with high-throughput experimentation for real-time feedback and optimization.
Software Tools & Practical Gain familiarity with essential libraries such as scikit-learn, PyTorch, and RDKit. Discover best practices for building reproducible workflows, scaling your computations, and maintaining data integrity and security.
Ethical & Sustainability Reflect on the broader implications of your work. Learn how to incorporate sustainability metrics, meet regulatory standards, and responsibly navigate proprietary data and intellectual property.
Hands-On Project & Case Consolidate your learning with a capstone project that guides you through each step of the process, from dataset preparation to designing novel polymer candidates. Real-world examples illuminate industrial applications in packaging, biomedical devices, electronics, and beyond.
Whether you are a student keen to enter the new frontier of materials informatics, a researcher striving to accelerate polymer innovation, or an industry professional seeking a competitive edge, Data-Driven Polymer Design provides both the theoretical framework and practical tools to drive your work forward. Embrace a data-driven future and transform how you discover, optimize, and implement polymer materials.
In an era where innovation in materials science drives advancements in every sector—from sustainable packaging to aerospace engineering—Data-Driven Polymer Machine Learning for Rapid Materials Discovery stands as a guide to the future of polymer development. This resource bridges the gap between traditional polymer R&D and the transformative power of machine learning, offering a roadmap for scientists, engineers, and students looking to accelerate discovery cycles and unlock unprecedented materials performance.
Inside this book, you will
Foundational Build a solid grounding in polymer science and the fundamentals of machine learning. Understand how polymer structure, composition, and morphology influence properties like tensile strength, glass transition temperature, and barrier performance.
Data Handling & Feature Learn practical strategies for data acquisition, cleaning, and representation. Explore techniques to extract meaningful descriptors from complex polymer structures, ensuring that your models capture the nuances needed for accurate predictions.
Core & Advanced ML Master supervised learning methods for property prediction and unsupervised techniques for pattern discovery. Delve into advanced topics including uncertainty quantification, inverse design, and integration with high-throughput experimentation for real-time feedback and optimization.
Software Tools & Practical Gain familiarity with essential libraries such as scikit-learn, PyTorch, and RDKit. Discover best practices for building reproducible workflows, scaling your computations, and maintaining data integrity and security.
Ethical & Sustainability Reflect on the broader implications of your work. Learn how to incorporate sustainability metrics, meet regulatory standards, and responsibly navigate proprietary data and intellectual property.
Hands-On Project & Case Consolidate your learning with a capstone project that guides you through each step of the process, from dataset preparation to designing novel polymer candidates. Real-world examples illuminate industrial applications in packaging, biomedical devices, electronics, and beyond.
Whether you are a student keen to enter the new frontier of materials informatics, a researcher striving to accelerate polymer innovation, or an industry professional seeking a competitive edge, Data-Driven Polymer Design provides both the theoretical framework and practical tools to drive your work forward. Embrace a data-driven future and transform how you discover, optimize, and implement polymer materials.
88 pages, Kindle Edition
Published December 12, 2024
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