Overview

Reactive PublishingChemML in Action: Practical Machine Learning for Chemical Sciences Unlock the power of machine learning to transform how you approach research in chemistry and materials science. This hands-on guide bridges the gap between theoretical machine learning concepts and real-world chemical applications. Whether you're a chemist exploring data-driven methods for the first time or an experienced researcher looking to integrate ML workflows into your laboratory or computational pipeline, this book provides clear, practical instruction tailored specifically to the needs of the chemical sciences. What you'll find inside: Step-by-step tutorials on applying machine learning techniques to common challenges in molecular property prediction, reaction modeling, materials discovery, and spectral analysis Guidance on preparing and featurizing chemical data for ML models Practical examples using Python libraries and tools designed for cheminformatics Strategies for building reproducible, interpretable models that address real scientific questions Best practices for avoiding common pitfalls when moving from traditional computational chemistry to data-driven approaches Written in an accessible style with minimal jargon, ChemML in Action emphasizes working code, visual explanations, and immediately applicable techniques. No prior deep expertise in machine learning or programming is assumed, only a foundational knowledge of chemistry. Ideal for graduate students, academic researchers, and industry scientists seeking to accelerate discovery through modern computational methods. Start applying practical machine learning to your chemical research today.

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