Machine Learning in Chemistry: The Impact of Artificial Intelligence

Machine Learning in Chemistry: The Impact of Artificial Intelligence

Book Description
Progress in the application of machine learning (ML) to the physical and life sciences has been rapid. A decade ago, the method was mainly of interest to those in computer science departments, but more recently ML tools have been developed that show significant potential across wide areas of science. There is a growing consensus that ML software, and related areas of artificial intelligence, may, in due course, become as fundamental to scientific research as computers themselves.
Yet a perception remains that ML is obscure or esoteric, that only computer scientists can really understand it, and that few meaningful applications in scientific research exist. This book challenges that view.
With contributions from leading research groups, it presents in-depth examples to illustrate how ML can be applied to real chemical problems. Through these examples, the reader can both gain a feel for what ML can and cannot (so far) achieve, and also identify characteristics that might make a problem in physical science amenable to a ML approach.
This text is a valuable resource for scientists who are intrigued by the power of machine learning and want to learn more about how it can be applied in their own field.


Chapter 1. Computers as Scientists
Chapter 2. How Do Machines Learn?
Chapter 3. MedChemInformatics: An Introduction to Machine Learning for Drug Discovery
Chapter 4. Machine Learning for Nonadiabatic Molecular Dynamics
Chapter 5. Machine Learning in Science – A Role for Mechanical Sympathy?
Chapter 6. A Prediction of Future States: AI-powered Chemical Innovation for Defense Applications
Chapter 7. Machine Learning for Chemical Synthesis
Chapter 8. Constraining Chemical Networks in Astrochemistry
Chapter 9. Machine Learning at the (Nano)materials-biology Interface
Chapter 10. Machine Learning Techniques Applied to a Complex Polymerization Process
Chapter 11. Machine Learning and Scoring Functions (SFs) for Molecular Drug Discovery: Prediction and Characterisation of Druggable Drugs and Targets
Chapter 12. Artificial Intelligence Applied to the Prediction of Organic Materials
Chapter 13. A New Era of Inorganic Materials Discovery Powered by Data Science
Chapter 14. Machine Learning Applications in Chemical Engineering
Chapter 15. Representation Learning in Chemistry
Chapter 16. Demystifying Artificial Neural Networks as Generators of New Chemical Knowledge: Antimalarial Drug Discovery as a Case Study
Chapter 17. Machine Learning for Core-loss Spectrum
Chapter 18. Autonomous Science: Big Data Tools for Small Data Problems in Chemistry
Chapter 19. Machine Learning for Heterogeneous Catalysis: Global Neural Network Potential from Construction to Applications
Chapter 20. A Few Guiding Principles for Practical Applications of Machine Learning to Chemistry and Materials

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