computational and data driven chemistry using artificial intelligence

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Computational and Data Driven Chemistry Using Artificial Intelligence
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Publisher : Elsevier
Release Date :
ISBN 10 : 9780128222492
Pages : 310 pages
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Offering the ability to process large or complex data-sets, compare molecular characteristics and behaviours, and help researchers design or identify new structures, Artificial Intelligence (AI) holds huge potential to revolutionise the future of chemistry. Computational and Data-Driven Chemistry Using Artificial Intelligence: Volume 1: Fundamentals, Methods and Applications highlights fundamental knowledge and current developments in the field, giving readers insight into how these tools can be harnessed to enhance their own work. Volume 1 explores the fundamental knowledge and current methods being used to apply AI across a whole host of chemistry applications. Part 1 provides foundational information on AI in chemistry, with an introduction to the field and guidance on database usage and statistical analysis to help support newcomers to the field. Part 2 then goes on to discuss approaches currently used to address problems in broad areas such as computational and theoretical chemistry; materials, synthetic and medicinal chemistry; crystallography, analytical chemistry, and spectroscopy. Finally, potential future trends in the field are discussed. Drawing on the knowledge of its expert team of global contributors, Computational and Data-Driven Chemistry Using Artificial Intelligence: Volume 1: Fundamentals, Methods and Applications is a fascinating insight into this rapidly developing field and a great resource for all those interested in exploring the opportunities afforded by the intersection of chemistry and AI in their own work. Provides an accessible introduction to the current state and future possibilities for AI in chemistry Explores how computational chemistry methods and approaches can both enhance and be enhanced by AI Highlights the interdisciplinary, broad applicability of AI tools across a wide range of chemistry fields

Computational and Data Driven Chemistry Using Artificial Intelligence

Offering the ability to process large or complex data-sets, compare molecular characteristics and behaviours, and help researchers design or identify new structures, Artificial Intelligence (AI) holds huge potential to revolutionise the future of chemistry. Computational and Data-Driven Chemistry Using Artificial Intelligence: Volume 1: Fundamentals, Methods and Applications highlights fundamental knowledge and

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Machine Learning in Chemistry

Atomic-scale representation and statistical learning of tensorial properties -- Prediction of Mohs hardness with machine learning methods using compositional features -- High-dimensional neural network potentials for atomistic simulations -- Data-driven learning systems for chemical reaction prediction: an analysis of recent approaches -- Using machine learning to inform decisions in drug

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Applications of Computational Intelligence in Data Driven Trading

“Life on earth is filled with many mysteries, but perhaps the most challenging of these is the nature of Intelligence.” – Prof. Terrence J. Sejnowski, Computational Neurobiologist The main objective of this book is to create awareness about both the promises and the formidable challenges that the era of Data-Driven Decision-Making

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Machine Learning and Data Driven Research in Chemistry

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Handbook of Materials Modeling

The first reference of its kind in the rapidly emerging field of computational approachs to materials research, this is a compendium of perspective-providing and topical articles written to inform students and non-specialists of the current status and capabilities of modelling and simulation. From the standpoint of methodology, the development follows

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Data Driven Computational Neuroscience

Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.

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Artificial Intelligence in Drug Discovery

Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial

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Environmental Health Perspectives

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Computational Toxicology

A comprehensive analysis of state-of-the-art molecular modeling approaches and strategies applied to risk assessment for pharmaceutical and environmental chemicals This unique volume describes how the interaction of molecules with toxicologically relevant targets can be predicted using computer-based tools utilizing X-ray crystal structures or homology, receptor, pharmacophore, and quantitative structure activity

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Applications of Computational Intelligence in Data Driven Trading

“Life on earth is filled with many mysteries, but perhaps the most challenging of these is the nature of Intelligence.” – Prof. Terrence J. Sejnowski, Computational Neurobiologist The main objective of this book is to create awareness about both the promises and the formidable challenges that the era of Data-Driven Decision-Making

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Data Driven Computational Neuroscience

Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.

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Artificial Intelligence in Healthcare

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The

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Data Driven Methods for Adaptive Spoken Dialogue Systems

Data driven methods have long been used in Automatic Speech Recognition (ASR) and Text-To-Speech (TTS) synthesis and have more recently been introduced for dialogue management, spoken language understanding, and Natural Language Generation. Machine learning is now present “end-to-end” in Spoken Dialogue Systems (SDS). However, these techniques require data collection and

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Computational Intelligence

Download or read online Computational Intelligence written by Anonim, published by Unknown which was released on 2001. Get Computational Intelligence Books now! Available in PDF, ePub and Kindle.

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