Artificial Intelligence in Drug Discovery

Artificial Intelligence in Drug Discovery

Author: Nathan Brown

Publisher: Royal Society of Chemistry

Published: 2020-11-04

Total Pages: 425

ISBN-13: 1839160543

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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 Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.


Fundamentals Of Quantum Materials: A Practical Guide To Synthesis And Exploration

Fundamentals Of Quantum Materials: A Practical Guide To Synthesis And Exploration

Author: Johnpierre Paglione

Publisher: World Scientific

Published: 2021-01-04

Total Pages: 276

ISBN-13: 9811219389

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Despite a long tradition of sophisticated, creative materials synthesis among quantum materials researchers, a sense of broader community has been lacking. In initiating the Fundamentals of Quantum Materials Winter School held annually at the University of Maryland, we wanted to bring together the next generation of growers to learn techniques and pointers directly from senior scientists, and it turns out that we were not alone. The enthusiasm from both students and teachers has been both gratifying and invigorating. Four schools later, we can confidently say that physicists, chemists, and materials scientists, experimentalists and theorists alike, all want to know how to make a good sample. With this in mind, we asked our lecturers to record their most important ideas and share their expertise with a broader audience. This resource is a compilation of fundamental and practical guides on the modern methods of materials synthesis utilized by these experts. We hope that you enjoy reading their essential guidance and state-of-the-art techniques as you explore the Fundamentals of Quantum Materials.


Computational Materials System Design

Computational Materials System Design

Author: Dongwon Shin

Publisher: Springer

Published: 2017-11-10

Total Pages: 239

ISBN-13: 3319682806

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This book provides state-of-the-art computational approaches for accelerating materials discovery, synthesis, and processing using thermodynamics and kinetics. The authors deliver an overview of current practical computational tools for materials design in the field. They describe ways to integrate thermodynamics and kinetics and how the two can supplement each other.


Energizing America

Energizing America

Author: Varun Sivaram

Publisher:

Published: 2020-09-14

Total Pages:

ISBN-13: 9780578758527

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Clean energy innovation is central to the fight against climate change. To rise to this challenge, the United States should launch a National Energy Innovation Mission. Led by the president and authorized by Congress, this mission should harness the nation's unmatched innovative capabilities-at research universities, federal laboratories, and private firms (both large and small), in all regions of the country-to speed the progress of clean energy technologies. To jumpstart this mission and unlock a virtuous cycle of public and private investment, the US federal government should triple its funding for energy research, development, and demonstration (RD&D) over the next five years to $25 billion by 2025. "Energizing America" offers policymakers a strategic framework to build a growing RD&D portfolio over the next five years, detailed fundingproposals across the full spectrum of critical energy technologies, and recommendations for immediate action.


Artificial Intelligence in Healthcare

Artificial Intelligence in Healthcare

Author: Adam Bohr

Publisher: Academic Press

Published: 2020-06-21

Total Pages: 385

ISBN-13: 0128184396

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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 ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data


Future of solar photovoltaic

Future of solar photovoltaic

Author: International Renewable Energy Agency IRENA

Publisher: International Renewable Energy Agency (IRENA)

Published: 2019-11-01

Total Pages: 145

ISBN-13: 9292601989

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This study presents options to fully unlock the world’s vast solar PV potential over the period until 2050. It builds on IRENA’s global roadmap to scale up renewables and meet climate goals.


Artificial Intelligence for Materials Science

Artificial Intelligence for Materials Science

Author: Yuan Cheng

Publisher: Springer Nature

Published: 2021-03-26

Total Pages: 231

ISBN-13: 3030683109

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Machine learning methods have lowered the cost of exploring new structures of unknown compounds, and can be used to predict reasonable expectations and subsequently validated by experimental results. As new insights and several elaborative tools have been developed for materials science and engineering in recent years, it is an appropriate time to present a book covering recent progress in this field. Searchable and interactive databases can promote research on emerging materials. Recently, databases containing a large number of high-quality materials properties for new advanced materials discovery have been developed. These approaches are set to make a significant impact on human life and, with numerous commercial developments emerging, will become a major academic topic in the coming years. This authoritative and comprehensive book will be of interest to both existing researchers in this field as well as others in the materials science community who wish to take advantage of these powerful techniques. The book offers a global spread of authors, from USA, Canada, UK, Japan, France, Russia, China and Singapore, who are all world recognized experts in their separate areas. With content relevant to both academic and commercial points of view, and offering an accessible overview of recent progress and potential future directions, the book will interest graduate students, postgraduate researchers, and consultants and industrial engineers.


Machine Learning in Chemistry

Machine Learning in Chemistry

Author: Jon Paul Janet

Publisher: American Chemical Society

Published: 2020-05-28

Total Pages: 189

ISBN-13: 0841299005

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Recent advances in machine learning or artificial intelligence for vision and natural language processing that have enabled the development of new technologies such as personal assistants or self-driving cars have brought machine learning and artificial intelligence to the forefront of popular culture. The accumulation of these algorithmic advances along with the increasing availability of large data sets and readily available high performance computing has played an important role in bringing machine learning applications to such a wide range of disciplines. Given the emphasis in the chemical sciences on the relationship between structure and function, whether in biochemistry or in materials chemistry, adoption of machine learning by chemistsderivations where they are important


Accelerated Materials Discovery

Accelerated Materials Discovery

Author: Phil De Luna

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2022-02-21

Total Pages: 215

ISBN-13: 3110738082

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Typical timelines to go from discovery to impact in the advanced materials sector are between 10 to 30 years. Advances in robotics and artificial intelligence are poised to accelerate the discovery and development of new materials dramatically. This book is a primer for any materials scientist looking to future-proof their careers and get ahead of the disruption that artificial intelligence and robotic automation is just starting to unleash. It is meant to be an overview of how we can use these disruptive technologies to augment and supercharge our abilities to discover new materials that will solve world’s biggest challenges. Written by world leading experts on accelerated materials discovery from academia (UC Berkeley, Caltech, UBC, Cornell, etc.), industry (Toyota Research Institute, Citrine Informatics) and national labs (National Research Council of Canada, Lawrence Berkeley National Labs).


Deep Learning with Azure

Deep Learning with Azure

Author: Mathew Salvaris

Publisher: Apress

Published: 2018-08-24

Total Pages: 298

ISBN-13: 1484236793

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Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer. Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI? Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI. What You'll Learn Become familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AI Use pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more) Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolving Discover the options for training and operationalizing deep learning models on Azure Who This Book Is For Professional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful.