Delivery: Can be download immediately after purchasing. For new customer, we need process for verification from 30 mins to 12 hours.
Version: PDF/EPUB. If you need EPUB and MOBI Version, please send contact us.
Compatible Devices: Can be read on any devices.
Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.
This is a digital product.
Additional ISBNs
9783030402440
Machine Learning Meets Quantum Physics 1st Edition is written by Kristof T. Schütt; Stefan Chmiela; O. Anatole von Lilienfeld and published by Springer. The Digital and eTextbook ISBNs for Machine Learning Meets Quantum Physics are 9783030402457, 3030402452 and the print ISBNs are 9783030402457, 3030402452. Additional ISBNs for this eTextbook include 9783030402440.

Art and Merchandise in Keith Haring’s Pop Shop eBook
Bringing Aztlan to Mexican Chicago: My Life, My Work, My Art eBook
Microsoft Office - Integration eBook
Parents as Partners in Education eBook
Powerpoint 2007 eBook
Word 2007 eBook
College Geometry: A Discovery Approach (Subscription) eBook
Invitation to Public Speaking Handbook eBook
Elementary Geometry for College Students eBook
Visible Learning for Mathematics, Grades K-12: What Works Best to Optimize Student Learning eBook
The Age of Surveillance Capitalism eBook
Limnology: Lake and River Ecosystems eBook
70-740 Installation, Storage, and Compute with Windows Server 2016 eBook
The Art of Writing About Art eBook
Microsoft Office Publisher 2007 Step by Step eBook
Art Themes eBook
Handbook of Scheduling eBook
The Complete Project Management Office Handbook eBook
The Age of Surveillance Capitalism eBook
Republic eBook
Food System Transparency: Law, Science and Policy of Food and Agriculture eBook
STAT2: Modeling with Regression and ANOVA eBook
Murach's HTML5 and CSS3 (4th Edition), 4th Edition eBook
Nursing School Entrance Exams eBook
CB 8th Edition eBook
Understanding Juvenile Law eBook
Above the Fold, Revised Edition eBook
The Bloat Cure eBook
Excel 2007 eBook
Human–Computer Interaction eBook
The Data Detective: Ten Easy Rules to Make Sense of Statistics eBook
Governing Sustainable Seafood eBook
Building Codes Illustrated: A Guide to Understanding the 2015 International Building Code eBook
Schaum’s Outline of Logic 2nd edition eBook
Bring Your Own Devices (BYOD) Survival Guide eBook
Electrical Wiring Commercial, 16th Edition eBook
Access 2007 eBook 


Reviews
There are no reviews yet.