Machine Learning Meets Quantum Physics
(Sprache: Englisch)
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...
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Klappentext zu „Machine Learning Meets Quantum Physics “
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.
Inhaltsverzeichnis zu „Machine Learning Meets Quantum Physics “
Introduction to Material Modeling.- Kernel Methods for Quantum Chemistry.- Introduction to Neural Networks.- Building nonparametric n-body force fields using Gaussian process regression.- Machine-learning of atomic-scale properties based on physical principles.- Quantum Machine Learning with Response Operators in Chemical Compound Space.- Physical extrapolation of quantum observables by generalization with Gaussian Processes.- Message Passing Neural Networks.- Learning representations of molecules and materials with atomistic neural networks.- Molecular Dynamics with Neural Network Potentials.- High-Dimensional Neural Network Potentials for Atomistic Simulations.- Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights.- Active learning and Uncertainty Estimation.- Machine Learning for Molecular Dynamics on Long Timescales.- Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design.- Polymer Genome: A polymer informatics platform to accelerate polymer discovery.- Bayesian Optimization in Materials Science.- Recommender Systems for Materials Discovery.- Generative Models for Automatic Chemical Design.
Autoren-Porträt
Kristof T. Schütt studied computer science at the Technische Universität Berlin where he received the MSc. in 2012 and the Ph.D. degree in 2018.During his doctoral studies in the machine learning group of TU Berlin and at the Berlin Big Data Center, his research interests has been representation learning of atomistic systems, in particular the development of interpretable neural networks for applications in quantum chemistry.Dr. Schütt has continued this research in a postdoctoral position at the Berlin Center for Machine Learning.
Stefan Chmiela is a postdoc researcher in the Machine Learning group at Technische Universität Berlin, where he obtained his Doctor degree in computer science in 2019.
His inter-disciplinary research revolves around developing efficient machine learning methods to approximate the many-body problem, without unraveling its full combinatorial complexity. A particular focus lies on the description of atomic interactions in quantum chemistry, under consideration of the natural invariants that restrict a system's degrees of freedom.
O. Anatole von Lilienfeld is Associate Professor of Physical Chemistry at the University of Basel. Research in his laboratory deals with the development of improved methods for first principles based sampling of chemical compound space using quantum mechanics, super computers, Big Data, and machine learning.
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Previously, he was an associate professor at the Free University of Brussels. From 2013 to 2015 he was a Swiss National Science Foundation professor at the University of Basel. He worked for Argonne and Sandia National Laboratories, and from 2007 to 2010 he was a distinguished Harry S. Truman Fellow at Sandia National Laboratories, after postdoctoral work at New York University and at the University of California Los Angeles. In 2005, he was awarded a PhD in computational chemistry from EPF Lausanne. His diploma thesis work was done at ETH Zürich and the University of Cambridge. He studied chemistry at ETH Zürich, the Ecole de Chimie Polymers et Materiaux in Strasbourg, and the University of Leipzig.
He is editor in chief of the IOP journal "Machine Learning: Science and Technology", has been awarded the Feynman Prize in Nanotechnology, and is an ERC consolidator grantee.
Alexandre Tkatchenko is a Professor of Theoretical Chemical Physics at the University of Luxembourg and Visiting Professor at the Berlin Big Data Center. He obtained his bachelor degree in Computer Science and a Ph.D. in Physical Chemistry at the Universidad Autonoma Metropolitana in Mexico City.
Between 2008 and 2010, he was an Alexander von Humboldt Fellow at the Fritz Haber Institute of the Max Planck Society in Berlin.
Between 2011 and 2016, he led an independent research group at the same institute.
Tkatchenko has given more than 230 plenary/keynote/invited talks, seminars and colloquia worldwide, published more than 150 articles in peer-reviewed academic journals (h-index=57), and serves on the editorial boards of Physical Review Letters (APS) and Science Advances (AAAS).
He received a number of awards, including elected Fellow of the American Physical Society, the Gerhard Ertl Young Investigator Award of the German Physical Society, and two flagship grants from the European Research Council (ERC): a Starting Grant in 2011 and a Consolidator Grant in 2017.
His group pushes the boundaries of quantum mechanics, statistical mechanics, and machine learning to develop efficient methods to enable accurate modeling and obtain new insights into complex materials.
Koji Tsuda received B.E., M.E., and Ph.D degrees from Kyoto University, Japan, in 1994, 1995, and 1998, respectively. Subsequently, he joined former Electrotechnical Laboratory (ETL), Tsukuba, Japan, asR
Previously, he was an associate professor at the Free University of Brussels. From 2013 to 2015 he was a Swiss National Science Foundation professor at the University of Basel. He worked for Argonne and Sandia National Laboratories, and from 2007 to 2010 he was a distinguished Harry S. Truman Fellow at Sandia National Laboratories, after postdoctoral work at New York University and at the University of California Los Angeles. In 2005, he was awarded a PhD in computational chemistry from EPF Lausanne. His diploma thesis work was done at ETH Zürich and the University of Cambridge. He studied chemistry at ETH Zürich, the Ecole de Chimie Polymers et Materiaux in Strasbourg, and the University of Leipzig.
He is editor in chief of the IOP journal "Machine Learning: Science and Technology", has been awarded the Feynman Prize in Nanotechnology, and is an ERC consolidator grantee.
Alexandre Tkatchenko is a Professor of Theoretical Chemical Physics at the University of Luxembourg and Visiting Professor at the Berlin Big Data Center. He obtained his bachelor degree in Computer Science and a Ph.D. in Physical Chemistry at the Universidad Autonoma Metropolitana in Mexico City.
Between 2008 and 2010, he was an Alexander von Humboldt Fellow at the Fritz Haber Institute of the Max Planck Society in Berlin.
Between 2011 and 2016, he led an independent research group at the same institute.
Tkatchenko has given more than 230 plenary/keynote/invited talks, seminars and colloquia worldwide, published more than 150 articles in peer-reviewed academic journals (h-index=57), and serves on the editorial boards of Physical Review Letters (APS) and Science Advances (AAAS).
He received a number of awards, including elected Fellow of the American Physical Society, the Gerhard Ertl Young Investigator Award of the German Physical Society, and two flagship grants from the European Research Council (ERC): a Starting Grant in 2011 and a Consolidator Grant in 2017.
His group pushes the boundaries of quantum mechanics, statistical mechanics, and machine learning to develop efficient methods to enable accurate modeling and obtain new insights into complex materials.
Koji Tsuda received B.E., M.E., and Ph.D degrees from Kyoto University, Japan, in 1994, 1995, and 1998, respectively. Subsequently, he joined former Electrotechnical Laboratory (ETL), Tsukuba, Japan, asR
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Bibliographische Angaben
- 2020, 1st ed. 2020, XVI, 467 Seiten, 125 farbige Abbildungen, Masse: 15,6 x 23,4 cm, Kartoniert (TB), Englisch
- Herausgegeben: Kristof T. Schütt, Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, Klaus-Robert Müller
- Verlag: Springer, Berlin
- ISBN-10: 3030402444
- ISBN-13: 9783030402440
Sprache:
Englisch
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