LEADER 05788nam a22006015i 4500001 99125381624606421 005 20251113180745.0 006 m o d | 007 cr#cnu|||||||| 008 200603s2020 sz | o |||| 0|eng d 020 3-030-40245-2 024 7 10.1007/978-3-030-40245-7 |2doi 035 (CKB)5280000000218610 035 (MiAaPQ)EBC6303916 035 (DE-He213)978-3-030-40245-7 035 (PPN)248595024 035 (MiAaPQ)EBC6219538 035 (EXLCZ)995280000000218610 040 MiAaPQ |beng |erda |epn |cMiAaPQ |dMiAaPQ 050 4 Q325.5 |b.M334 2020 072 7 PHQ |2bicssc 072 7 SCI057000 |2bisacsh 072 7 PHQ |2thema 082 0 006.31 |223 082 006.31 245 10 Machine Learning Meets Quantum Physics / |cedited by Kristof T. Schütt, Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, Klaus-Robert Müller. 250 1st ed. 2020. 264 1 Cham : |bSpringer International Publishing : |bImprint: Springer, |c2020. 300 1 online resource (xvi, 467 pages) : |billustrations 336 text |btxt |2rdacontent 337 computer |bc |2rdamedia 338 online resource |bcr |2rdacarrier 490 1 Lecture Notes in Physics, |x1616-6361 ; |v968 505 0 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. 520 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 emerginginterdisciplinary 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. . 650 0 Quantum theory. 650 0 Mathematical physics. 650 0 Machine learning. 650 0 Chemistry, Physical and theoretical. 650 14 Quantum Physics. 650 24 Theoretical, Mathematical and Computational Physics. 650 24 Machine Learning. 650 24 Theoretical Chemistry. 700 1 Schütt, Kristof T., |eeditor. |4edt |4http://id.loc.gov/vocabulary/relators/edt 700 1 Chmiela, Stefan, |eeditor. |4edt |4http://id.loc.gov/vocabulary/relators/edt 700 1 Lilienfeld, O. Anatole von, |d1976- |eeditor. |4edt |4http://id.loc.gov/vocabulary/relators/edt 700 1 Tkatchenko, A. |q(Alexandre), |eeditor. |4edt |4http://id.loc.gov/vocabulary/relators/edt 700 1 Tsuda, Koji, |eeditor. |4edt |4http://id.loc.gov/vocabulary/relators/edt 700 1 Müller, Klaus-Robert, |eeditor. |0(orcid)0000-0002-3861-7685 |1https://orcid.org/0000-0002-3861-7685 |4edt |4http://id.loc.gov/vocabulary/relators/edt 776 08 |z3-030-40244-4 830 0 Lecture Notes in Physics, |x1616-6361 ; |v968 906 BOOK