Bibliothèque de L'IMIST/CNRST

Notes bibliographiques

Introduction / Kristof T. Schütt, Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, and Klaus-Robert Müller -- part 1. Fundamentals. Introduction to material modeling / Jan Hermann -- Kernel methods for quantum chemistry / Wiktor Pronobis and Klaus-Robert Müller -- Introduction to neural networks / Grégor Montavon -- part 2. Incorporating prior knowledge: invariances, symmetries, conservation laws. Building nonparametric n-body force fields using Gaussian process regression / Aldo Glielmo, Claudio Zeni Ádám Fekete, and Alessandro De Vita -- Machine-learning of atomic-scale properties based on physical principles / Gábor Csányi, Michael J. Willatt, and Michele Ceriotti -- Accurate molecular dynamics enabled by efficient physically constrained machine learning approaches / Stefan Chmiela, Huziel E. Sauceda, Alexandre Tkatchenko, and Klaus-Robert Müller -- Quantum machine learning with resposne operators in chemical compound space / Felix Andreas Faber, Anders S. Christensen, and O. Anatole von Lilienfield -- Physical extrapolation of quantum observables by generalization with Gaussian processes / R.A. Vargas-Hernández and R.V. Krems -- part 3. Deep learning of atomistic representations. Message passing neural networks / Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl -- Learning representations of molecules and materials with atomistic neural networks / Kristof T. Schütt, Alexandre Tkatchenko, and Klaus-Robert Müller -- part 4. Atomistic simulations. Molecular dynamics with neural network potentials / Michael Gastegger and Philipp Marquetand -- High-dimensional neural network potentials for atomistic simulations / Matti Hellström and Jörg Behler -- Construction of machine learned force fields with quantum chemical accuracy: applications and chemical insights / Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky, Klaus-Robert Müller, and Alexandre Tkatchenko -- Active learning and uncertainty estimation / Alexander Shapeev, Konstantin Gubaev, Evgenii Tsymbalov, and Evgeny Podryabinkin -- Machine learning for molecular dynamics on long timescales / Frank Noé -- part 5. Discovery and design. Database-driven high-throughput calculations and machine learning models for materials design / Rickard Armiento -- Polymer genome: a polymer informatics platform to accelerate polymer discovery / Anand Chandrasekaran, Chiho Kim, and Rampi Ramprasad -- Bayesian optimization in materials science / Zhufeng Hou and Koji Tsuda -- Recommender systems for materials discovery / Atsuto Seko, Hiroyuki Hayashi, Hisashi Kashima, and Isao Tanaka -- Generative models for automatic chemical design / Daniel Schwalbe-Koda and Rafael Gómez-Bombarelli

"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."