Methods and Applications of Autonomous Experimentation

Herausgeber: Noack, Marcus; Ushizima, Daniela

• Gebundenes Buch

Produktbeschreibung

Illustrating theoretical foundations and incorporating practitioners' first-hand experience, book is a practical guide to successful Autonomous Experimentation.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis Ltd (Sales)
• Seitenzahl: 402
• Erscheinungstermin: 14. Dezember 2023
• Englisch
• Abmessung: 254mm x 178mm x 25mm
• Gewicht: 984g
• ISBN-13: 9781032314655
• ISBN-10: 1032314656
• Artikelnr.: 69032208

Autorenporträt

Marcus M. Noack received his Ph.D. in applied mathematics from Oslo University, Norway. At Lawrence Berkeley National Laboratory, he is working on stochastic function approximation, optimization and uncertainty quantification, applied to Autonomous Experimentation. Daniela Ushizima, Ph.D. in physics from the University of Sao Paulo, Brazil after majoring in computer science, has been associated with Lawrence Berkeley National Laboratory since 2007, where she investigates machine learning algorithms applied to image processing. Her primary focus has been on developing computer vision software to automate scientific data analysis.

Inhaltsangabe

Preface
Contributors
Chapter 1 Autonomous Experimentation in Practice
Kevin G. Yager
Chapter 2 A Friendly Mathematical Perspective on Autonomous Experimentation
Marcus M. Noack
Chapter 3 A Perspective on Machine Learning for Autonomous Experimentation
Joshua Schrier and Alexander J. Norquist
Chapter 4 Gaussian Processes
Marcus M. Noack
Chapter 5 Uncertainty Quantification
Mark D. Risser and Marcus M. Noack
Chapter 6 Surrogate Model Guided Optimization
Juliane Mueller
Chapter 7 Artificial Neural Networks
Daniela Ushizima
Chapter 8 NSLS2
Philip M. Maffettone, Daniel B. Allan, Andi Barbour, Thomas A. Caswell,
Dmitri Gavrilov, Marcus D. Handwell, Thomas Morris, Daniel Olds, Maksim
Rakitin, Stuart I. Campbell and Bruce Ravel
Chapter 9 Reinforcement Learning
Yixuan Sun, Krishnan Raghavan and Prasanna Balaprakash
Chapter 10 Applications of Autonomous Methods to Synchrotron X-ray
Scattering and Diffraction Experiments
Masafumi Fukuto, Yu-Chen Wiegart, Marcus M. Noack and Kevin G. Yager
Chapter 11 Autonomous Infrared Absorption Spectroscopy
Hoi-Ying Holman, Steven Lee, Liang Chen, Petrus H. Zwart and Marcus M.
Noack
Chapter 12 Autonomous Hyperspectral Scanning Tunneling Spectroscopy
Antonio Rossi, Darian Smalley, Masahiro Ishigami, Eli Rotenberg, Alexander
Weber-Barigoni and John C. Thomas
Chapter 13 Autonomous Control and Analyses of Fabricated Ecosystems
Trent R. Northern, Peter Andeer, Marcus M. Noack, Ptrus H. Zwart and
Daniela Ushizima
Chapter 14 Autonomous Neutron Experiments
Martin Boehm, David E. Perryman, Alessio De Francesco, Luisa Scaccia,
Alessandro Cunsolo, Tobias Weber, Yannick LeGoc and Paolo Mutti
Chapter 15 Material Discovery in Poorly Explored High-Dimensional Targeted
Spaces
Suchismita Sarker and Apurva Mehta
Chapter 16 Autonomous Optical Microscopy for Exploring Nucleation and
Growth of DNA Crystals
Aaron N. Michelson
Chapter 17 Constratined Autonomous Modelin of Metal-Mineral Adsorption
Elliot Chang, Linda Beverly and Haruko Wainwright
Chapter 18 Physics-In-The-Loop
Aaron Gilad Kusne
Chapter 19 A Closed Loop of Diverse Disciplines
Marucs M. Noack and Kevin G. Yager
Chapter 20 Analysis of Raw Data
Marcus M. Noack and Kevin G. Yager
Chapter 21 Autonomous Intelligent Decision Making
Marcus M. Noack and Kevin G. Yager
Chapter 22 Data Infrastructure
Marcus M. Noack and Kevin G. Yager
Bibliography
Index