Autonomous Experimentation is poised to revolutionize scientific experiments at advanced experimental facilities. Whereas previously, human experimenters were burdened with the laborious task of overseeing each measurement, recent advances in mathematics, machine learning and algorithms have alleviated this burden by enabling automated and intelligent decision-making, minimizing the need for human interference. Illustrating theoretical foundations and incorporating practitioners’ first-hand experiences, this book is a practical guide to successful Autonomous Experimentation.
Despite the field’s growing potential, there exists numerous myths and misconceptions surrounding Autonomous Experimentation. Combining insights from theorists, machine-learning engineers and applied scientists, this book aims to lay the foundation for future research and widespread adoption within the scientific community.
This book is particularly useful for members of the scientific community looking to improve their research methods but also contains additional insights for students and industry professionals interested in the future of the field.
Edited by:
Marcus Noack,
Daniela Ushizima
Imprint: Chapman & Hall/CRC
Country of Publication: United Kingdom
Dimensions:
Height: 254mm,
Width: 178mm,
Weight: 980g
ISBN: 9781032314655
ISBN 10: 1032314656
Series: Chapman & Hall/CRC Computational Science
Pages: 402
Publication Date: 14 December 2023
Audience:
College/higher education
,
Professional and scholarly
,
Primary
,
Undergraduate
Format: Hardback
Publisher's Status: Active
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
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.
