Methods and Applications of Autonomous Experimentation

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

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.

Autorentext

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.

Klappentext

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

Inhalt

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