Methods and Applications of Autonomous Experimentation

✍ Scribed by Marcus M. Noack; Daniela Ushizima

• Publisher: CRC Press
• Year: 2023
• Tongue: English
• Leaves: 445
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Endorsements
Contents
List of Figures
List of Tables
Foreword
Preface
Contributors
SECTION I: Introduction
CHAPTER 1: Autonomous Experimentation in Practice
CHAPTER 2: A Friendly Mathematical Perspective on Autonomous Experimentation
CHAPTER 3: A Perspective on Machine Learning for Autonomous Experimentation
SECTION II: Methods, Mathematics, and Algorithms
CHAPTER 4: Gaussian Processes
CHAPTER 5: Uncertainty Quantification
CHAPTER 6: Surrogate Model Guided Optimization
CHAPTER 7: Artificial Neural Networks
CHAPTER 8: Artificial Intelligence Driven Experiments at User Facilities
CHAPTER 9: Reinforcement Learning
SECTION III: Applications
CHAPTER 10: Autonomous Synchrotron X-Ray Scattering and Diffraction
CHAPTER 11: Autonomous Infrared Absorption Spectroscopy
CHAPTER 12: Autonomous Hyperspectral Scanning Tunneling Spectroscopy
CHAPTER 13: Autonomous Control and Analysis of Fabricated Ecosystems
CHAPTER 14: Autonomous Neutron Experiments
CHAPTER 15: Material Discovery in Poorly Explored High-Dimensional Targeted Spaces
CHAPTER 16: Autonomous Optical Microscopy for Exploring Nucleation and Growth of DNA Crystals
CHAPTER 17: Constrained Autonomous Modeling of Metal-Mineral Adsorption
CHAPTER 18: Live Autonomous Beamline Experiments: Physics In the Loop
SECTION IV: A Guide through Autonomous Experimentation
CHAPTER 19: A Closed Loop of Diverse Disciplines
CHAPTER 20: Analysis of Raw Data
CHAPTER 21: Autonomous Intelligent Decision Making
CHAPTER 22: Data Infrastructure
Bibliography
Index