ABSTRACT
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
part I|46 pages
Introduction
part II|128 pages
Methods, Mathematics, and Algorithms
chapter Chapter 6|21 pages
Surrogate Model Guided Optimization Algorithms and Their Potential Use in Autonomous Experimentation
part III|140 pages
Applications
chapter Chapter 16|8 pages
Autonomous Optical Microscopy for Exploring Nucleation and Growth of DNA Crystals
part IV|22 pages
A Guide through Autonomous Experimentation