☆ 4.8 Article

Autonomous experiments using active learning and AI

NATURE REVIEWS MATERIALS (2023)

Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI)

Alexander E. Siemenn et al.

Summary: Needle-in-a-Haystack problems can be found in various applications such as rare disease prediction, ecological resource management, fraud detection, and material property optimization. Current optimization algorithms are not capable of efficiently solving these challenging multidimensional problems, leading to slow convergence or being stuck in local minima. This paper presents a Zooming Memory-Based Initialization algorithm (ZoMBI) that leverages Bayesian optimization principles to quickly and effectively optimize Needle-in-a-Haystack problems, achieving significant speed-ups compared to traditional methods and discovering highly optimized solutions in fewer experiments.

NPJ COMPUTATIONAL MATERIALS (2023)

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Review Materials Science, Multidisciplinary

Machine learning in nuclear materials research

Dane Morgan et al.

Summary: This article explores the application of machine learning in nuclear materials research, particularly in complex time-dependent interactions. Through high-throughput computational and experimental data approaches, machine learning can assist researchers in developing models and making predictions, thereby improving the accuracy and efficiency of research.

CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE (2022)

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Editorial Material Multidisciplinary Sciences

CLOUD LABS: WHERE ROBOTS DO THE RESEARCH

Carrie Arnold

Summary: Numerous companies offer remote, automated workforce for conducting experiments 24/7.

NATURE (2022)

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Review Materials Science, Multidisciplinary

Autonomous experimentation systems for materials development: A community perspective

Eric Stach et al.

Summary: Materials research and development are crucial for solving world problems, and the partnership between humans and robots can accelerate technological advancements. The new paradigm brings both challenges and opportunities, requiring collaborative efforts across academia, industry, government, and funding agencies.

MATTER (2021)

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Review Chemistry, Multidisciplinary