Advances in materials are an important contributor to our technological progress, and yet the process of materials discovery and development itself is slow. Our current research process is human-centred, where human researchers design, conduct, analyse and interpret experiments, and then decide what to do next. We have built an Autonomous Research System (ARES)-an autonomous research robot capable of first-of-its-kind closed-loop iterative materials experimentation. ARES exploits advances in autonomous robotics, artificial intelligence, data sciences, and high-throughput and in situ techniques, and is able to design, execute and analyse its own experiments orders of magnitude faster than current research methods. We applied ARES to study the synthesis of singlewalled carbon nanotubes, and show that it successfully learned to grow them at targeted growth rates. ARES has broad implications for the future roles of humans and autonomous research robots, and for human-machine partnering. We believe autonomous research robots like ARES constitute a disruptive advance in our ability to understand and develop complex materials at an unprecedented rate.

We investigated memory qualities that affect judgements of whether a recollection is a personal memory or self‐knowledge. In Experiment 1, college students described three types of childhood experiences: remembered, known but not remembered, and unsure whether remembered or known. After describing the experiences, they rated their memories on several characteristics (e.g. visual detail, emotion). Remembered events were rated as containing more information on almost all the dimensions than the known events (unsure events were rated between the other two types of events). Based on the observed differences, in Experiments 2 and 3 we manipulated remember versus know ratings. Participants described a remember, know, or unsure event. Some then formed a mental image of the event while others did not. Creation and description of a mental image led participants to rate known events closer to remember. The remember/know rating is a source‐monitoring decision based on the quality of the memory. © 1998 John Wiley & Sons, Ltd.

A major technological challenge in materials research is the large and complex parameter space, which hinders experimental throughput and ultimately slows down development and implementation. In single-walled carbon nanotube (CNT) synthesis, for instance, the poor yield obtained from conventional catalysts is a result of limited understanding of input-to-output correlations. Autonomous closedloop experimentation combined with advances in machine learning (ML) is uniquely suited for highthroughput research. Among the ML algorithms available, Bayesian optimization (BO) is especially apt for exploration and optimization within such high-dimensional and complex parameter space. BO is an adaptive sequential design algorithm for finding the global optimum of a black-box objective function with the fewest possible measurements. Here, we demonstrate a promising application of BO in CNT synthesis as an efficient and robust algorithm which can (1) improve the growth rate of CNT in the BO-planner experiments over the seed experiments up to a factor 8; (2) rapidly improve its predictive power (or learning); (3) Consistently achieve good performance regardless of the number or origin of seed experiments; (4) exploit a high-dimensional, complex parameter space, and (5) achieve the former 4 tasks in just over 100 hundred experiments (~8 experimental hours)-a factor of 5× faster than our previously reported results.