ABSTRACT
Precise control is an essential and elusive quality of emerging self-driving transmission electron microscopes (TEMs). It is widely understood these instruments must be capable of performing rapid, high-volume, and arbitrary movements for practical self-driving operation. However, stage movements are difficult to automate at scale, owing to mechanical instability, hysteresis, and thermal drift. Such difficulties pose major barriers to artificial intelligence-directed microscope designs that require repeatable, precise movements. To guide design of emerging instruments, it is necessary to understand the behavior of existing mechanisms to identify rate limiting steps for full autonomy. Here, we describe a general framework to evaluate stage motion in any TEM. We define metrics to evaluate stage degrees of freedom, propose solutions to improve performance, and comment on fundamental limits to automated experimentation using present hardware.
ABSTRACT
Artificial intelligence (AI) promises to reshape scientific inquiry and enable breakthrough discoveries in areas such as energy storage, quantum computing, and biomedicine. Scanning transmission electron microscopy (STEM), a cornerstone of the study of chemical and materials systems, stands to benefit greatly from AI-driven automation. However, present barriers to low-level instrument control, as well as generalizable and interpretable feature detection, make truly automated microscopy impractical. Here, we discuss the design of a closed-loop instrument control platform guided by emerging sparse data analytics. We hypothesize that a centralized controller, informed by machine learning combining limited a priori knowledge and task-based discrimination, could drive on-the-fly experimental decision-making. This platform may unlock practical, automated analysis of a variety of material features, enabling new high-throughput and statistical studies.
ABSTRACT
The Price equation is a mathematical expression of selectionist and non-selectionist pressures on biological, cultural, and behavioral change. We use it here to specify instrumental and noninstrumental behaviors as they arise within the context of the Pavlovian autoshaping procedure, for rats trained under reward certainty and reward uncertainty. The point of departure for this endeavor is that some portion of autoshaped behavior referred to as goal-tracking appears instrumental-a function of resource attainment (the individual approaches the location where the unconditioned stimulus is to be delivered). By contrast, some other portion of autoshaped behavior referred to as sign-tracking is noninstrumental-irrelevant to making contact with the to-be-delivered unconditioned stimulus. A Price equation model is proposed that unifies our understanding of Pavlovian autoshaping behavior by isolating operant and respondent influences on goal-tracking (instrumental) and sign-tracking (noninstrumental) behavior.
