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Monolayer molecular electrodes composed of titanium oxide nanosheets (TiO2ns) or ruthenium oxide nanosheets (RuO2ns) were prepared and their activities in the oxygen reduction reaction (ORR) were evaluated to investigate the ORR active sites in oxide catalysts. In TiO2ns, the influence of physical distortion sites in the crystal structure formed by introducing oxygen vacancies was determined. The ORR activity of TiO2ns was improved by introducing physical distortion sites. In RuO2ns, the effects of both the type of crystal structure and electrochemical distortion sites arising from redox reactions on ORR performance were studied. The type of crystal structure had almost no effect on ORR activity. In contrast, electrochemical distortion sites were expected to behave as the ORR active sites because the on-set potential of the ORR was similar to the redox peak position for the RuO2ns. Thus, the distortion sites in oxide crystal structures may behave as the active sites in the ORR independent of the metal species.
Discovering knowledge from data is a quantum jump from quantity to quality, which is the characteristic and the spirit of the development of science. Symbolic regression (SR) is playing a greater role in the discovery of knowledge from data, specifically in this era of exponential data growth, because SRs are able to discover mathematical formulas from data. These formulas may provide scientifically meaningful models, especially when combined with domain knowledge. This article provides an overview of SR applications in the field of materials science and engineering. Integrating domain knowledge with SR is the key and a crucial approach, which allows gaining knowledge from data quickly, accurately, and scientifically. In the data-driven paradigm, SR allows for uncovering the underlying mechanisms of materials behavior, properties, and functions, in a wide range of areas from basic academic research to industrial applications, including experiments and computations, by providing explicit interpretable models from data, in comparison with other machine-learning “black-box” models. SR will be a powerful tool for rational and automatic materials development.
Machine learning (ML) and artificial intelligence (AI) are quickly becoming commonplace in materials research. In addition to the standard workflow of fitting a model to a large set of data in order to make predictions, the materials community is finding novel and meaningful ways to integrate AI within their work. This has led to an acceleration not only of materials design and discovery, but also of other aspects of materials research as well, including faster computational models, the development of autonomous and intelligent “robot researchers,” and the automatic discovery of physical models. In this issue, we highlight a few of these applications and argue that AI/ML is delivering real-world, practical solutions to materials problems. It is also clear that we need AI/ML methods and models, “dialects” that are better adapted to materials research.
Continued progress in artificial intelligence (AI) and associated demonstrations of superhuman performance have raised the expectation that AI can revolutionize scientific discovery in general and materials science specifically. We illustrate the success of machine learning (ML) algorithms in tasks ranging from machine vision to game playing and describe how existing algorithms can also be impactful in materials science, while noting key limitations for accelerating materials discovery. Issues of data scarcity and the combinatorial nature of materials spaces, which limit application of ML techniques in materials science, can be overcome by exploiting the rich scientific knowledge from physics and chemistry using additional AI techniques such as reasoning, planning, and knowledge representation. The integration of these techniques in materials-intelligent systems will enable AI governance of the scientific method and autonomous scientific discovery.
A fundamental design rule that nature has developed for biological machines is the intimate correlation between motion and function. One class of biological machines is molecular motors in living cells, which directly convert chemical energy into mechanical work. They coexist in every eukaryotic cell, but differ in their types of motion, the filaments they bind to, the cargos they carry, as well as the work they perform. Such natural structures offer inspiration and blueprints for constructing DNA-assembled artificial systems, which mimic their functionality. In this article, we describe two groups of cytoskeletal motors, linear and rotary motors. We discuss how their artificial analogues can be built using DNA nanotechnology. Finally, we summarize ongoing research directions and conclude that DNA origami has a bright future ahead.
At GE Research, we are combining “physics” with artificial intelligence and machine learning to advance manufacturing design, processing, and inspection, turning innovative technologies into real products and solutions across our industrial portfolio. This article provides a snapshot of how this physical plus digital transformation is evolving at GE.