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The researchers achieved a 50MSpectra/s (megaspectra per second) measurement rate, a 100-fold increase compared to the fastest measurement of 50kSpectra/s (kilospectra per second) so far. Ideguchi describes the wide-ranging potential of this improvement.
The researchers demonstrate a novel, extremely high-performance spectrometer that can measure light with a 0.05 nanometers wavelength resolution. That’s about 1.6 million times smaller than the width of a human hair, and the same resolution that can be achieved on a device 1,000 times bigger.
The research team’s findings have far-reaching implications for the future of 2D materials. Driven by their success with MoS2, the researchers believe that their approach can be extended to other 2D materials and heterostructures, offering a powerful platform for designing and engineering next-generation 2D materials with tailored properties.
Ferroelectricity is a property of certain materials that creates a spontaneous or induced electric polarization of molecules, resulting in electric field inside the material. The polarization state can be permanent or reversed to opposite direction by the application of an external electric field. Ferroelectric materials in the form of thin films or small crystals can be used for binary information storage and non-volatile (independent of energy source) memories.
The team’s meta-optic design began by optimizing an optic comprising two metasurface lenses which serve to encode the information for a particular object classification task. Two versions were fabricated based on networks trained on a database of handwritten numbers and a database of clothing images commonly used for testing various machine learning systems. The meta-imager achieved 98.6% accuracy in handwritten numbers and 88.8% accuracy in clothing images.
The MIT team developed a new computational approach that allowed them to represent two reactants in any arbitrary orientation with respect to each other, using a type of model known as a diffusion model, which can learn which types of processes are most likely to generate a particular outcome. As training data for their model, the researchers used structures of reactants, products, and transition states that had been calculated using quantum computation methods, for 9,000 different chemical reactions.
Designing new compounds or alloys whose surfaces can be used as catalysts in chemical reactions can be a complex process relying heavily on the intuition of experienced chemists. A team of researchers at MIT has devised a new approach using machine learning that removes the need for intuition and provides more detailed information than conventional methods can practically achieve.
With the type of co-design used in this study — hardware and software developed in tandem — nanowire networks may ultimately serve a complementary role alongside silicon-based electronic devices. Brain-like memory and processing embedded in physical systems capable of continuous adapting and learning may be particularly well-suited to so-called “edge computing,” which processes complex data on the spot without requiring communication with far-off servers.
