Search

Neuromorphic computing provides a means for achieving faster and more energy efficient computations than conventional digital computers for artificial intelligence (AI). However, its current accuracy is generally less than the dominant software-based AI. The key to improving accuracy is to reduce the intrinsic randomness of memristive devices, emulating synapses in the brain for neuromorphic computing. Here using a planar device as a model system, the controlled formation of conduction channels is achieved with high oxygen vacancy concentrations through the design of sharp protrusions in the electrode gap, as observed by X-ray multimodal imaging of both oxygen stoichiometry and crystallinity. Classical molecular dynamics simulations confirm that the controlled formation of conduction channels arises from confinement of the electric field, yielding a reproducible spatial distribution of oxygen vacancies across switching cycles. This work demonstrates an effective route to control the otherwise random electroforming process by electrode design, facilitating the development of more accurate memristive devices for neuromorphic computing.

Biomaterials is an exciting and dynamic field, which uses a collection of diverse materials to achieve desired biological responses. While there is constant evolution and innovation in materials with time, biomaterials research has been hampered by the relatively long development period required. In recent years, driven by the need to accelerate materials development, the applications of machine learning in materials science has progressed in leaps and bounds. The combination of machine learning with high-throughput theoretical predictions and high-throughput experiments (HTE) has shifted the traditional Edisonian (trial and error) paradigm to a data-driven paradigm. In this review, each type of biomaterial and their key properties and use cases are systematically discussed, followed by how machine learning can be applied in the development and design process. The discussions are classified according to various types of materials used including polymers, metals, ceramics, and nanomaterials, and implants using additive manufacturing. Last, the current gaps and potential of machine learning to further aid biomaterials discovery and application are also discussed.

Atomically thin Bi2 O2 Se has emerged as a new member in 2D materials with ultrahigh carrier mobility and excellent air-stability, showing great potential for electronics and optoelectronics. In addition, its ferroelectric nature renders an ultralow thermal conductivity, making it a perfect candidate for thermoelectrics. In this work, the thermoelectric performance of 2D Bi2 O2 Se is investigated over a wide temperature range (20-300 K). A gate-tunable transition from polar optical phonon (POP) scattering to piezoelectric scattering is observed, which facilitates the capacity of drastic mobility engineering in 2D Bi2 O2 Se. Consequently, a high power factor of more than 400 µW m-1 K-2 over an unprecedented temperature range (80-200 K) is achieved, corresponding to the persistently high mobility arising from the highly gate-tunable scattering mechanism. This finding provides a new avenue for maximizing thermoelectric performance by changing the scattering mechanism and carrier mobility over a wide temperature range.