Modeling electric response of materials, a million atoms at a time
Researchers in the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed a machine learning framework that can predict with quantum-level accuracy how materials respond to electric fields, up to the scale of a million atoms—vastly accelerating simulations beyond quantum mechanical methods, which can model only a few hundred atoms at
Nonreciprocal light speed control achieved using cavity magnonics device
The reliable manipulation of the speed at which light travels through objects could have valuable implications for the development of various advanced technologies, including high-speed communication systems and quantum information processing devices. Conventional methods for manipulating the speed of light, such as techniques leveraging so-called electromagnetically induced transparency (EIT) effects, work by utilizing quantum interference