The US Department of Energy (DoE) recently announced the names of 83 scientists who have been shortlisted for their 2021 Early Career Research Program. The list includes four MIT faculty members: Riccardo Comin of the Department of Physics ; Netta Engelhardt from the Department of Physics and the Center for Theoretical Physics; Philip Harris of the Department of Physics and the Laboratory of Nuclear Sciences; and Mingda Li from the Department of Nuclear Science and Engineering.
Each year, the DoE selects researchers for significant funding “from the nation’s scientific workforce by supporting outstanding researchers during the crucial early career years, when many scientists are doing their most formative work.” .
Resonant Coherent Diffractive Imaging of Quantum Solids
The quantum technologies of tomorrow – more powerful computing, better navigation systems, and more precise magnetic imaging and sensing devices – rely on understanding the properties of quantum materials. Quantum materials contain unique physical characteristics and can lead to phenomena such as superconductivity. Detecting and visualizing these materials at the nanoscale will allow scientists to understand and exploit the properties of quantum materials.
Riccardo comin, the class of 1947 career development assistant professor of physics, heads the Comin Photon Scattering Lab at MIT. The group uses high-energy electromagnetic waves, or X-rays, to observe how new collective states emerge at the nanoscale in quantum materials. This is a difficult feat, because the lenses of cameras and the human eye do not work for x-rays the way they do for visible light. Conventional microscopy techniques are not well suited to visualize these complex phenomena.
To overcome this technical limitation, the Comin group worked on a “lensless” X-ray microscopy approach to image these electronic textures.
“These new imaging techniques are truly fascinating and deeply challenge our traditional methods of performing x-ray microscopy,” Comin said. “We now rely on special algorithms that can compute the task of image reconstruction that is normally taken care of by a lens. “
Support from the DoE Early Career Research program will be instrumental in the group’s work in developing and applying these new techniques to study the nanoscale organization of quantum materials of interest. Beyond the horizon of quantum materials, the availability of lensless x-ray imaging methods provides a powerful new set of tools for the characterization of catalysts, batteries, data storage devices, matter. soft and biological systems.
Emergence of space-time from quantum gravity
Few phenomena in modern physics remain as mysterious as the interior of the black hole. Black holes appear to destroy objects that fall into them, as well as information about what those objects were. Yet according to the fundamentals of quantum mechanics (the study of the behavior of subatomic particles), knowing the current state of a given system should mean knowing everything about its past and future.
General relativity and quantum mechanics are two very proven theories. When it comes to black holes, general relativity and quantum mechanics disagree on one fundamental point: whether information about the region behind the event horizon can escape and be decoded by an observer outside the black hole. . The clash between general relativity and quantum mechanics on this issue results in what is called the “black hole information paradox”. In recent years, scientists have made many connections between gravity and quantum information.
Netta Engelhardt, Biedenharn’s assistant career development professor in physics and a member of the Center for Theoretical Physics, studies quantum gravity and the black hole information paradox.
“With recent advancement in our understanding of the black hole information paradox, the link between gravity, quantum computational complexity, and black holes has new potential to shed light on some of the most fundamental questions on quantum gravity, starting with ‘What is really going on inside a black hole?’ », Says Engelhardt.
With the support of the DoE Prize, his project aims to solve the black hole information paradox by using some of the new tools and ideas at the intersection of the two theories.
Exploit the Large Hadron Collider with new insights into real-time data processing and artificial intelligence
Particle accelerators help scientists learn more about the particles that make up matter.
With nearly 17 miles in circumference, the Large Hadron Collider (LHC) at the European Nuclear Research Center is the largest and most powerful particle accelerator in the world, producing valuable information for researchers.
Researchers have spent a lot of time using data from the LHC to study new particle interactions at the highest energies. But over the next two decades, they plan to shift their focus, directing their efforts to precision measurements that target physical processes with small interaction forces and extended background rates.
As a result of these more detailed observations, physicists expect other rare and hidden processes within the Standard Model (MS) of particle physics, and potentially beyond MS, to emerge as more data accumulates.
Philippe harris, an assistant professor of physics and researcher at the Nuclear Science Laboratory, is working on a physics program to measure these smaller, more discrete processes. Specifically, with the support of DoE funding, his research aims to exploit a new measurement technique he created to identify light resonances that decay into quarks – the particles that ultimately combine to create subatomic particles.
“In conjunction with advanced artificial intelligence algorithms, this new technique can open up a multitude of unique measurements and research,” says Harris. “The fully developed state-of-the-art system will enable new measurements of the Higgs boson, new dark matter research and analyzes of a multitude of unexplored scientific phenomena.”
Multimodal neutron scattering augmented by machine learning for emerging topological materials
Topological materials are a class of quantum materials whose electronic properties provide robust protection against external influences. This robustness enables a wide range of promising applications, such as next-generation energy-less electronics and error-tolerant quantum computers.
But it is difficult to directly test materials for their topological properties. Rather, scientists typically use methods that measure manifestations of topology. One of these methods is neutron scattering, or neutron spectroscopy, a process used by scientists to evaluate materials.
Neutron scattering has particular advantages when it comes to evaluating topological quantum materials, but more information is needed to understand exactly how the massive amounts of data collected during neutron spectroscopy fit into the topology.
The DoE Early Career Research Program Award will support Mingda Li, Norman C. Rasmussen Assistant Professor of Nuclear Science and Engineering, in his machine learning approach to analyze high-dimensional neutron scattering spectra in quantum materials.
“The new approach will augment existing neutron scattering probes by measuring things that were not previously measurable,” Li said. In doing so, “it will allow for a wider discovery of hidden material states that may have electronic applications and identify topological solutions that can be used for computer memory “.