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      "body": "<html> <p>https://cdn.steemitimages.com/DQmTwB7QeE8banonoQiPaTBSTf6EazKkbVKxbvG8yFfBhsH/image.png<br> </p> <p><br></p> <p>Inverse problem solving isn't a new technique in classical physics, but this algorithm represents one of the first successful examples of an inverse problem solving method with quantum materials. And it could make searching for interesting physics a more streamlined and deliberate process for many scientists. More physicists are working in condensed matter than any other subfield of physics -- the rich diversity of condensed matter systems and phenomena provide ample unsolved problems to explore, from superconductivity and superfluidity to magnetism and topology. Experimentalists probe the macro-and microscopic properties of materials to observe the behavior and interactions of particles in materials under a strict set of controls. 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Now, if you know the desired type of physics you would like to study, you can represent that in a wave function, and the algorithm will generate all of the Hamiltonians -- or the specific models -- for which we would get that set of properties. To be more exact, the algorithm gives us Hamiltonians with that wave function as an energy eigenstate.'</p> <p><br></p> <p><em>Clark says the algorithm gives a new way to study physical phenomena such as superconductivity.</em></p> <p>'Typically, you would guess Hamiltonians that are likely to be superconducting and then try to solve them. What this algorithm -- in theory -- will allow us to do is to write down a wave function that we know superconducts and then automatically generate all of the Hamiltonians or the specific models that give that wave function as their solution. Once you have the Hamiltonians, in some sense, that gives you all the other properties of the system -- the excitation spectrum, all the finite temperature properties.</p> <p>That requires some more steps once you have the Hamiltonian, so we didn't improve that part of the research process. But what we did, we found a way to find interesting models, interesting Hamiltonians.'</p> <p>Chertkov adds, 'There are lots of wave functions people have written down for which there are no known Hamiltonians -- maybe 50 years worth. Now we can take any of these wave functions and ask if any Hamiltonians give those as eigenstates and you may end up with one model, no models, or many. For example, we are interested in spin-liquid wave functions, highly entangled quantum states with interesting topological properties.</p> <p><br></p> <p><em>Theorists have constructed many spin-liquid wave functions, but don't know which Hamiltonians give them.</em></p> <p><br></p> <p><code>In the future, our algorithm should let us find these Hamiltonians.'</code></p> <p><br></p> <p>Clark and Chertkov tested the algorithm on wave functions related to frustrated magnetism, a topic that presents interesting physics with many open questions. Frustrated magnetism occurs in a class of materials that is insulating, so the electrons don't move around, but their spins interact. Clark explains one such wave function they tested, 'The electron spins in a frustrated magnet want to be anti-aligned, like the north and south on a magnet, but can't because they live on triangles. So we make a wave function out of a linear-superposition of all of these frustrated states and we turn the crank of this algorithm, and ask, given this wavefunction, which is an interesting quantum state on a frustrated magnet, are there</p> <p><br></p> <p><code>Hamiltonians that would give it. And we found some.'</code></p> <p><br></p> <p>Chertkov says the results of the algorithm could point experimentalists in the right direction to find interesting new physics: 'That would hopefully be one way it would be used. You pick a wave function that has some kind of physics that you care about and you see what sort of interactions can give you that sort of physics, and hopefully then the models you find through this method can be looked for in experiments. And it turns out you find many models with our method.'</p> <p>Clark sums up, 'This has inverted the part of the process where we were sort of hunting in the dark. 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      "body": "<html> <p><em><strong>Working to address 'hotspots' in computer chips that degrade their performance, UCLA engineers have developed a new semiconductor material, defect-free boron arsenide, that is more effective at drawing and dissipating waste heat than any other known semiconductor or metal materials.</strong></em></p> <p><br></p> <p>https://cdn.steemitimages.com/DQmc28fiSxjrExuXKmVtG5yWjqZyjizkjjmAcx6wB5YdfXp/image.png<br></p> <p><br></p> <p>This could potentially revolutionize thermal management designs for computer processors and other electronics, or for light-based devices like LEDs.</p> <p><br></p> <p>The study was recently published in Science and was led by Yongjie Hu, UCLA assistant professor of mechanical and aerospace engineering.</p> <p>Computer processors have continued to shrink down to nanometer sizes where today there can be billions of transistors are on a single chip. This phenomenon is described under Moore's Law, which predicts that the number of transistors on a chip will double about every two years. Each smaller generation of chips helps make computers faster, more powerful and able to do more work. But doing more work also means they're generating more heat.</p> <p>Managing heat in electronics has increasingly become one of the biggest challenges in optimizing performance. High heat is an issue for two reasons. First, as transistors shrink in size, more heat is generated within the same footprint. This high heat slows down processor speeds, in particular at 'hotspots' on chips where heat concentrates and temperatures soar. Second, a lot of energy is used to keep those processors cool. If CPUs did not get as hot in the first place, then they could work faster and much less energy would be needed to keep them cool.</p> <p>The UCLA study was the culmination of several years of research by Hu and his students that included designing and making the materials, predictive modeling, and precision measurements of temperatures.</p> <p>The defect-free boron arsenide, which was made for the first time by the UCLA team, has a record-high thermal conductivity, more than three-times faster at conducting heat than currently used materials, such as silicon carbide and copper, so that heat that would otherwise concentrate in hotspots is quickly flushed away.</p> <blockquote>'This material could help greatly improve performance and reduce energy demand in all kinds of electronics, from small devices to the most advanced computer data center equipment,' Hu said. 'It has excellent potential to be integrated into current manufacturing processes because of its semiconductor properties and the demonstrated capability to scale-up this technology. It could replace current state-of-the-art semiconductor materials for computers and revolutionize the electronics industry.'</blockquote> <p>The study's other authors are UCLA graduate students in Hu's research group: Joonsang Kang, Man Li, Huan Wu, and Huuduy Nguyen.</p> <p>In addition to the impact for electronic and photonics devices, the study also revealed new fundamental insights into the physics of how heat flows through a material.</p> <blockquote>'This success exemplifies the power of combining experiments and theory in new materials discovery, and I believe this approach will continue to push the scientific frontiers in many areas, including energy, electronics, and photonics applications,' Hu said.</blockquote> <p>The research was funded by the National Science Foundation, the Air Force Office of Scientific Research, the American Chemical Society's Petroleum Research Fund, UCLA's Sustainable LA Grand Challenge, and the Anthony and Jeanne Pritzker Family Foundation.</p> </html>",
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