Imagine a future where computers delve into the fundamental laws of physics, unveiling new scientific horizons with minimal human intervention. This captivating vision is inching closer to reality with the advent of an innovative artificial intelligence technique we’ve coined as the Machine Learning Renormalization Group (MLRG) at UC San Diego. MLRG algorithm aims to automate one of the most pivotal tools in physics – the renormalization group – which studies how systems change across scales. Our goal is to enable AI to learn how to efficiently coarse-grain the description of physical systems in a self-supervised manner.
As condensed matter physicists, we are fascinated by the strange collective behaviors that can emerge in quantum materials. Often times, new and exotic phases of matter arise from subtle interplay between the quantum mechanics of the electrons and the complex interactions within the material. One particular phenomenon that has intrigued us is called symmetric mass generation (SMG). This refers to a situation where massless Dirac fermions, which ordinarily behave like photons, acquire a mass gap without any symmetry breaking. Naively, one would expect that generating a mass gap requires breaking a symmetry, like the Higgs mechanism. However, SMG generates a gap while preserving all symmetries!
Superconductivity is the superhero of the materials world — a phenomenon where certain materials can conduct electricity without any resistance when cooled down to a specific temperature. Imagine a world with power lines that never lose energy or transportation systems levitating on superconducting tracks, all thanks to this unique property. The challenge, however, has always been that most materials only become superconductors at extremely low temperatures. This limitation makes them impractical for most real-world applications. But what if we could find materials that become superconductors at higher, more manageable temperatures? That’s where high-temperature superconductors come into play.
Today, I am thrilled to share with you an exciting journey into the heart of quantum mechanics, where we tackle an enduring conundrum in physics – the quantum-to-classical transition. Our research team leveraged the power of artificial intelligence to provide a fresh perspective on this age-old question. We’ve combined innovative artificial intelligence technology with quantum physics to model one of the most paradoxical concepts in quantum mechanics - Schrödinger’s Cat!
In the world of condensed matter physics, a seemingly familiar topic like Fermi liquids still holds intriguing secrets waiting to be uncovered. Although every undergraduate student learns about Fermi liquids, their surprising stability under interactions has remained fascinating. Recently, our research (arXiv:2302.12731) has shed new light on this mystery by unraveling a profound topological reason behind the stability of Fermi liquids. In this post, we will take you on an exciting journey into the uncharted territories of phase space, where we have discovered new ways to understand and classify Fermi surface anomalies.
