Research in the Department seeks to explore and explain fundamental questions that range from understanding the origin of the universe, including string theory, cosmology, and astrophysics, to understanding the visible world of colloids and the world on an ever diminishing scale, from the mesoscale to the nanoscale, condensed matter, and atomic, molecular and particle physics.
A computer image of the dissociation process of an ultracold molecule (credit: Columbia University).
Leaders in the field of ultracold molecule research from Columbia and Harvard universities are teaming up to propel understanding of the quantum mechanics of chemical reactions.
The partnership will result in the development of new, more precise techniques that will expand the field of ultracold chemistry to a currently unattainable array of molecular species and reactions, enabling new generations of experiments in fundamental physics.[...]
Principal Investigator Zelevinsky and co-researcher John Doyle, at Harvard, have received a $1 million grant over the course of three years to take their work to the next level by developing an experimental facility that will open up the field of ultracold chemistry to a much wider array of atomic and molecular species and reactions.
Ultracold atoms arranged in an ordered lattice could provide clues to the origin of high-temperature superconductivity. The array of lattice sites (valleys in the white energy surface) is produced in experiments by laser light. (Credit: Christie Chiu/Harvard University)
Since their 1986 discovery, cuprate superconductors have puzzled physicists. These copper-containing materials can conduct electricity with zero resistance at temperatures of up to 135 K, well above the maximum temperature of 30 to 40 K predicted by theory. For the last 33 years, researchers have sought to explain this enigmatic behavior but still lack a complete description. However, physicists working with ultracold atoms arranged in ordered lattices of laser light think that their experiments could soon provide needed clues. These experiments may be close to generating a model of a high-temperature superconductor in which atoms play the role of electrons. Such a system would allow researchers unprecedented control over the factors that produce superconductivity and would provide a set of tools that could lead to a solution to the high-temperature mystery...
Phase transitions occur when a substance changes from a solid, liquid or gaseous state to a different state—like ice melting or vapor condensing. During these phase transitions, there is a point at which the system can display properties of both states of matter simultaneously. A similar effect occurs when normal metals transition into superconductors—characteristics fluctuate and properties expected to belong to one state carry into the other.
Scientists at Harvard [led by Prof. Philip Kim] have developed a bismuth-based, two-dimensional superconductor that is only one nanometer thick. By studying fluctuations in this ultra-thin material as it transitions into superconductivity, the scientists gained insight into the processes that drive superconductivity more generally. Because they can carry electric currents with near-zero resistance, as they are improved, superconducting materials will have applications in virtually any technology that uses electricity...
For more informaiton, read the original research paper: * Zhao, S. Y. Frank, Nicola Poccia, Margaret G. Panetta, Cyndia Yu, et al., "Sign-Reversing Hall Effect in Atomically Thin High-Temperature Bi2.1Sr1.9CaCu2.0O8 + δ Superconductors," Phys. Rev. Lett. 122 (20 June 2019) DOI:https://doi-org/10.1103/PhysRevLett.122.247001
Congratulations to Matthew Reece for his promotion to Professor of Physics with tenure!
Reece received his PhD from Cornell in 2008, where his advisor was Csaba Csaki. He has been a professor at the Harvard Department of Physics since 2012. Matt works on theoretical particle physics, mostly related to the search for new physics beyond the Standard Model. His work ranges from physics at the LHC and future colliders to novel models and tests of dark matter, to theories of early-universe cosmology and the Swampland program.
Despite the importance of fluid flow for transporting and organizing populations, few laboratory systems exist to systematically investigate the impact of advection on their spatial evolutionary dynamics. To address this problem, A group of scholars from Harvard physics, SEAS, and FAS Center for Systems Biology, led by Prof. David Nelson, studied the morphology and genetic spatial structure of microbial colonies growing on the surface of a nutrient-laden fluid 104 to 105 times more viscous than water in Petri dishes; the extreme but finite viscosity inhibits undesired thermal convection and allows populations to effectively live at the air-liquid interface due to capillary forces. The authors discovered that S. cerevisiae (baker’s yeast) growing on a viscous liquid behave like “active matter”: They metabolically generate fluid flows many times larger than their unperturbed colony expansion speed, and that flow, in turn, can dramatically impact their colony morphology and spatial population genetics. The authors show that yeast cells generate fluid flows by consuming surrounding nutrients and decreasing the local substrate density, leading to misaligned fluid pressure and density contours, which ultimately generates vorticity via a thresholdless baroclinic instability. Numerical simulations with experimentally measured parameters demonstrate that an intense vortex ring is produced below the colony’s edge. As the viscosity of the substrate is lowered and the self-induced flow intensifies, three distinct morphologies are observed: at the highest viscosity, cell proliferation and movement produces compact circular colonies with, however, a stretched regime of exponential expansion; intermediate viscosities give rise to compact colonies with “fingers” that are usually monoclonal and then break into smaller cell clusters; at the lowest viscosity, the expanding colony fractures into many genetically diverse, mutually repelling, islandlike fragments that can colonize an entire 94-mm-diameter Petri dish within 36 hours. The authors propose a simple phenomenological model that predicts the early colony dynamics and belileve that their results provide rich opportunities to study the interplay between fluid flow and spatial population genetics for future investigations.
* See: Severine Atis, Bryan T. Weinstein, Andrew W. Murray, and David R. Nelson, "Microbial Range Expansions on Liquid Substrates
," Phys. Rev. X 9 (24 June 2019) https://doi.org/10.1103/PhysRevX.9.021058.
Fellow of Churchill College, University of Cambridge, and
Director's Visitor at the Institute for Advanced Study, Princeton, Summer 2019.
In conversation with Jacob Barandes
"The Universe Speaks in Numbers: How Modern Math Reveals Nature's Deepest Secrets"
Wednesday, June 5, 2019 @6:00PM
Science Center Hall D, One Oxford Street, Cambridge, MA 02138
In The Universe Speaks in Numbers (Basic Books, coming out on May 28, 2019), Graham Farmelo, the award-winning author of The Strangest Man and Churchill's Bomb, takes his readers on a journey from the Scientific Revolution to string theory, highlighting the role of mathematics in guiding the search for the most fundamental laws of nature. He will be joined by Harvard's own Jacob Barandes in conversation about this new book which explores how the harmonies between physics and mathematics enrich and deepen our understanding of the universe.
One of the three physicists who shared the 2018 International Center for Theoretical Physics Dirac Medal for cross-disciplinary approaches to many-body systems, Subir Sachdev, was at ICTP on March 28, 2019, for award ceremony, and sat down to discuss his career, his approach to research, and his future research plans.
Motivated by the success in many different fields of science and engineering, such as image recognition and analysis of big data in biology, machine learning has recently come to prominence in condensed matter physics – in particular, for the purpose of classification and detection of phase transitions. While symmetry-breaking phase transitions can be easily identified with machine learning, topological phase transitions are more difficult to capture; in some cases, they cannot be identified properly even with supervised learning, i.e., when the labels of the training data are provided. Such difficulty stems from the non-local nature of topological phase transitions.
Recently, two postdocs at Harvard University, Joaquin Rodriguez-Nieva and Mathias Scheurer, proposed an unsupervised machine learning approach that identifies topological phase transitions from raw data without the need of manual feature engineering. The key idea behind their approach is to construct a diffusion process on the data set to identify smooth deformations between samples (see figure, above). Using bare spin configurations as input, the approach is shown to be capable of classifying samples of the two-dimensional XY model by winding number and capture the BKT transition. They also demonstrate the success of the approach on the Ising gauge theory. In order to explain why their approach succeeds in topological classification, the authors derive a connection between the output of the machine learning method and the eigenstates of a quantum-mechanical problem.