Bulletin of the American Physical Society
APS March Meeting 2019
Volume 64, Number 2
Monday–Friday, March 4–8, 2019; Boston, Massachusetts
Session H43: DCMP Prize Session IInvited
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Sponsoring Units: DCMP Chair: Daniel Arovas, University of California, San Diego Room: BCEC 210B |
Tuesday, March 5, 2019 2:30PM - 3:06PM |
H43.00001: Oliver E. Buckley Condensed Matter Prize talk: Elihu Abrahams: pioneer in the physics of disordered electronic systems Invited Speaker: Patrick Lee Elihu Abrahams (1927-2018) was recognized by the Buckley Prize for his early and key contributions to the theory of transport in doped semiconductors and disordered electronic systems. In a seminal work with his student Miller, he introduced the network model for hopping conductivity. Later he co-authored with Anderson, Licciardello and Ramakrishnan an influential paper (6169 citations) on the scaling theory of localization. I shall review these accomplishments and share memory of a life well lived. |
Tuesday, March 5, 2019 3:06PM - 3:42PM |
H43.00002: Oliver E. Buckley Condensed Matter Prize Talk: Coulomb gap here, there, and everywhere Invited Speaker: Boris Shklovskii In 1975 Alexei Efros and myself discovered that due to electron-electron interactions the density of localized electron states vanishes near the Fermi level as a quadratic function of the energy distance to the Fermi level. We named this phenomenon the Coulomb gap and showed that it leads to the variable range hopping conductivity which depends on the temperature T as exp[-(TES/T)1/2]. This Efros-Shklovskii law was confirmed in hundreds of experimental papers, where in many cases it describes 106 times dynamic range of conductivity. After going through the history and physics of this discovery I will review many new applications of Efros-Shklovskii law beyond lightly doped semiconductors among which the Quantum Hall Effect is the most prominent. I will also dwell on the McMillan-Shklovskii theory of the Coulomb gap emergence across a Metal-Insulator transition and the related question of screening of the Coulomb gap. |
Tuesday, March 5, 2019 3:42PM - 4:18PM |
H43.00003: APS Medal for Exceptional Achievement in Research: Topology and Other Tools in Condensed Matter Physics Invited Speaker: Bertrand I. Halperin Notions from topology have played a big role in our current understanding of both classical and quantum systems, and much of my work has been related, in one way or another, to topological ideas. However, topology has only been one tool in my research, sometimes as a starting point and sometimes an afterthought. Other ingredients have included ideas from statistics and quantum mechanics, and analyses of interaction energies and the effects of topological defects on other variables of a system. One portion of my work has focused on phase transitions that could be understood in terms of a proliferation of topological defects, and on dynamic properties that could be understood by the motion of such defects. Other portions of my work have made important use of notions of percolation. My work on quantum Hall systems, at least in retrospect, has always been related to notions of topology in Hilbert space. In my talk, I will present some examples of these applications. |
Tuesday, March 5, 2019 4:18PM - 4:54PM |
H43.00004: Aneesur Rahman Prize for Computational Physics Talk: Digital Alchemy, Machine Learning and Inverse Design for Self Assembly Invited Speaker: Sharon Glotzer From the Stone Age to the Silicon Age, the materials available to humankind define the world in which we live. The materials of tomorrow will be designed and engineered on demand, where and when they are needed, with precision and personalization. Computer simulation and machine learning both have critical roles to play in creating this future. Already, they allow — from a nearly infinite number of possibilities — the inverse design of nanoparticle building blocks optimized for self assembly into colloidal crystal structures with targeted properties. In this talk, we present a new thermodynamic computational approach to the inverse design of colloidal matter, and demonstrate its use in obtaining colloidal crystals with arbitrary complexity, engineered phase transitions, and target photonic properties. We show how machine learning can be used to autonomously identify crystal structures in hundreds of thousands of simulations, as well as to identify key alchemical attributes of particles that correlate with colloidal crystal structure. |
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