Uncovering the effects of interface-induced ordering of liquid on crystal growth using machine learning

The process of crystallization is often understood in terms of the fundamental microstructural elements of the crystallite being formed, such as surface orientation or the presence of defects. Considerably less is known about the role of the liquid structure on the kinetics of crystal growth. Here atomistic simulations and machine learning methods are employed together to demonstrate that the liquid adjacent to solid-liquid interfaces presents significant structural ordering, which effectively reduces the mobility of atoms and slows down the crystallization kinetics. Through detailed studies of silicon and copper we discover that the extent to which liquid mobility is affected by interface-induced ordering (IIO) varies greatly with the degree of ordering and nature of the adjacent interface. Physical mechanisms behind the IIO anisotropy are explained and it is demonstrated that incorporation of this effect on a physically-motivated crystal growth model enables the quantitative prediction of the growth rate temperature dependence

Supply Shock Versus Demand Shock: The Local Effects of New Housing in Low-Income Areas

We study the local effects of new market-rate housing in low-income areas using microdata on large apartment buildings, rents, and migration. New buildings decrease nearby rents by 5 to 7 percent relative to locations slightly farther away or developed later, and they increase in-migration from low-income areas. Results are driven by a large supply effect—we show that new buildings absorb many high-income households—that overwhelms any offsetting endogenous amenity effect. The latter may be small because most new buildings go into already-changing areas. Contrary to common concerns, new buildings slow local rent increases rather than initiate or accelerate them

Optical, electronic, and dynamical phenomena in the shock compression of condensed matter

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2003.Includes bibliographical references (leaves 109-113).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Despite the study of shock wave compression of condensed matter for over 100 years, scant progress has been made in understanding the microscopic details. This thesis explores microscopic phenomena in shock compression of condensed matter including electronic excitations at the shock front, a new dynamical formulation of shock waves that links the microscopic scale to the macroscopic scale, and basic questions regarding the role of crystallinity in the propagation of electromagnetic radiation in a shocked material. In Chapter 2, the nature of electronic excitations in crystalline solid nitromethane are examined under conditions of shock compression. Density functional theory calculations are used to determine the crystal bandgap under hydrostatic stress, uniaxial strain, and shear strain for pure and defective materials. In all cases, the bandgap is not lowered enough to produce a significant population of excited states. In Chapter 3, a new multi-scale simulation method is formulated for the study of shocked materials. The method allows the molecular dynamics simulation of the system under dynamical shock conditions for orders of magnitude longer time periods than is possible using the popular non-equilibrium molecular dynamics (NEMD) approach. An example calculation is given for a model potential for silicon in which a computational speedup of 10⁵ is demonstrated. Results of these simulations are consistent with some recent experimental observations. Chapters 4 and 5 present unexpected new physical phenomena that result when light interacts with a shock wave propagating through a photonic crystal.(cont.) These new phenomena include the capture of light at the shock wave front and re-emission at a tunable pulse rate and carrier frequency across the bandgap, and bandwidth narrowing of an arbitrary signal as opposed to the ubiquitous bandwidth broadening. Reversed and anomalous Doppler shifts are also predicted in light reflected from the shock front.by Evan J. Reed.Ph.D

A data-centric framework for crystal structure identification in atomistic simulations using machine learning

Atomic-level modeling performed at large scales enables the investigation of mesoscale materials properties with atom-by-atom resolution. The spatial complexity of such cross-scale simulations renders them unsuitable for simple human visual inspection. Instead, specialized structure characterization techniques are required to aid interpretation. These have historically been challenging to construct, requiring significant intuition and effort. Here we propose an alternative framework for a fundamental structural characterization task: classifying atoms according to the crystal structure to which they belong. Our approach is data-centric and favors the employment of Machine Learning over heuristic rules of classification. A group of data-science tools and simple local descriptors of atomic structure are employed together with an efficient synthetic training set. We also introduce the first standard and publicly available benchmark data set for evaluation of algorithms for crystal-structure classification. It is demonstrated that our data-centric framework outperforms all of the most popular heuristic methods -- especially at high temperatures when lattices are the most distorted -- while introducing a systematic route for generalization to new crystal structures. Moreover, through the use of outlier detection algorithms our approach is capable of discerning between amorphous atomic motifs (i.e., noncrystalline phases) and unknown crystal structures, making it uniquely suited for exploratory materials synthesis simulations.Comment: 16 pages, 7 figure