1,735 research outputs found
Estimating the peace dividend: the impact of violence on house prices in Northern Ireland
This paper exploits data on the pattern of violence across regions and over time to estimate the impact of the peace process in Northern Ireland on house prices. We begin with a linear model that estimates the average treatment effect of a conflict-related killing on house prices .showing a negative correlation between house prices and killings. We then develop an approach based on an economic model where the parameters of the statistical process are estimated for a Markov switching model where conflict and peace are treated as a latent state. From this, we are able to construct a measure of the discounted number of killings which is updated in the light of actual killings. This model naturally suggests a heterogeneous effect of killings across space and time which we use to generate estimates of the peace dividend. The economic model suggests a somewhat different pattern of estimates to the linear model. We also show that there is some evidence of spillover effects of violence in adjacent regions.
Fast, accurate, and transferable many-body interatomic potentials by symbolic regression
The length and time scales of atomistic simulations are limited by the
computational cost of the methods used to predict material properties. In
recent years there has been great progress in the use of machine learning
algorithms to develop fast and accurate interatomic potential models, but it
remains a challenge to develop models that generalize well and are fast enough
to be used at extreme time and length scales. To address this challenge, we
have developed a machine learning algorithm based on symbolic regression in the
form of genetic programming that is capable of discovering accurate,
computationally efficient manybody potential models. The key to our approach is
to explore a hypothesis space of models based on fundamental physical
principles and select models within this hypothesis space based on their
accuracy, speed, and simplicity. The focus on simplicity reduces the risk of
overfitting the training data and increases the chances of discovering a model
that generalizes well. Our algorithm was validated by rediscovering an exact
Lennard-Jones potential and a Sutton Chen embedded atom method potential from
training data generated using these models. By using training data generated
from density functional theory calculations, we found potential models for
elemental copper that are simple, as fast as embedded atom models, and capable
of accurately predicting properties outside of their training set. Our approach
requires relatively small sets of training data, making it possible to generate
training data using highly accurate methods at a reasonable computational cost.
We present our approach, the forms of the discovered models, and assessments of
their transferability, accuracy and speed
Generalizability of Functional Forms for Interatomic Potential Models Discovered by Symbolic Regression
In recent years there has been great progress in the use of machine learning
algorithms to develop interatomic potential models. Machine-learned potential
models are typically orders of magnitude faster than density functional theory
but also orders of magnitude slower than physics-derived models such as the
embedded atom method. In our previous work, we used symbolic regression to
develop fast, accurate and transferrable interatomic potential models for
copper with novel functional forms that resemble those of the embedded atom
method. To determine the extent to which the success of these forms was
specific to copper, here we explore the generalizability of these models to
other face-centered cubic transition metals and analyze their out-of-sample
performance on several material properties. We found that these forms work
particularly well on elements that are chemically similar to copper. When
compared to optimized Sutton-Chen models, which have similar complexity, the
functional forms discovered using symbolic regression perform better across all
elements considered except gold where they have a similar performance. They
perform similarly to a moderately more complex embedded atom form on properties
on which they were trained, and they are more accurate on average on other
properties. We attribute this improved generalized accuracy to the relative
simplicity of the models discovered using symbolic regression. The genetic
programming models are found to outperform other models from the literature
about 50% of the time in a variety of property predictions, with about 1/10th
the model complexity on average. We discuss the implications of these results
to the broader application of symbolic regression to the development of new
potentials and highlight how models discovered for one element can be used to
seed new searches for different elements
Collapse of Nonlinear Gravitational Waves in Moving-Puncture Coordinates
We study numerical evolutions of nonlinear gravitational waves in
moving-puncture coordinates. We adopt two different types of initial data --
Brill and Teukolsky waves -- and evolve them with two independent codes
producing consistent results. We find that Brill data fail to produce long-term
evolutions for common choices of coordinates and parameters, unless the initial
amplitude is small, while Teukolsky wave initial data lead to stable
evolutions, at least for amplitudes sufficiently far from criticality. The
critical amplitude separates initial data whose evolutions leave behind flat
space from those that lead to a black hole. For the latter we follow the
interaction of the wave, the formation of a horizon, and the settling down into
a time-independent trumpet geometry. We explore the differences between Brill
and Teukolsky data and show that for less common choices of the parameters --
in particular negative amplitudes -- Brill data can be evolved with
moving-puncture coordinates, and behave similarly to Teukolsky waves
Investigation of a Quantum Monte Carlo Protocol To Achieve High Accuracy and High-Throughput Materials Formation Energies
High-throughput calculations based on density functional theory (DFT) methods have been widely implemented in the scientific community. However, depending on both the properties of interest as well as particular chemical/structural phase space, accuracy even for correct trends remains a key challenge for DFT. In this work, we evaluate the use of quantum Monte Carlo (QMC) to calculate material formation energies in a high-throughput environment. We test the performance of automated QMC calculations on 21 compounds with high quality reference data from the Committee on Data for Science and Technology (CODATA) thermodynamic database. We compare our approach to different DFT methods as well as different pseudopotentials, showing that errors in QMC calculations can be progressively improved especially when correct pseudopotentials are used. We determine a set of accurate pseudopotentials in QMC via a systematic investigation of multiple available pseudopotential libraries. We show that using this simple automated recipe, QMC calculations can outperform DFT calculations over a wide set of materials. Out of 21 compounds tested, chemical accuracy has been obtained in formation energies of 11 structures using our QMC recipe, compared to none using DFT calculations.National Science Foundation (U.S.) (Grant DMR 1206242)National Science Foundation (U.S.) (Grant DMR 1352373)United States. Department of Energy (Award INCITE MAT307)United States. Department of Energy (Award INCITE MAT141)National Science Foundation (U.S.) (Grant XSEDE TG-DMR090027
Rapid Generation of Optimal Generalized Monkhorst-Pack Grids
Computational modeling of the properties of crystalline materials has become
an increasingly important aspect of materials research, consuming hundreds of
millions of CPU-hours at scientific computing centres around the world each
year, if not more. A routine operation in such calculations is the evaluation
of integrals over the Brillouin zone. We have previously demonstrated that
performing such integrals using generalized Monkhorst-Pack k-point grids can
roughly double the speed of these calculations relative to the widely-used
traditional Monkhorst-Pack grids, and such grids can be rapidly generated by
querying a free, internet-accessible database of pre-generated grids. To
facilitate the widespread use of generalized k-point grids, we present new
algorithms that allow rapid generation of optimized generalized Monkhorst-Pack
grids on the fly, an open-source library to facilitate their integration into
external software packages, and an open-source implementation of the database
tool that can be used offline. We also present benchmarks of the speed of our
algorithms on structures randomly selected from the Inorganic Crystal Structure
Database. For grids that correspond to a real-space supercell with at least 50
angstroms between lattice points, which is sufficient to converge density
functional theory calculations within 1 meV/atom for nearly all materials, our
algorithm finds optimized grids in an average of 0.19 seconds on a single
processing core. For 100 angstroms between real-space lattice points, our
algorithm finds optimal grids in less than 5 seconds on average
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