335 research outputs found
A Comparison of Tension and Compression Creep in a Polymeric Composite and the Effects of Physical Aging on Creep Behavior
Experimental and analytical methods were used to investigate the similarities and differences of the effects of physical aging on creep compliance of IM7/K3B composite loaded in tension and compression. Two matrix dominated loading modes, shear and transverse, were investigated for two load cases, tension and compression. The tests, run over a range of sub-glass transition temperatures, provided material constants, material master curves and aging related parameters. Comparing results from the short-term data indicated that although trends in the data with respect to aging time and aging temperature are similar, differences exist due to load direction and mode. The analytical model used for predicting long-term behavior using short-term data as input worked equally as well for the tension or compression loaded cases. Comparison of the loading modes indicated that the predictive model provided more accurate long term predictions for the shear mode as compared to the transverse mode. Parametric studies showed the usefulness of the predictive model as a tool for investigating long-term performance and compliance acceleration due to temperature
How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning
Metamaterials are composite materials with engineered geometrical micro- and
meso-structures that can lead to uncommon physical properties, like negative
Poisson's ratio or ultra-low shear resistance. Periodic metamaterials are
composed of repeating unit-cells, and geometrical patterns within these
unit-cells influence the propagation of elastic or acoustic waves and control
dispersion. In this work, we develop a new interpretable, multi-resolution
machine learning framework for finding patterns in the unit-cells of materials
that reveal their dynamic properties. Specifically, we propose two new
interpretable representations of metamaterials, called shape-frequency features
and unit-cell templates. Machine learning models built using these feature
classes can accurately predict dynamic material properties. These feature
representations (particularly the unit-cell templates) have a useful property:
they can operate on designs of higher resolutions. By learning key coarse scale
patterns that can be reliably transferred to finer resolution design space via
the shape-frequency features or unit-cell templates, we can almost freely
design the fine resolution features of the unit-cell without changing coarse
scale physics. Through this multi-resolution approach, we are able to design
materials that possess target frequency ranges in which waves are allowed or
disallowed to propagate (frequency bandgaps). Our approach yields major
benefits: (1) unlike typical machine learning approaches to materials science,
our models are interpretable, (2) our approaches leverage multi-resolution
properties, and (3) our approach provides design flexibility.Comment: Under revie
Uncertainty Quantification of Bandgaps in Acoustic Metamaterials with Stochastic Geometric Defects and Material Properties
This paper studies the utility of techniques within uncertainty
quantification, namely spectral projection and polynomial chaos expansion, in
reducing sampling needs for characterizing acoustic metamaterial dispersion
band responses given stochastic material properties and geometric defects. A
novel method of encoding geometric defects in an interpretable, resolution
independent is showcased in the formation of input space probability
distributions. Orders of magnitude sampling reductions down to and
are achieved in the 1D and 7D input space scenarios respectively
while maintaining accurate output space probability distributions through
combining Monte Carlo, quadrature rule, and sparse grid sampling with surrogate
model fitting
In situ, 3D characterization of the deformation mechanics of a superelastic NiTi shape memory alloy single crystal under multiscale constraint
Microstructural elements in NiTi shape memory alloys (SMAs) e precipitates, phase boundaries, inclusions
14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon
Chemistry and materials science are complex. Recently, there have been great
successes in addressing this complexity using data-driven or computational
techniques. Yet, the necessity of input structured in very specific forms and
the fact that there is an ever-growing number of tools creates usability and
accessibility challenges. Coupled with the reality that much data in these
disciplines is unstructured, the effectiveness of these tools is limited.
Motivated by recent works that indicated that large language models (LLMs)
might help address some of these issues, we organized a hackathon event on the
applications of LLMs in chemistry, materials science, and beyond. This article
chronicles the projects built as part of this hackathon. Participants employed
LLMs for various applications, including predicting properties of molecules and
materials, designing novel interfaces for tools, extracting knowledge from
unstructured data, and developing new educational applications.
The diverse topics and the fact that working prototypes could be generated in
less than two days highlight that LLMs will profoundly impact the future of our
fields. The rich collection of ideas and projects also indicates that the
applications of LLMs are not limited to materials science and chemistry but
offer potential benefits to a wide range of scientific disciplines
Non-standard neutrino interactions in IceCube
Non-standard neutrino interactions (NSI) may arise in various types of new physics. Their existence would change the potential that atmospheric neutrinos encounter when traversing Earth matter and hence alter their oscillation behavior. This imprint on coherent neutrino forward scattering can be probed using high-statistics neutrino experiments such as IceCube and its low-energy extension, DeepCore. Both provide extensive data samples that include all neutrino flavors, with oscillation baselines between tens of kilometers and the diameter of the Earth.
DeepCore event energies reach from a few GeV up to the order of 100 GeV - which marks the lower threshold for higher energy IceCube atmospheric samples, ranging up to 10 TeV.
In DeepCore data, the large sample size and energy range allow us to consider not only flavor-violating and flavor-nonuniversal NSI in the μ−τ sector, but also those involving electron flavor.
The effective parameterization used in our analyses is independent of the underlying model and the new physics mass scale. In this way, competitive limits on several NSI parameters have been set in the past. The 8 years of data available now result in significantly improved sensitivities. This improvement stems not only from the increase in statistics but also from substantial improvement in the treatment of systematic uncertainties, background rejection and event reconstruction
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