30 research outputs found
Higher-order triplet interaction in energy-level modeling of excited-state absorption for an expanded porphyrin cadmium complex
Recent measurements of transmission versus fluence for a methanol-solvated asymmetric pentaazadentate porphyrin-like (APPC) cadmium complex, [(C6H4-APPC)Cd]Cl, showed the limitations of current energy-level models in predicting the transmission behavior of organic reverse saturable absorbers at fluences greater than 1 J/cm². A new model has been developed that incorporates higher-order triplet processes and accurately fits both nanosecond and picosecond transmission-versus-fluence data. This model has provided the first known determination of a higher triplet excited-state absorption cross section and lifetime for an APPC complex and also described a previously unreported feature in the transmission-versus-fluence data. The intersystem crossing rate and the previously neglected higher triplet excited-state absorption cross section are shown to govern the excited-state population dynamics of methanol-solvated [(C6H4-APPC)Cd]Cl most strongly at more-practical device energies
Machine Learning Changes the Rules for Flux Limiters
Learning to integrate non-linear equations from highly resolved direct
numerical simulations (DNSs) has seen recent interest for reducing the
computational load for fluid simulations. Here, we focus on determining a
flux-limiter for shock capturing methods. Focusing on flux limiters provides a
specific plug-and-play component for existing numerical methods. Since their
introduction, an array of flux limiters has been designed. Using the
coarse-grained Burgers' equation, we show that flux-limiters may be
rank-ordered in terms of their log-error relative to high-resolution data. We
then develop theory to find an optimal flux-limiter and present flux-limiters
that outperform others tested for integrating Burgers' equation on lattices
with , , , and coarse-grainings. We train
a continuous piecewise linear limiter by minimizing the mean-squared misfit to
6-grid point segments of high-resolution data, averaged over all segments.
While flux limiters are generally designed to have an output of
at a flux ratio of , our limiters are not bound by this rule, and yet
produce a smaller error than standard limiters. We find that our machine
learned limiters have distinctive features that may provide new rules-of-thumb
for the development of improved limiters. Additionally, we use our theory to
learn flux-limiters that outperform standard limiters across a range of values
(as opposed to at a specific fixed value) of coarse-graining, number of
discretized bins, and diffusion parameter. This demonstrates the ability to
produce flux limiters that should be more broadly useful than standard limiters
for general applications.Comment: fixed erratum: one corrected figure and some minor text update
Raman spectrometry study of phonon anharmonicity of hafnia at elevated temperatures
Raman spectra of monoclinic hafnium oxide (HfO_2) were measured at temperatures up to 1100 K. Raman peak shifts and broadenings are reported. Phonon dynamics calculations were performed with the shell model to obtain the total and partial phonon density of states, and to identify the individual motions of Hf and O atoms in the Raman modes. Correlating these motions to the thermal peak shifts and broadenings, it was found that modes involving changes in oxygen-oxygen bond length were the most anharmonic. The hafnium-dominated modes were more quasiharmonic and showed less broadening with temperature. Comparatively, the oxygen-dominated modes were more influenced by the cubic term in the interatomic potential than the hafnium-dominated modes. An approximately quadratic correlation was found between phonon-line broadening and softening
Robust design under uncertainty in quantum error mitigation
Error mitigation techniques are crucial to achieving near-term quantum
advantage. Classical post-processing of quantum computation outcomes is a
popular approach for error mitigation, which includes methods such as Zero
Noise Extrapolation, Virtual Distillation, and learning-based error mitigation.
However, these techniques have limitations due to the propagation of
uncertainty resulting from a finite shot number of the quantum measurement. To
overcome this limitation, we propose general and unbiased methods for
quantifying the uncertainty and error of error-mitigated observables by
sampling error mitigation outcomes. These methods are applicable to any
post-processing-based error mitigation approach. In addition, we present a
systematic approach for optimizing the performance and robustness of these
error mitigation methods under uncertainty, building on our proposed
uncertainty quantification methods. To illustrate the effectiveness of our
methods, we apply them to Clifford Data Regression in the ground state of the
XY model simulated using IBM's Toronto noise model.Comment: 9 pages, 5 figure
AtomSim: web-deployed atomistic dynamics simulator
AtomSim, a collection of interfaces for computational crystallography simulations, has been developed. It uses forcefield-based dynamics through physics engines such as the General Utility Lattice Program, and can be integrated into larger computational frameworks such as the Virtual Neutron Facility for processing its dynamics into scattering functions, dynamical functions etc. It is also available as a Google App Engine-hosted web-deployed interface. Examples of a quartz molecular dynamics run and a hafnium dioxide phonon calculation are presented
Self-Powered Dynamic Systems in the Framework of Optimal Uncertainty Quantification
The energy that is needed for operating a self-powered device is provided by the energy excess in the system in the form of kinetic energy, or a combination of regenerative and renewable energy. This paper addresses the energy exchange issues pertaining to regenerative and renewable energy in the development of a self-powered dynamic system. A rigorous framework that explores the supply and demand of energy for self-powered systems is developed, which considers uncertainties and optimal bounds, in the context of optimal uncertainty quantification. Examples of regenerative and solar-powered systems are given, and the analysis of self-powered feedback control for developing a fully self-powered dynamic system is discussed
Predictive Scale-Bridging Simulations through Active Learning
Throughout computational science, there is a growing need to utilize the
continual improvements in raw computational horsepower to achieve greater
physical fidelity through scale-bridging over brute-force increases in the
number of mesh elements. For instance, quantitative predictions of transport in
nanoporous media, critical to hydrocarbon extraction from tight shale
formations, are impossible without accounting for molecular-level interactions.
Similarly, inertial confinement fusion simulations rely on numerical diffusion
to simulate molecular effects such as non-local transport and mixing without
truly accounting for molecular interactions. With these two disparate
applications in mind, we develop a novel capability which uses an active
learning approach to optimize the use of local fine-scale simulations for
informing coarse-scale hydrodynamics. Our approach addresses three challenges:
forecasting continuum coarse-scale trajectory to speculatively execute new
fine-scale molecular dynamics calculations, dynamically updating coarse-scale
from fine-scale calculations, and quantifying uncertainty in neural network
models
A Raman Spectrometry Study of Phonon Anharmonicity of Zirconia at Elevated Temperatures
Raman spectra of monoclinic zirconia (ZrO_2) were measured at temperatures of up to 950 K. Temperature-dependent Raman peak shifts
and broadenings were reported and compared with prior results on hafnia
(HfO_2). Lattice dynamics calculations were performed with both shell
model and density functional theory to obtain Raman frequencies, and
the total and partial phonon density of states. These calculations were
also used to identify the individual motions of metal and oxygen atoms
in the different Raman modes. By correlating these motions to the
thermal peak shifts and broadenings, it was confirmed that modes
involving changes in oxygen-oxygen bond length were the most
anharmonic. The metal-dominated modes were found to be more
quasiharmonic, and thus showed less broadening with temperature. Mass
effects were evident by comparing the mode softening and shifting
between zirconia and hafnia