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Uncertainty Quantification in Atomistic Modeling of Metals and Its Effect on Mesoscale and Continuum Modeling: A Review
The design of next-generation alloys through the integrated computational materials engineering (ICME) approach relies on multiscale computer simulations to provide thermodynamic properties when experiments are difficult to conduct. Atomistic methods such as density functional theory (DFT) and molecular dynamics (MD) have been successful in predicting properties of never before studied compounds or phases. However, uncertainty quantification (UQ) of DFT and MD results is rarely reported due to computational and UQ methodology challenges. Over the past decade, studies that mitigate this gap have emerged. These advances are reviewed in the context of thermodynamic modeling and information exchange with mesoscale methods such as the phase-field method (PFM) and calculation of phase diagrams (CALPHAD). The importance of UQ is illustrated using properties of metals, with aluminum as an example, and highlighting deterministic, frequentist, and Bayesian methodologies. Finally, challenges facing routine uncertainty quantification and an outlook on addressing them are also presented.
AFRL Additive Manufacturing Modeling Series: Challenge 4, In Situ Mechanical Test of an IN625 Sample with Concurrent High-Energy Diffraction Microscopy Characterization
In this work, we describe 3D characterization of an additively manufactured Inconel 625 nickel-base superalloy specimen conducted during a uniaxial tension test using a suite of nondestructive x-ray techniques. High-energy diffraction microscopy in both near- and far-field modalities are employed in situ to track evolution of the material orientation and stress–strain fields at six points during the mechanical test, and these data streams are registered with micro-computed tomography reconstructions which probe the material density. This data volume was matched to a multi-modal serial sectioning characterization of the specimen taken after loading, described in this article’s companion. Twenty-eight grains which were monitored throughout the experiment were selected to form the basis for AFRL AM Modeling Series Challenge 4, Microscale Structure-to-Properties.
Combined Experimental and Modeling Study of Microwave-Assisted Methane Dehydroaromatization Process
ACS Fall Meeting 2022, Chicago, IL, August 21-25, 2022
Electromechanical memcapacitor model offering biologically plausible spiking.
In this article, we introduce a new nanoscale electromechanical device - a leaky memcapacitor - and show that it may be useful for the hardware implementation of spiking neurons. The leaky memcapacitor is a movableplate capacitor that becomes quite conductive when the plates come close to each other. The equivalent circuit of the leaky memcapacitor involves a memcapacitive and memristive system connected in parallel. In the leaky memcapacitor, resistance and capacitance depend on the same internal state variable, which is the displacement of the movable plate. We have performed a comprehensive analysis showing that several types of spiking observed in biological neurons can be implemented with the leaky memcapacitor. Significant attention is paid to the dynamic properties of the model. As in leaky memcapacitors the capacitive, leaking resistive, and reset functionalities are implemented naturally within the same device structure, their use will simplify the creation of spiking neural networks.
A multi-reservoir model for projecting drought impacts on thermoelectric disruption risk across the Texas power grid
Not Available
Comparative modeling reveals the molecular determinants of aneuploidy fitness cost in a wild yeast model
Although implicated as deleterious in many organisms, aneuploidy can underlie rapid phenotypic evolution. However, aneuploidy will be maintained only if the benefit outweighs the cost, which remains incompletely understood. To quantify this cost and the molecular determinants behind it, we generated a panel of chromosome duplications in Saccharomyces cerevisiae and applied comparative modeling and molecular validation to understand aneuploidy toxicity. We show that 74%–94% of the variance in aneuploid strains’ growth rates is explained by the cumulative cost of genes on each chromosome, measured for single-gene duplications using a genomic library, along with the deleterious contribution of small nucleolar RNAs (snoRNAs) and beneficial effects of tRNAs. Machine learning to identify properties of detrimental gene duplicates provided no support for the balance hypothesis of aneuploidy toxicity and instead identified gene length as the best predictor of toxicity. Our results present a generalized framework for the cost of aneuploidy with implications for disease biology and evolution.
Modeling Nonperturbative Field-Driven Vibronic Dynamics: Selective State Preparation and Nonlinear Spectroscopy
The partially linearized density matrix formalism for nonadiabatic dynamics is adapted to incorporate a classical external electromagentic field into the system Hamiltonian. This advancement encompasses the possibility of describing field-driven dynamics and computing a variety of linear and nonlinear spectroscopic signals beyond the perturbative limit. Here, the capabilities of the developed approach are demonstrated on a simple two-state vibronic model coupled to a bath, for which we (a) perform an exhaustive search in the field parameter space for optimal state preparation and (b) compute time-resolved transient absorption spectroscopy to monitor the effect of different pulse shapes on measurable experimental signals. While no restrictions on the form of the field have to be assumed, we focus here on Gaussian shaped (linearly) chirped pulses.
Modeling Photolytic Decomposition of Energetically Functionalized Dodecanes
The photolytic stability of explosives and energetic functional groups is of importance for those who regularly handle or are exposed to explosives in typical environmental conditions. This study models the photolytic degradation of dodecane substituted with various energetic functional groups: azide, nitro, nitrate ester, and nitramine. For the studied molecules, it was found that excitons localize on the energetic functional group, no matter where they were initially formed, and thus, the predominant degradation pathway involves the degradation of the energetic functional group. The relative trends for both 4 and 8 eV excitation energies followed with what is expected from the relative stability of the energetic functional groups to thermal and sub-shock degradation. The one notable exception was the azide functional group; more work should be done to further understand the photolytic effects on the azide functional group.
Parallelized Screening of Characterized and DFT-Modeled Bimetallic Colloidal Cocatalysts for Photocatalytic Hydrogen Evolution
Using a newly designed and developed parallelized photoreactor and colorimetric detection method, a large sampling of bimetallic co-catalysts (Pd/Sn, Pd/Mo, Pd/Ru, Pd/Pb, Pd/Ni, Ni/Sn, Mo/Sn and Pt/Sn) for photocatalytic water reduction have been tested in this work. Of these co-catalysts, the combination of palladium and tin showed the highest synergistic behavior and peak hydrogen gas production at a low relative fraction of palladium. The resulting Pd/Sn bimetallic co-catalysts were characterized using high-angle annular dark-field (HAADF) and scanning transmission electron microscopy energy dispersive X-ray spectroscopy (STEM-EDS), and specifically, investigation of the palladium/tin mixture indicated that palladium and tin elements reside within the same particle. The experimental catalytic activity for the palladium/tin mixture was compared to density functional theory-derived energy values associated with the adsorption of hydrogen onto a surface. This comparison demonstrated that the typical peak found in electrochemical Sabatier volcano plots at ΔG H* = ~0 eV was replicated in the experimental photocatalytic system with a peak activity observed at ΔG H* = -0.036 eV. Computational confirmation of the results expressed here demonstrates the efficacy of colorimetric detection of hydrogen in parallel and presents a model for increasingly rapid catalyst screening.
Influence of spin state and electron configuration on the active site and mechanism for catalytic hydrogenation on metal cation catalysts supported on NU-1000: insights from experiments and microkinetic modeling
Spin state is found to determine the mechanism and active site of catalytic hydrogenation on metal cation catalysts.
Costameric integrin and sarcoglycan protein levels are altered in a Drosophila model for Limb-girdle
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Toward UV models of kinetic mixing and portal matter. VII. A light dark photon in the 3 𝑐 3 𝐿 1 𝐴 1 𝐵 model
The kinetic mixing (KM) portal, by which the Standard Model (SM) photon mixes with a light dark photon arising from a new 𝑈(1) 𝐷 gauge group, allows for the possibility of viable scenarios of sub-GeV thermal dark matter with appropriately suppressed couplings to the SM. This KM can only occur if particles having both SM and dark quantum numbers, here termed portal matter, also exist. The presence of such types of states and the strong suggestion of a need to embed 𝑈(1) 𝐷 into a non-Abelian gauge structure not too far above the TeV scale based on the renormalization-group equation running of the 𝑈(1)𝐷 gauge coupling is potentially indicative of an enlarged group linking together the visible and dark sectors. The gauge group 𝐺 = 𝑆𝑈(3) 𝑐 × 𝑆𝑈(3) 𝐿 × 𝑈(1) 𝐴 × 𝑈(1) 𝐵 = 3 𝑐 3 𝐿 1 𝐴 1 𝐵 is perhaps the simplest setup wherein the SM and dark interactions are partially unified in a non-Abelian fashion that is not a simple product group of the form 𝐺 = 𝐺 SM × 𝐺 𝐷 encountered frequently in earlier work. The present paper describes the implications and phenomenology of this type of setup.
The triose phosphate utilization limitation of photosynthetic rate: Out of global models but important for leaf models
The effect of including triose phosphate utilization (TPU) on parameterization of the Farquhar, von Caemmerer, Berry model of photosynthetic gas exchange measurements is explored. Better fits to data are found even though TPU rarely limits photosynthesis under physiological conditions.
Development and characterization of the flight model spectrometer onboard LuSEE-Night
The Lunar Surface Electromagnetics Experiment - Night, LuSEE-Night, is a low-frequency radio astronomy experiment that will explore the cosmic Dark Ages signal on the radio-quiet far side of the Moon. Here, the LuSEE-Night carries a radio frequency spectrometer consisting of a set of antennas, analog and digital processing electronics, and will be launched by NASA’s Commercial Lunar Payload Services in 2025. The spectrometer is designed to observe the spectrum of the radio sky in the 0.5-50MHz band. The flight model (FM) of the four-channel spectrometer has been developed. The FM has been characterized for linearity, gain, noise, and their temperature dependence, confirming that the FM meets all the requirements for LuSEE-Night.