Deep Clustering based on Bi-Space Association Learning
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97 MATHEMATICS AND COMPUTING↗
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Discretely Charged Dark Matter in Inflation Models Based on Holographic Space-Time
The holographic space-time (HST) model of inflation has a potential explanation for dark matter as tiny primordial black holes. Motivated by a recent paper of Barrau, we propose a version of this model where some of the inflationary black holes (IBHs), whose decay gives rise to the Hot Big Bang, carry the smallest value of a discrete symmetry charge. The fraction f of IBHs carrying this charge is difficult to estimate from first principles, but we determine it by requiring that the crossover between radiation and matter domination occurs at the correct temperature T eq ~ 1eV=10 –28 M P . The fraction is small, f ~ 2×10 –9 , so we believe this gives an extremely plausible model of dark matter.
79 ASTRONOMY AND ASTROPHYSICS↗
Effective Hamiltonians derived from equation-of-motion coupled-cluster wave functions: Theory and application to the Hubbard and Heisenberg Hamiltonians
Effective Hamiltonians, which are commonly used for fitting experimental observables, provide a coarse-grained representation of exact many-electron states obtained in quantum chemistry calculations; however, the mapping between the two is not trivial. In this contribution, we apply Bloch’s formalism to equation-of-motion coupled-cluster (EOM-CC) wave functions to rigorously derive effective Hamiltonians in the Bloch’s and des Cloizeaux’ forms. We report the key equations and illustrate the theory by application to systems with two or three unpaired electrons, which give rise to electronic states of covalent and ionic characters. We show that the Hubbard and Heisenberg Hamiltonians can be extracted directly from the so-obtained effective Hamiltonians. By establishing a quantitative connection between many-body states and simple models, the approach facilitates the analysis of the correlated wave functions. We propose a simple diagnostic for assessing the validity of the model space choice based on the overlaps between the target- and model-space states. Finally, artifacts affecting the quality of electronic structure calculations such as spin contamination are also discussed.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Chemical Complexity of Phosphorous-bearing Species in Various Regions of the Interstellar Medium
Phosphorus-related species are not known to be as omnipresent in space as hydrogen, carbon, nitrogen, oxygen, and sulfur-bearing species. Astronomers spotted very few P-bearing molecules in the interstellar medium and circumstellar envelopes. Limited discovery of the P-bearing species imposes severe constraints in modeling the P-chemistry. In this paper, we carry out extensive chemical models to follow the fate of P-bearing species in diffuse clouds, photon-dominated or photodissociation regions (PDRs), and hot cores/corinos. We notice a curious correlation between the abundances of PO and PN and atomic nitrogen. Since N atoms are more abundant in diffuse clouds and PDRs than in the hot core/corino region, PO/PN reflects <1 in diffuse clouds, ≪1 in PDRs, and >1 in the late warm-up evolutionary stage of the hot core/corino regions. During the end of the post-warm-up stage, we obtain PO/PN > 1 for hot core and <1 for its low-mass analog. We employ a radiative transfer model to investigate the transitions of some of the P-bearing species in diffuse cloud and hot core regions and estimate the line profiles. Our study estimates the required integration time to observe these transitions with ground-based and space-based telescopes. We also carry out quantum chemical computation of the infrared features of PH{sub 3}, along with various impurities. We notice that SO{sub 2} overlaps with the PH{sub 3} bending-scissoring modes around ∼1000–1100 cm{sup −1}. We also find that the presence of CO{sub 2} can strongly influence the intensity of the stretching modes around ∼2400 cm{sup −1} of PH{sub 3}.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
A dimensionless number for high-throughput design of multi-principal element alloys in directed energy deposition
The present study demonstrates the development of a dimensionless number to predict the build height in the additive manufacturing technique of directed energy deposition (DED). The build height can also be used to estimate the dendrite arm spacing and, thus, the cooling rate in the fabrication of samples. A baseline sample, 316L stainless steel, was used to fit the build height to the dimensionless number. A range of process parameters, including laser power, laser feed rate, powder flow rate, layer thickness, and hatch spacing, were varied. Based upon dendrite arm spacing, the estimated cooling rate varied between 102 and 104 K/s. Using the fitted relationship for the stainless steel, high-throughput (HT) processing of multi-principal element alloys (MPEAs) was performed. For this study, HT is the ability to fabricate a batch of 25 bulk samples (∼1 cm3) with different compositions within a 5-h period with ±10 at. % accuracy. A range of compositions using in situ alloying of elemental powders in the Fe–Ni–Cr–Mo system were made. The MPEAs' build height followed the same relationship to the dimensionless number as the 316L alloy. The dimensionless number predicts both macro and meso-scale features in HT processing, thus offering a design tool for choosing process parameters in DED additive manufacturing. Also, the ability to control or increase cooling rates can enhance the ability to promote metastability as well as control meso-scale chemical distributions of alloy samples.
Islam, Zahabul (ORCID:0000000169613209)↗
Design Space Exploration of Emerging Memory Technologies for Machine Learning Applications
Memory design space exploration methods study memory systems’ performances and limitations before implementation. The computer memory design space has grown exponentially because of the enormous growth of memory types, memory controllers, and application software. Computer simulators are commonly used for memory design space exploration. However, complex memory simulations take an enormous amount of time. Hence, in this paper, we proposed a machine learning-based design space exploration method for dynamic random-access memory and non-volatile memory systems. We applied our method to the CosmoGAN and LeNet applications to predict the following six memory response parameters: (i) bandwidth, (ii) power, (iii) average latency, (iv) average total latency, (v) memory reads, and (vi) memory writes. Our experimental results show that machine learning models can predict memory response parameter values faster than simulations. We used support vector machine, random forest, and gradient boosting machine learning models. We observed that the support vector machine provides better performance for bandwidth, average latency, and average total latency. The random forest model works better for memory reads and writes. The gradient boosting model provides superior prediction performance for power. We provide a detailed discussion on learning curve characteristics, error analysis, and memory type recommendation.
Hasan, S M Shamimul↗
An interoperable implementation of collective‐variable based enhanced sampling methods in extended phase space within the OpenMM package
Collective variable (CV)-based enhanced sampling techniques are widely used today for accelerating barrier-crossing events in molecular simulations. A class of these methods, which includes temperature accelerated molecular dynamics (TAMD)/driven-adiabatic free energy dynamics (d-AFED), unified free energy dynamics (UFED), and temperature accelerated sliced sampling (TASS), uses an extended variable formalism to achieve quick exploration of conformational space. These techniques are powerful, as they enhance the sampling of a large number of CVs simultaneously compared to other techniques. Extended variables are kept at a much higher temperature than the physical temperature by ensuring adiabatic separation between the extended and physical subsystems and employing rigorous thermostatting. Here, in this work, we present a computational platform to perform extended phase space enhanced sampling simulations using the open-source molecular dynamics engine OpenMM. The implementation allows users to have interoperability of sampling techniques, as well as employ state-of-the-art thermostats and multiple time-stepping. This work also presents protocols for determining the critical parameters and procedures for reconstructing high-dimensional free energy surfaces. As a demonstration, we present simulation results on the high dimensional conformational landscapes of the alanine tripeptide in vacuo, tetra-N-methylglycine (tetra-sarcosine) peptoid in implicit solvent, and the Trp-cage mini protein in explicit water.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Synthesis, Structural Characterization and Chemical Bonding of Sr 7 Li 6 Sn 12 and its Quaternary Derivatives with Eu and Alkaline Earth Metal (Mg, Ca, Ba) Substitutions. A Tale of Seven Li‐Containing Stannides and Two Complex Crystal Structures
In this paper, we discuss the synthesis and the structural characterization of the new ternary compound Sr 7 Li 6 Sn 12 and its six quaternary derivatives, where alkaline earth metals (Ca, Mg, Ba) and the rare earth metal Eu are substituted, yielding crystalline phases of monoclinic or orthorhombic symmetry. The title compounds were synthesized via high‐temperature solid‐state reactions of the corresponding elements. The crystal structures were determined by single‐crystal X‐ray diffraction methods. The (Sr,Ca) 7 Li 6 Sn 12 and Eu 7 (Mg,Li) 6 Sn 12 phases represent a new structure type (space group P 2/ m , No. 10) while Sr 7 Li 6 Sn 12 and the rest of the title phases crystallize in the orthorhombic base‐centered space group Cmmm (No. 65) which is similar to the Eu 7 Li 8– x Sn 10+ x ( x ≈ 2.0) phase with the Ce 7 Li 8 Ge 10 structure type (Pearson code oC 50). Careful examination of the resulting structures shows intricate disordering between Li and Sn atoms, which is governed by the total number of valence electrons. The discussion of experimental results is also supported by DFT electronic structure calculations.
Osman, Hussien H.↗
WFIP3 High Frequency Radar / Processed Data
These data were collected by Kirincich as part of the DOE Wind Forecast Improvement Project, based on support from NOAA-IOOS, WHOI, and DOE. The data consist of estimates of the near-surface horizontal (East and North) ocean currents made via High Frequency (HF) radar-based remote sensing of the Ocean backscatter spectrum. The effective measurement depth of the WHOI HF radars is 0.5 m below the ocean surface. The WHOI HF radar system, as operated during the experiment, consisted of 4 land-based sites spaced at 30-40km intervals along the area spanning from Nantucket,MA to Narragansett,RI that operate at 16 MHz and 25 MHz systems (see Kirincich et al, 2019 and Kirincich et al 2022 for details). Received doppler spectra from each were processed using the advanced methods of Kirincich et al. 2012, and Kirincich et al 2019, and unpublished work Kirincich, 2024) into radial or elliptical velocity estimates every 30 min based on a 30 min averaging window.
17 WIND ENERGY↗
Optimization and stabilization of Fermilab Booster using hybrid Bayesian/RL framework
PIPII project will raise Fermilab Booster intensity and ramp rate. Beam losses will limit average power and are hard to simulate. Presently, Booster uses operator-guided empirical tuning. This task is challenging due to high dimensionality, multiple objectives, critical safety constraints, and drifts. We developed a synergistic suite of Bayesian optimization (BO) and reinforcement learning (RL) tools to optimize and stabilize beam losses. First, active learning was used to build a rough model. Data was collected parasitically using two novel safety constraint types – nonlinear input space restrictions (based on optics model), and uncertainty constraints (to stop bad steps/beam aborts). We then applied online multi-objective BO with scalarized objectives and fitting to improve/rebalance losses, increasing safety margins by 25%. Using BO model as a safety veto, we tried several on/off-policy RL agents for long term stabilization; SAC had best performance. We found that adding contextual (state) information further improved performance, eventually integrating key knobs like linac phase and temperature into the parameter space. Long term testing is ongoing to enable operational use.
Kuklev, Nikita [Fermilab]↗
OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms
The execution of agent-based simulations (ABSs) on hardware accelerator devices such as graphics processing units (GPUs) has been shown to offer great performance potentials. However, in heterogeneous hardware environments, it can become increasingly difficult to find viable partitions of the simulation and provide implementations for different hardware devices. To automate this process, we present OpenABLext, an extension to OpenABL, a model specification language for ABSs. By providing a device-aware OpenCL backend, OpenABLext enables the co-execution of ABS on heterogeneous hardware platforms consisting of central processing units, GPUs, and field programmable gate arrays (FPGAs).We present a novel online dispatching method that efficiently profiles partitions of the simulation during run-time to optimize the hardware assignment while using the profiling results to advance the simulation itself. In addition, OpenABLext features automated conflict resolution based on user-specified rules, supports graph-based simulation spaces, and utilizes an efficient neighbor search algorithm. We show the improved performance of OpenABLext and demonstrate the potential of FPGAs in the context of ABS. We illustrate how co-execution can be used to further lower execution times. OpenABLext can be seen as an enabler to tap the computing power of heterogeneous hardware platforms for ABS.
97 MATHEMATICS AND COMPUTING↗
Cyber-Attack Detection for Photovoltaic Farms Based on Power-Electronics-Enabled Harmonic State Space Modeling
Here in this paper, a physics-data-based detection method is proposed to detect a variety of cyber-attacks in Photovoltaic (PV) farms using the power electronics-enabled harmonic state space (HSS) models, which, to our knowledge, is original. At the device level, HSS-based detection is developed to monitor harmonic vectors of individual PV converter with minimum sensor measurements, thus improving accuracy and robustness compared to Kalman Filter-based detection. At the system level that involves multiple PV converters, a clustering approach is developed to investigate attack propagation and accurately locate attack sources within a PV farm. The proposed approach is one of the first attempts to address PV security through interaction between the device and system, maximizing the accuracy and robustness at different levels. To verify the feasibility, a comprehensive attacks model is built, including single attack, coordinated attacks, and replay attacks. Besides, the impacts of irradiance changes are taken into consideration in the test scenarios. With the real-time data acquisition and hardware-in-the-loop testbed, comprehensive test results are provided to verify the feasibility of the proposed detection methodology.
42 ENGINEERING↗
Cosmological particle production and pairwise hotspots on the CMB
Heavy particles with masses much bigger than the inflationary Hubble scale H * , can get non-adiabatically pair produced during inflation through their couplings to the inflaton. If such couplings give rise to time-dependent masses for the heavy particles, then following their production, the heavy particles modify the curvature perturbation around their locations in a time-dependent and scale non-invariant manner. This results into a non-trivial spatial profile of the curvature perturbation that is preserved on superhorizon scales and eventually generates localized hot or cold spots on the CMB. We explore this phenomenon by studying the inflationary production of heavy scalars and derive the final temperature profile of the spots on the CMB by taking into account the subhorizon evolution, focusing in particular on the parameter space where pairwise hot spots (PHS) arise. When the heavy scalar has an $\mathscr{O}$(1) coupling to the inflaton, we show that for an idealized situation where the dominant background to the PHS signal comes from the standard CMB fluctuations themselves, a simple position space search based on applying a temperature cut, can be sensitive to heavy particle masses M o /H * ~$\mathscr{O}$(100). The corresponding PHS signal also modifies the CMB power spectra and bispectra, although the corrections are below (outside) the sensitivity of current measurements (searches).
79 ASTRONOMY AND ASTROPHYSICS↗
Scalable implementation of polynomial filtering for density functional theory calculation in PARSEC
In this work, we present an efficient implementation of polynomial filtering methods in PARSEC, a real-space pseudopotential based Kohn–Sham density functional theory solver. The implementation described here improves upon a Chebyshev-filtered subspace iteration algorithm used in the previous version of PARSEC. We present a hybrid polynomial filtering scheme that combines Chebyshev-filtered subspace iteration and a spectrum slicing method that partitions the spectrum into several spectral slices and uses bandpass-filtered subspace iteration to compute approximate eigenpairs within each interior slice simultaneously. We describe a procedure to partition a spectrum and construct polynomial filters. We also discuss a number of practical issues such as the use of appropriate data layouts for carrying out the computation on a two-dimensional process grid and how to achieve good load balance by allocating an appropriate number of process groups to each spectral slice. Numerical examples are presented to demonstrate the effectiveness of the hybrid polynomial filtering method as well as the superior parallel scalability of spectrum slicing in comparison to that of Chebyshev-filtered subspace iteration.
97 MATHEMATICS AND COMPUTING↗
A deep learning-based direct forecasting of CO 2 plume migration
Accurate and timely forecasts of CO 2 plume evolution in geological reservoirs are crucial for CO 2 migration detection, leakage risk assessment, and operation decision support. Conventional forecasting usually adopts a two-step strategy, first calibrating reservoir model parameters against observations using iterative inverse modeling (or history matching) and then applying the calibrated model for predictions. This method impedes real-time forecasts due to the heavy computational demand in inverse modeling and may suffer from poor prediction accuracy because of the limited observation data. In this work, we propose a deep learning-based latent space mapping framework to forecast CO 2 plume migration directly by avoiding the inverse modeling. We first use the convolutional autoencoder to map the high-dimensional complex plume extents onto low-dimensional latent space. Next, we use neural networks to learn the relationship between the observation variables and the prediction latent variables. And then for given observation data, we infer the prediction values directly. This one-step direct forecasting is computationally efficient which requires a few number of parallelizable reservoir simulations and it can provide accurate predictions with limited observations by learning the observation-prediction relationship in the reduced dimension. Therefore, our proposed method enables an in-time forecast of dynamic CO 2 plume distributions. In this work, we demonstrate the effectiveness and accuracy of our method in predicting the CO 2 plume migration using four metrics such as plume area, centroid movement distance, and plume spreading in the primary and secondary directions. And the spatio-temporal evolution patterns of plume migration under diverse geological complexities are also accurately quantified.
15 GEOTHERMAL ENERGY↗
237 Np Mössbauer Isomer Shifts: A Lesson About the Balance of Static and Dynamic Electron Correlation in Heavy Element Complexes
A large set of neptunium compounds with different oxidation states (III to VII) was assembled to study the Mössbauer isomer shift by wave function calculations and better understand covalency in f-elements complexes. The contact density approach was used to calculate the isomer shift using complete active space self-consistent field (CASSCF) multiconfiguration wave functions, as well as matrix product states [from Density Matrix Renormalization Group (DMRG) algorithms] for large active spaces. Dynamic correlation effects for the isomer shifts were treated via CASPT2 energy derivatives with respect to the nuclear radius. The CASSCF calculations appear to produce different orbital overlocalization errors for low and high Np oxidation states. For compounds with low Np oxidation numbers, the errors can be attributed to the overlocalization of the 5f orbitals. For the compounds with high Np oxidation numbers, the main errors arise from an overlocalization of ligand orbitals and concomitant to weak donation bonding. Attempts to mitigate the overlocalization errors with large active spaces using DMRG were only partially successful, showing that explicit treatment of dynamic correlation is necessary for accurate predictions of Mössbauer isomer shifts. The CASPT2 calculations perform very satisfactorily. For a subset of Np compounds, both static and dynamic correlation effects were substantial. Furthermore, a rational active space selection based on orbital entanglement diagrams proved beneficial for determining the optimal reference wave function.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Spin–spin interactions in defects in solids from mixed all-electron and pseudopotential first-principles calculations
Abstract Understanding the quantum dynamics of spin defects and their coherence properties requires an accurate modeling of spin-spin interaction in solids and molecules, for example by using spin Hamiltonians with parameters obtained from first principles calculations. We present a real-space approach based on density functional theory for the calculation of spin-Hamiltonian parameters, where only selected atoms are treated at the all-electron level, while the rest of the system is described with the pseudopotential approximation. Our approach permits calculations for systems containing more than 1000 atoms, as demonstrated for defects in diamond and silicon carbide. We show that only a small number of atoms surrounding the defect needs to be treated at the all-electron level, in order to obtain an overall all-electron accuracy for hyperfine and zero-field splitting tensors. We also present results for coherence times, computed with the cluster correlation expansion method, highlighting the importance of accurate spin-Hamiltonian parameters for quantitative predictions of spin dynamics.
36 MATERIALS SCIENCE↗