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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Neural Network Water Model Based on the MB-Pol Many-Body Potential

The MB-pol many-body potential accurately predicts many properties of water, including cluster, liquid phase, and vapor–liquid equilibrium properties, but its high computational cost can make applying it in large-scale simulations quite challenging. Here, in order to address this limitation, we developed a “deep potential” neural network (DPMD) model based on the MB-pol potential for water. We find that a DPMD model trained on mostly liquid configurations yields a good description of the bulk liquid phase but severely underpredicts vapor–liquid coexistence densities. By contrast, adding cluster configurations to the neural network training set leads to a good agreement for the vapor coexistence densities. Liquid phase densities under supercooled conditions are also represented well, even though they were not included in the training set. These results confirm that neural network models can combine accuracy and transferability if sufficient attention is given to the construction of a representative training set for the target system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Revealing micro-asperity rupture through finite source inversion

Laboratory studies of acoustic emission events with seismic signatures have used the point source assumption. Given the millimeter-scale size and clustering of the asperities and the centimeter-scale distances to the sensors, the records can convey information about the source complexity that is not fully revealed in a point source inversion. We develop a finite source inversion technique following Hartzell and Heaton, 1983 to compute the spatiotemporal distribution of slip during rupture of asperities and asperity clusters. First inversions of purely synthetic signals reveal some of the applicability and limitations of the algorithm. A preliminary inversion of one recorded event is included for demonstration. With much further sensitivity analysis and modeling improvements, the resulting slip profiles will provide a new tool for understanding the frictional strength and rupture behaviors of self-similar rough-rough interfaces at any scale.

Parker, JM↗
DOE OSTI2020

Accurate prediction of core-level spectra of radicals at density functional theory cost via square gradient minimization and recoupling of mixed configurations

State-specific orbital optimized approaches are more accurate at predicting core-level spectra than traditional linear-response protocols, but their utility had been restricted due to the risk of "variational collapse" down to the ground state. We employ the recently developed square gradient minimization algorithm to reliably avoid variational collapse and study the effectiveness of orbital optimized density functional theory (DFT) at predicting second period element 1s core-level spectra of open-shell systems. Several density functionals (including SCAN, B3LYP, and ωB97X-D3) are found to predict excitation energies from the core to singly occupied levels with high accuracy (≤0.3 eV RMS error) against available experimental data. Higher excited states are, however, more challenging by virtue of being intrinsically multiconfigurational. We thus present a configuration interaction inspired route to self-consistently recouple single determinant mixed configurations obtained from DFT, in order to obtain approximate doublet states. This recoupling scheme is used to predict the C K-edge spectra of the allyl radical, the O K-edge spectra of CO + , and the N K-edge of NO 2 with high accuracy relative to experiment, indicating substantial promise in using this approach for the computation of core-level spectra for doublet species [vs more traditional time dependent DFT, equation of motion coupled cluster singles and doubles (EOM-CCSD), or using unrecoupled mixed configurations]. Finally, we also present general guidelines for computing core-excited states from orbital optimized DFT.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Cosmology with stacked cluster weak lensing and cluster–galaxy cross-correlations

ABSTRACT Cluster weak lensing is a sensitive probe of cosmology, particularly the amplitude of matter clustering σ8 and matter density parameter Ωm. The main nuisance parameter in a cluster weak lensing cosmological analysis is the scatter between the true halo mass and the relevant cluster observable, denoted $\sigma _{\ln M_\mathrm{ c}}$. We show that combining the cluster weak lensing observable ΔΣ with the projected cluster–galaxy cross-correlation function wp,cg and galaxy autocorrelation function wp,gg can break the degeneracy between σ8 and $\sigma _{\ln M_\mathrm{ c}}$ to achieve tight, per cent-level constraints on σ8. Using a grid of cosmological N-body simulations, we compute derivatives of ΔΣ, wp,cg, and wp,gg with respect to σ8, Ωm, $\sigma _{\ln M_\mathrm{ c}}$, and halo occupation distribution (HOD) parameters describing the galaxy population. We also compute covariance matrices motivated by the properties of the Dark Energy Survey cluster and weak lensing survey and the BOSS CMASS galaxy redshift survey. For our fiducial scenario combining ΔΣ, wp,cg, and wp,gg measured over 0.3−30.0 h−1 Mpc, for clusters at z = 0.35−0.55 above a mass threshold Mc ≈ 2 × 1014 h−1 M⊙, we forecast a $1.4{{\ \rm per\ cent}}$ constraint on σ8 while marginalizing over $\sigma _{\ln M_\mathrm{ c}}$ and all HOD parameters. Reducing the mass threshold to 1 × 1014 h−1 M⊙ and adding a z = 0.15−0.35 redshift bin sharpens this constraint to $0.8{{\ \rm per\ cent}}$. The small-scale (rp < 3.0 h−1 Mpc) ‘mass function’ and large-scale (rp > 3.0 h−1 Mpc) ‘halo-mass cross-correlation’ regimes of ΔΣ have comparable constraining power, allowing internal consistency tests from such an analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Exploring the exact limits of the real-time equation-of-motion coupled cluster cumulant Green’s functions

In this paper, we analyze the properties of the recently proposed real-time equation-of-motion coupled-cluster (RT-EOM-CC) cumulant Green’s function approach [Rehr et al., J. Chem. Phys. 152, 174113 (2020)]. We specifically focus on identifying the limitations of the original time-dependent coupled cluster (TDCC) ansatz and propose an enhanced double TDCC ansatz, ensuring the exactness in the expansion limit. In addition, we introduce a practical cluster-analysis-based approach for characterizing the peaks in the computed spectral function from the RT-EOM-CC cumulant Green’s function approach, which is particularly useful for the assignments of satellite peaks when many-body effects dominate the spectra. Our preliminary numerical tests focus on reproducing, approximating, and characterizing the exact impurity Green’s function of the three-site and four-site single impurity Anderson models using the RT-EOM-CC cumulant Green’s function approach. The numerical tests allow us to have a direct comparison between the RT-EOM-CC cumulant Green’s function approach and other Green’s function approaches in the numerical exact limit.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Designing a parallel Feel-the-Way clustering algorithm on HPC systems

This paper introduces a new parallel clustering algorithm, named Feel-the-Way clustering algorithm, that provides better or equivalent convergence rate than the traditional clustering methods by optimizing the synchronization and communication costs. Our algorithm design centers on how to optimize three factors simultaneously: reduced synchronizations, improved convergence rate, and retained same or comparable optimization cost. To compare the optimization cost, we use the Sum of Square Error (SSE) cost as the metric, which is the sum of the square distance between each data point and its assigned clusters. Compared with the traditional MPI k-means algorithm, the new Feel-the-Way algorithm requires less communications among participating processes. As for the convergence rate, the new algorithm requires fewer number of iterations to converge. As for the optimization cost, it obtains the SSE costs that are close to the k-means algorithm. In the paper, we first design the full-step Feel-the-Way k-means clustering algorithm that can significantly reduce the number of iterations that are required by the original k-means clustering method. Next, we improve the performance of the full-step algorithm by adopting an optimized sampling-based approach, named reassignment-history-aware sampling. Our experimental results show that the optimized sampling-based Feel-the-Way method is significantly faster than the widely used k-means clustering method, and can provide comparable optimization costs. More extensive experiments with several synthetic datasets and real-world datasets (e.g., MNIST, CIFAR-10, ENRON, and PLACES-2) show that the new parallel algorithm can outperform the open source MPI k-means library by up to 110% on a high-performance computing system using 4,096 CPU cores. In addition, the new algorithm can take up to 51% fewer iterations to converge than the k-means clustering algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Simulating the absorption spectra of helium clusters (N = 70, 150, 231, 300) using a charge transfer correction to superposition of fragment single excitations

Simulations of the n = 2 absorption spectra of HeN(N = 70, 150, 231, 300) clusters are reported, with nuclear configurations sampled by path integral molecular dynamics. The electronic structure is treated by a new approach, ALMO-CIS+CT, which is a formulation of configuration interaction singles (CIS) based on absolutely localized molecular orbitals (ALMOs). The method generalizes the previously reported ALMO-CIS model [K. D. Closser et al. J. Chem. Theory Comput. 11, 5791 (2015)] to include spatially localized charge transfer (CT) effects. It is designed to recover large numbers of excited states in atomic and molecular clusters, such as the entire n = 2 Rydberg band in helium clusters. ALMO-CIS+CT is shown to recover most of the error caused by neglecting charge transfer in ALMO-CIS and has comparable accuracy to standard CIS for helium clusters. For the n = 2 band, CT stabilizes states towards the blue edge by up to 0.5 eV. ALMO-CIS+CT retains the formal cubic scaling of ALMO-CIS with respect to system size. With improvements to the implementation over that originally reported for ALMO-CIS, ALMO-CIS+CT is able to treat helium clusters with hundreds of atoms using modest computing resources. Here, a detailed simulation of the absorption spectra associated with the 2s and 2p bands of helium clusters up to 300 atoms is reported, using path integral molecular dynamics with a spherical boundary condition to generate atomic configurations at 3 K. The main features of experimentally reported fluorescence excitation spectra for helium clusters are reproduced.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

IMG-ABC: An Atlas of Biosynthetic Gene Clusters to Fuel the Discovery of Novel Secondary Metabolites

In the discovery of secondary metabolites (SMs), large-scale analysis of sequence data is a promising exploration path that remains largely underutilized due to the lack of relevant computational resources. We present IMG-ABC (https://img.jgi.doe.gov/abc/) -- An Atlas of Biosynthetic gene Clusters within the Integrated Microbial Genomes (IMG) system1. IMG-ABC is a rich repository of both validated and predicted biosynthetic clusters (BCs) in cultured isolates, single-cells and metagenomes linked with the SM chemicals they produce and enhanced with focused analysis tools within IMG. The underlying scalable framework enables traversal of phylogenetic dark matter and chemical structure space -- serving as a doorway to a new era in the discovery of novel molecules.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Cluster-Graph Fingerprinting: A Framework for Quantitative Analysis of Machine-Learned Interatomic Model Training and Simulation Data

Machine-learned interatomic models represent a significant advancement in simulation methods, extending the predictive ability of first-principles methods to previously inaccessible length and time scales. However, the data-driven nature of these models can lead to difficult-to-detect errors that can compromise prediction accuracy. To address this challenge, we introduce a novel fingerprinting approach based on the Chebyshev Interaction Model for Efficient Simulation (ChIMES) ML-IAM graph-based descriptor. Our strategy enables efficient and statistically rigorous analysis of system configurations used in ML-IAM training and those generated by their application, e.g., in molecular dynamics simulations. We demonstrate that these fingerprints can effectively assess novelty of a configuration relative to an existing data set and determine dissimilarity among individual configurations, which are two key tasks in workflows for active learning-based ML-IAM training, data set curation, and on-the-fly uncertainty quantification.

36 MATERIALS SCIENCE↗
DOE OSTI2016

The chemical homogeneity of open clusters

Determining the level of chemical homogeneity in open clusters is of fundamental importance in the study of the evolution of star-forming clouds and that of the Galactic disk. Yet limiting the initial abundance spread in clusters has been hampered by difficulties in obtaining consistent spectroscopic abundances for different stellar types. Without reference to any specific model of stellar photospheres, a model for a homogeneous cluster is that it forms a one-dimensional sequence, with any differences between members due to variations in stellar mass and observational uncertainties. I present a novel method for investigating the abundance spread in open clusters that tests this one-dimensional hypothesis at the level of observed stellar spectra, rather than constraining homogeneity using derived abundances as is traditionally done. Using high-resolution APOGEE spectra for 49 giants in M67, NGC 6819, and NGC 2420 I demonstrate that these spectra form one-dimensional sequences for each cluster. With detailed forward modeling of the spectra and Approximate Bayesian Computation, I derive strong limits on the initial abundance spread of 15 elements: <0.01 (0.02) dex for C and Fe, ≲0.015 (0.03) dex for N, O, Mg, Si, and Ni, ≲0.02 (0.03) dex for Al, Ca, and Mn, and ≲0.03 (0.05) dex for Na, S, K, Ti, and V (at 68% and 95% confidence, respectively). The strong limits on C and O imply that no pollution by massive core-collapse supernovae occurred during star formation in open clusters, which, thus, need to form within ≲6 Myr. Further development of this and related techniques will bring the power of differential abundances to stars other than solar twins in large spectroscopic surveys and will help unravel the history of star formation and chemical enrichment in the Milky Way through chemical tagging.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

IMG-ABC. A knowledge base to fuel discovery of biosynthetic gene clusters and novel secondary metabolites

In the discovery of secondary metabolites, analysis of sequence data is a promising exploration path that remains largely underutilized due to the lack of computational platforms that enable such a systematic approach on a large scale. In this work, we present IMG-ABC (https://img.jgi.doe.gov/abc), an atlas of biosynthetic gene clusters within the Integrated Microbial Genomes (IMG) system, which is aimed at harnessing the power of “big” genomic data for discovering small molecules. IMG-ABC relies on IMG’s comprehensive integrated structural and functional genomic data for the analysis of biosynthetic gene clusters (BCs) and associated secondary metabolites (SMs). SMs and BCs serve as the two main classes of objects in IMG-ABC, each with a rich collection of attributes. A unique feature of IMG-ABC is the incorporation of both experimentally validated and computationally predicted BCs in genomes as well as metagenomes, thus identifying BCs in uncultured populations and rare taxa. We demonstrate the strength of IMG-ABC’s focused integrated analysis tools in enabling the exploration of microbial secondary metabolism on a global scale, through the discovery of phenazine-producing clusters for the first time in lphaproteobacteria . IMG-ABC strives to fill the long-existent void of resources for computational exploration of the secondary metabolism universe; its underlying scalable framework enables traversal of uncovered phylogenetic and chemical structure space, serving as a doorway to a new era in the discovery of novel molecules. IMG-ABC is the largest publicly available database of predicted and experimental biosynthetic gene clusters and the secondary metabolites they produce. The system also includes powerful search and analysis tools that are integrated with IMG’s extensive genomic/metagenomic data and analysis tool kits. As new research on biosynthetic gene clusters and secondary metabolites is published and more genomes are sequenced, IMG-ABC will continue to expand, with the goal of becoming an essential component of any bioinformatic exploration of the secondary metabolism world.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

BLACK HOLE MERGERS AND BLUE STRAGGLERS FROM HIERARCHICAL TRIPLES FORMED IN GLOBULAR CLUSTERS

Hierarchical triple-star systems are expected to form frequently via close binary–binary encounters in the dense cores of globular clusters (GCs). In a sufficiently inclined triple, gravitational interactions between the inner and outer binary can cause large-amplitude oscillations in the eccentricity of the inner orbit (“Lidov–Kozai (LK) cycles”), which can lead to a collision and merger of the two inner components. In this paper we use Monte Carlo models of dense star clusters to identify all triple systems formed dynamically and we compute their evolution using a highly accurate three-body integrator which incorporates relativistic and tidal effects. We find that a large fraction of these triples evolve through a non-secular dynamical phase which can drive the inner binary to higher eccentricities than predicted by the standard secular perturbation theory (even including octupole-order terms). We place constraints on the importance of LK-induced mergers for producing: (i) gravitational wave sources detectable by Advanced LIGO (aLIGO), for triples with an inner pair of stellar black holes (BHs); and (ii) blue straggler stars, for triples with main-sequence-star components. We find a realistic aLIGO detection rate of BH mergers due to the LK mechanism of ∼1 yr{sup −1}, with about 20% of these having a finite eccentricity when they first chirp into the aLIGO frequency band. While rare, these events are likely to dominate among eccentric compact object inspirals that are potentially detectable by aLIGO. For blue stragglers, we find that the LK mechanism can contribute up to ∼10% of their total numbers in GCs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Orbital-optimized MP2.5 and its analytic gradients: Approaching CCSD(T) quality for noncovalent interactions

Orbital-optimized MP2.5 [or simply “optimized MP2.5,” OMP2.5, for short] and its analytic energy gradients are presented. The cost of the presented method is as much as that of coupled-cluster singles and doubles (CCSD) [O(N{sup 6}) scaling] for energy computations. However, for analytic gradient computations the OMP2.5 method is only half as expensive as CCSD because there is no need to solve λ{sub 2}-amplitude equations for OMP2.5. The performance of the OMP2.5 method is compared with that of the standard second-order Møller–Plesset perturbation theory (MP2), MP2.5, CCSD, and coupled-cluster singles and doubles with perturbative triples (CCSD(T)) methods for equilibrium geometries, hydrogen transfer reactions between radicals, and noncovalent interactions. For bond lengths of both closed and open-shell molecules, the OMP2.5 method improves upon MP2.5 and CCSD by 38%–43% and 31%–28%, respectively, with Dunning's cc-pCVQZ basis set. For complete basis set (CBS) predictions of hydrogen transfer reaction energies, the OMP2.5 method exhibits a substantially better performance than MP2.5, providing a mean absolute error of 1.1 kcal mol{sup −1}, which is more than 10 times lower than that of MP2.5 (11.8 kcal mol{sup −1}), and comparing to MP2 (14.6 kcal mol{sup −1}) there is a more than 12-fold reduction in errors. For noncovalent interaction energies (at CBS limits), the OMP2.5 method maintains the very good performance of MP2.5 for closed-shell systems, and for open-shell systems it significantly outperforms MP2.5 and CCSD, and approaches CCSD(T) quality. The MP2.5 errors decrease by a factor of 5 when the optimized orbitals are used for open-shell noncovalent interactions, and comparing to CCSD there is a more than 3-fold reduction in errors. Overall, the present application results indicate that the OMP2.5 method is very promising for open-shell noncovalent interactions and other chemical systems with difficult electronic structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

P SI4 1.4: Open-source software for high-throughput quantum chemistry

PSI4 is a free and open-source ab initio electronic structure program providing implementations of Hartree–Fock, density functional theory, many-body perturbation theory, configuration interaction, density cumulant theory, symmetry-adapted perturbation theory, and coupled-cluster theory. Most of the methods are quite efficient, thanks to density fitting and multi-core parallelism. The program is a hybrid of C++ and Python, and calculations may be run with very simple text files or using the Python API, facilitating post-processing and complex workflows; method developers also have access to most of PSI4 's core functionalities via Python. Job specification may be passed using The Molecular Sciences Software Institute (MolSSI) QCSCHEMA data format, facilitating interoperability. We announce a rewrite of our top-level computation driver, and concomitant adoption of the MolSSI QCARCHIVE INFRASTRUCTURE project, makes the latest version of PSI4 well suited to distributed computation of large numbers of independent tasks. The project has fostered the development of independent software components that may be reused in other quantum chemistry programs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Discovering Hidden Geothermal Signatures using Unsupervised Machine Learning

Discovering hidden geothermal resources is a very challenging task. It requires the mining of large datasets, including various diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used Play Fairway Analysis (PFA) typically relies on subject-matter expertise to analyze site or regional data to estimate geothermal conditions and prospectivity. Here, we demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset of Southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. However, most of these systems are poorly characterized because of insufficient existing data and limited past explorative studies. This study aims to discover hidden patterns and relationships in the SWNM geothermal dataset to better understand regional hydrothermal conditions. This is achieved by applying an unsupervised machine learning algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden (latent) signatures characterizing datasets, (2) the optimal number of these signatures, (3) dominant data attributes associated with each signature, and (4) spatial distribution of the extracted signatures. Here, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, geothermal attributes at 44 locations in SWNM. NMFk successfully finds data patterns and identifies the spatial associations of hydrothermal signatures with the four physiographic provinces in SWNM (Colorado Plateau, Volcanic Field, Basin and Range, and the Rio Grande rift). The algorithm identified up to 5 hydrothermal signatures in the SWNM datasets that differentiate between low- and medium-temperature hydrothermal systems in different provinces. Also, the algorithm identifies two medium-temperature hydrothermal systems in SWNM that require further exploration for geothermal resource development. Based on our analyses, 12 of the attributes are important to identify medium-temperature hydrothermal systems, and the remaining six attributes are critical to characterize low-temperature hydrothermal systems. Based on the obtained results, we identify potential physiographic provinces for further exploration to characterize them as geothermal resources. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored areas.

58 GEOSCIENCES↗
DOE OSTI2014

Automated genomic context analysis and experimental validation platform for discovery of prokaryote transcriptional regulator functions

The clustering of genes in a pathway and the co-location of functionally related genes is widely recognized in prokaryotes. We used these characteristics to predict the metabolic involvement for a Transcriptional Regulator (TR) of unknown function, identified and confirmed its biological activity. A software tool that identifies the genes encoded within a defined genomic neighborhood for the subject TR and its homologs was developed. The output lists of genes in the genetic neighborhoods, their annotated functions, the reactants/products, and identifies the metabolic pathway in which the encoded-proteins function. When a set of TRs of known function was analyzed, we observed that their homologs frequently had conserved genomic neighborhoods that co-located the metabolically related genes regulated by the subject TR. We postulate that TR effectors are metabolites in the identified pathways; indeed the known effectors were present. We analyzed Bxe_B3018 from Burkholderia xenovorans , a TR of unknown function and predicted that this TR was related to the glycine, threonine and serine degradation. We tested the binding of metabolites in these pathways and for those that bound, their ability to modulate TR binding to its specific DNA operator sequence. Using rtPCR, we confirmed that methylglyoxal was an effector of Bxe_3018.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Density Functional Investigation of the Inclusion of Gold Clusters on a CH 3 S Self-Assembled Lattice on Au(111)

We employ first-principles density functional theoretical calculations to address the inclusion of gold (Au) clusters in a well-packed CH 3 S self-assembled lattice. We compute CH 3 S adsorption energies to quantify the energetic stability of the self-assembly and gold adsorption and dissolution energies to characterize the structural stability of a series of Au clusters adsorbed at the SAM-Au interface. Our results indicate that the inclusion of Au clusters with less than four Au atoms in the SAM-Au interface enhances the binding of CH 3 S species. In contrast, larger Au clusters destabilize the self-assembly. We attribute this effect to the low-coordinated gold atoms in the cluster. For small clusters, these low-coordinated sites have significantly different electronic properties compared to larger islands, which makes the binding with the self-assembly energetically more favorable. Our results further indicate that Au clusters in the SAM-Au interface are thermodynamically unstable and they will tend to dissolve, producing Au adatoms incorporated in the self-assembly in the form of CH 3 S-Au-SCH 3 species. This is due to the strong S-Au bond which stabilizes single Au adatoms in the self-assembly. Our results provide solid insight into the impact of adatom islands at the CH 3 S-Au interface.

Allen, Darnel J.↗
DOE OSTI2024

Effective many-body interactions in reduced-dimensionality spaces through neural network models

Accurately describing properties of challenging problems in physical sciences often requires complex mathematical models that are unmanageable to tackle head on. Therefore, developing reduced-dimensionality representations that encapsulate complex correlation effects in many-body systems is crucial to advance the understanding of these complicated problems. However, a numerical evaluation of these predictive models can still be associated with a significant computational overhead. To address this challenge, in this paper we discuss a combined framework that integrates recent advances in the development of active-space representations of coupled cluster (CC) downfolded Hamiltonians with neural network approaches. The primary objective of this effort is to train neural networks to eliminate the computationally expensive steps required for evaluating hundreds or thousands of Hugenholtz diagrams, which correspond to multidimensional tensor contractions necessary for evaluating a many-body form of downfolded effective Hamiltonians. Using small molecular systems (the H 2 O and HF molecules) as examples, we demonstrate that training neural networks employing effective Hamiltonians for a few nuclear geometries of molecules can accurately interpolate or extrapolate their forms to other geometrical configurations characterized by different intensities of correlation effects. We also discuss differences between effective interactions that define CC downfolded Hamiltonians with those of bare Hamiltonians defined by Coulomb interactions in the active spaces. Published by the American Physical Society 2024

97 MATHEMATICS AND COMPUTING↗