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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Multitask methods for predicting molecular properties from heterogeneous data

Data generation remains a bottleneck in training surrogate models to predict molecular properties. We demonstrate that multitask Gaussian process regression overcomes this limitation by leveraging both expensive and cheap data sources. In particular, we consider training sets constructed from coupled-cluster (CC) and density functional theory (DFT) data. We report that multitask surrogates can predict at CC-level accuracy with a reduction in data generation cost by over an order of magnitude. Of note, our approach allows the training set to include DFT data generated by a heterogeneous mix of exchange–correlation functionals without imposing any artificial hierarchy on functional accuracy. More generally, the multitask framework can accommodate a wider range of training set structures—including the full disparity between the different levels of fidelity—than existing kernel approaches based on Δ-learning although we show that the accuracy of the two approaches can be similar. Consequently, multitask regression can be a tool for reducing data generation costs even further by opportunistically exploiting existing data sources.

Chemistry↗
DOE OSTI2017

Propagator quantum chemical study of S-cis-(Z)-2-(2-formylethenyl)pyrrole: electronic structure and aspects of intramolecular hydrogen bond manifestation in ionization spectra

For the purpose of investigation of the electronic structures of functionalized pyrroles with potential biological activity the electronic structures and ionization spectra of S-cis-(Z)-2-(2-formylethenyl)pyrrole (FP) were calculated by the propagator quantum chemical method. The calculations were performed using the third-order algebraic diagrammatic construction method (ADC(3)) for one-particle Green´s function (electronic propagator) and the 6–31G** basis set. Going from FP (possessing the intramolecular hydrogen bond H⋯O) to its conformation FPR (without H⋯O bonding), the O1s-ionization energy and the ionization energy of the σ-type lone electron pair orbital of the O atom decrease by ~0.2 eV, which is a consequence of stopping the electron density transfer from the O atom. A strong electron density transfer through the hydrogen bond from the O atom to the NH group occurs in the nitrogen core level ionization spectrum as evidenced by a lower N1s-ionization energy of FP (by ~0.7 eV ) compared to that of FPR. The valence shell ionization spectra of FP and FPR calculated using the ADC(3) method are characterized by a high density of the satellite states. The results obtained indicate that the electronic structures of the compounds of the considered class are characterized by pronounced effects of electron correlation.

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

Silver clusters tune up electronic properties of graphene nanoflakes: A comprehensive theoretical study

Graphene-silver composites reflect a synergic effect of both components with enhanced electronic, adsorption and catalytic properties. We introduce here the detailed theoretical study on geometric, thermodynamic and electronic properties of silver clusters Ag n (n=2–10) adsorbed on graphene nanoflakes as a model for silver-graphene composites. First, a benchmark study is performed on Ag n …coronene complexes (n=2, 4) by evaluating various functional against reported high level ab-initio method for the selection of best functional for further calculations. Then, density functional theory study at the best chosen level is performed to inquire how clustering of adsorbate attached with carbon surface can impact geometric, electronic and thermodynamic properties of silver-graphene composites. Furthermore, the interaction energies are corrected for dispersion and basis set superposition error in order to get more realistic estimate of interaction energies. In progression, we investigated the dependence of interaction energy upon cluster size and dimensionality. For Ag n …coronene, the binding energies increase monotonically as the number of atoms in the cluster increases. However, for Ag n …dodecabenzocoronene, the interaction energies show a sudden drop at Ag 7 …dodecabenzocoronene. The difference in behavior between Ag n …dodecabenzocoronene and Ag n …coronene for interaction energy is attributed to edge effect present in coronene. Furthermore, coplanar orientations of the silver clusters on polyaromatic hydrocarbons have higher interaction energies. The 2D-3D transition of the silver cluster is observed for Ag 7 …coronene. CDA analysis reveals that backdonation from dodecabenzocoronene to silver clusters is more dominant in Ag 4 –Ag 7 whereas other clusters have dominant charge transfer to dodecabenzocoronene. Charge transfer analysis via constrained density functional theory (CDFT) reveals significant energy lowering from charge donor-acceptor interactions. Further energy decomposition analysis (EDA) using absolutely-localized molecular orbitals (ALMO) suggests that, while charge-transfer is important in these systems, the sum of electrostatics, Pauli repulsion, and London dispersion are far more significant as silver cluster size increases. NCI results also support the presence of van der Waals and electrostatic interactions. The hollow top is most favorite interaction sites compared to bridge top and head top binding sites over coronene lattice. The ionization potential, electron affinity, frontier molecular orbital analysis, chemical hardness, softness, chemical potential, and Fermi levels are deliberated to verify the stability and reactivity of most stable silver-graphene composites. The remarkable outcome of the current findings makes our silver-graphene composite as a potential applicant for high performance electronic and catalytic devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Resilience Design Patterns - A Structured Approach to Resilience at Extreme Scale (version 1.0)

Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. Projections based on the current generation of HPC systems and technology roadmaps suggest that very high fault rates in future systems. The errors resulting from these faults will propagate and generate various kinds of failures, which may result in outcomes ranging from result corruptions to catastrophic application crashes. Practical limits on power consumption in HPC systems will require future systems to embrace innovative architectures, increasing the levels of hardware and software complexities. The resilience challenge for extreme-scale HPC systems requires management of various hardware and software technologies that are capable of handling a broad set of fault models at accelerated fault rates. These techniques must seek to improve resilience at reasonable overheads to power consumption and performance. While the HPC community has developed various solutions, application-level as well as system-based solutions, the solution space of HPC resilience techniques remains fragmented. There are no formal methods and metrics to investigate and evaluate resilience holistically in HPC systems that consider impact scope, handling coverage, and performance & power eciency across the system stack. Additionally, few of the current approaches are portable to newer architectures and software ecosystems, which are expected to be deployed on future systems. In this document, we develop a structured approach to the management of HPC resilience based on the concept of resilience-based design patterns. A design pattern is a general repeatable solution to a commonly occurring problem. We identify the commonly occurring problems and solutions used to deal with faults, errors and failures in HPC systems. The catalog of resilience design patterns provides designers with reusable design elements. We define a design framework that enhances our understanding of the important constraints and opportunities for solutions deployed at various layers of the system stack. The framework may be used to establish mechanisms and interfaces to coordinate flexible fault management across hardware and software components. The framework also enables optimization of the cost-benefit trade-os among performance, resilience, and power consumption. The overall goal of this work is to enable a systematic methodology for the design and evaluation of resilience technologies in extreme-scale HPC systems that keep scientific applications running to a correct solution in a timely and cost-ecient manner in spite of frequent faults, errors, and failures of various types.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

A simple parcel method for prediction of cumulus onset and area-averaged cloud amount over heterogeneous land surfaces

A set of PBL data selected from the Wangara experiment is used to examine the relationship between cumulus amount and the calculated relative humidity of rising parcels. The Wilde et al. (1985) method is compared to two explicit parcel methods. It is shown that predictions using parcel methods, in which surface air is lifted to its equilibrium level while being diluted by a defined amount of mixed layer air, are superior for cloud amount, especially during periods of rapid cloud onset when the mean boundary layer top is swiftly rising through a near-neutral layer. Pending independent verification, it appears that an rms error in instantaneous cloud amount of + or - 10 percent may be achievable.

Wetzel, Peter J.↗
NASA NTRS2005

Genome-wide transcriptional analysis of flagellar regeneration in Chlamydomonas reinhardtii identifies orthologs of ciliary disease genes

The important role that cilia and flagella play in human disease creates an urgent need to identify genes involved in ciliary assembly and function. The strong and specific induction of flagellar-coding genes during flagellar regeneration in Chlamydomonas reinhardtii suggests that transcriptional profiling of such cells would reveal new flagella-related genes. We have conducted a genome-wide analysis of RNA transcript levels during flagellar regeneration in Chlamydomonas by using maskless photolithography method-produced DNA oligonucleotide microarrays with unique probe sequences for all exons of the 19,803 predicted genes. This analysis represents previously uncharacterized whole-genome transcriptional activity profiling study in this important model organism. Analysis of strongly induced genes reveals a large set of known flagellar components and also identifies a number of important disease-related proteins as being involved with cilia and flagella, including the zebrafish polycystic kidney genes Qilin, Reptin, and Pontin, as well as the testis-expressed tubby-like protein TULP2.

Polycystic Kidney Diseases/genetics↗
NASA NTRS1995

Design of High-Accuracy Multiple Flyby Trajectories Using Constrained Optimization

The trajectory optimization technique described in this paper provides several distinct advantages over previous formulations. First, fully numerically integrated trajectory modeling is used. That is, no approximations to the trajectory are made and the inclusion of any level of complicated force models desired is allowed. Second, only trajectory propagation is used so there is no requirement for optimization. This is accomplished by the novel method of splitting the trajectory into independent legs, which are then subjected to constrained optimization. Third, each of the trajectory legs may be specified by any convenient set of parameters particularly useful for that leg. Any of these parameters may then be subject to constraints. Fourth, the nonlinear optimization problem is solved by solving a sequence of linear problems which converges to the optimal nonlinear solution. Fifth, the robustness of this formulation requires little or no user interaction with the optimization once a feasible problem has been posed.

flyby↗
DOE OSTI2023

Process-based cost estimation framework for assessing economic viability of environmentally and socially sustainable rare earth element feedstocks

Rare Earth Elements (REEs) are critical raw material inputs that are required to enable the energy transition. Constraints on the U.S. supply of conventional REE feedstocks and the desire to diversify U.S. reliance on foreign supply chains have motivated the pursuit of REE recovery from unconventional feedstocks including waste products. Recovery of REEs from unconventional sources is also motivated by environmental and social considerations, including the potential to offset new mining activity and subsidize the remediation of REE-containing wastes from coal, natural gas, and other mineral extraction activities. Sourcing REEs from unconventional feedstocks would reduce supply chain constraints and minimize the environmental impacts of REE extraction, but current processes to exploit these feedstocks have uncertain technical and economic performance and the processes have not been demonstrated at scale. This work aims to reduce uncertainty in evaluating the performance of new REE production pathways. A database of operating and capital expenses for REE recovery and establish consistent process unit costs for common stages in the conventional supply chain are developed. Market prices are used to develop well-defined methods for determining product values for a diverse set of products that can be generated (e.g. oxalates, alloys, end-use products such as magnets) but are not typically considered in technoeconomic analyses. Finally, to provide insight on the effects of economies of scale and feedstock purity on capital and operating costs, process unit level cost functions relating total costs per kilogram of REE produced are reported. These tools can be applied to assess and facilitate scale-up of REE production from diverse sources of REEs.

Fritz, Alison↗
DOE OSTI2022

MAPPRAISER: A massively parallel map-making framework for multi-kilo pixel CMB experiments

Forthcoming cosmic microwave background (CMB) polarized anisotropy experiments have the potential to revolutionize our understanding of the Universe and fundamental physics. The sought-after, tale-telling signatures will be however distributed over voluminous data sets which these experiments will collect. These data sets will need to be efficiently processed and unwanted contributions due to astrophysical, environmental, and instrumental effects characterized and efficiently mitigated in order to uncover the signatures. This poses a significant challenge to data analysis methods, techniques, and software tools which will not only have to be able to cope with huge volumes of data but to do so with unprecedented precision driven by the demanding science goals posed for the new experiments. A keystone of efficient CMB data analysis is solvers of very large linear systems of equations. Such systems appear in very diverse contexts throughout CMB data analysis pipelines, however they typically display similar algebraic structures and can therefore be solved using similar numerical techniques. Linear systems arising in the so-called map-making problem are one of the most prominent and common ones. In this work we present a massively parallel, flexible and extensible framework, comprised of a numerical library, MIDAPACK, and a high level code, MAPPRAISER, which provide tools for solving efficiently such systems. Here, the framework implements iterative solvers based on conjugate gradient techniques: enlarged and preconditioned using different preconditioners. We demonstrate the framework on simulated examples reflecting basic characteristics of the forthcoming data sets issued by ground-based and satellite-borne instruments, executing it on as many as 16,384 compute cores. The software is developed as an open source project freely available to the community at: https://github.com/B3Dcmb/midapack.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Development of neural network force fields for corrosion studies

To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.

36 MATERIALS SCIENCE↗
NASA NTRS2013

Impact of Gender on Pharmocokinetics of Intranasal Scopolamine

Introduction: An intranasal gel dosage formulation of scopolamine (INSCOP) was developed for the treatment of Space Motion Sickness (SMS), which is commonly experienced by astronauts during space missions. The bioavailability and pharmacokinetics (PK) were evaluated under IND guidelines. Since information is lacking on the effect of gender on the PK of Scopolamine, we examined gender differences in PK parameters of INSCOP at three dose levels of 0.1, 0.2 and 0.4 mg. Methods: Plasma scopolamine concentrations as a function of time data were collected from twelve normal healthy human subjects (6 male/6 female) who participated in a fully randomized double blind crossover study. The PK parameters were derived using WinNonlin. Covariate analysis of PK profiles was performed using NONMEN and statistically compared using a likelihood ratio test on the difference of objective function value (OFV). Statistical significance for covariate analysis was set at P<0.05(ΔOFV=3.84). Results: No significant difference in PK parameters between male and female subjects was observed with 0.1 and 0.2 mg doses. However, CL and Vd were significantly different between male and female subjects at the 0.4 mg dose. Results from population covariate modeling analysis indicate that a onecompartment PK model with first-order elimination rate offers best fit for describing INSCOP concentration-time profiles. The inclusion of sex as a covariate enhanced the model fitting (ΔOFV=-4.1) owing to the genderdependent CL and Vd differences after the 0.4 mg dose. Conclusion: Statistical modeling of scopolamine concentration-time data suggests gender-dependent pharmacokinetics of scopolamine at the high dose level of 0.4 mg. Clearance of the parent compound was significantly faster and the volume of distribution was significantly higher in males than in females, As a result, including gender as a covariate to the pharmacokinetic model of scopolamine offers the best fit for PK modeling of the drug at dose of 0.4 mg or higher.

Putcha, L.↗
DOE OSTI2019

Physisorption of Water on Graphene: Subchemical Accuracy from Many-Body Electronic Structure Methods

According to the article, wet carbon interfaces are ubiquitous in the natural world and exhibit anomalous properties, which could be exploited by emerging technologies. However, progress is limited by lack of understanding at the molecular level. Remarkably, even for the most fundamental system (a single water molecule interacting with graphene), there is no consensus on the nature of the interaction. We tackle this by performing an extensive set of complementary state-of-the-art computer simulations on some of the world’s largest supercomputers. From this effort a consensus on the water–graphene interaction strength has been obtained. Our results have significant impact for the physical understanding, as they indicate that the interaction is weaker than predicted previously. They also pave the way for more accurate and reliable studies of liquid water at carbon interfaces.

Chemistry↗
DOE OSTI2017

A New MHD-assisted Stokes Inversion Technique

We present a new method of Stokes inversion of spectropolarimetric data and evaluate it by taking the example of a Sunrise/IMaX observation. An archive of synthetic Stokes profiles is obtained by the spectral synthesis of state-of-the-art magnetohydrodynamics (MHD) simulations and a realistic degradation to the level of the observed data. The definition of a merit function allows the archive to be searched for the synthetic Stokes profiles that best match the observed profiles. In contrast to traditional Stokes inversion codes, which solve the Unno–Rachkovsky equations for the polarized radiative transfer numerically and fit the Stokes profiles iteratively, the new technique provides the full set of atmospheric parameters. This gives us the ability to start an MHD simulation that takes the inversion result as an initial condition. After a relaxation process of half an hour solar time we obtain physically consistent MHD data sets with a target similar to the observation. The new MHD simulation is used to repeat the method in a second iteration, which further improves the match between observation and simulation, resulting in a factor of 2.2 lower mean χ{sup 2} value. One advantage of the new technique is that it provides the physical parameters on a geometrical height scale. It constitutes a first step toward inversions that give results consistent with the MHD equations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Clinical Experience and Evaluation of Patient Treatment Verification With a Transit Dosimeter

Purpose: To prospectively evaluate a protocol for transit dosimetry on a patient population undergoing intensity modulated radiation therapy (IMRT) and to assess the issues in clinical implementation of electronic portal imaging devices (EPIDs) for treatment verification. Methods and Materials: Fifty-eight patients were enrolled in the study. Amorphous silicon EPIDs were calibrated for dose and used to acquire images of delivered fields. Measured EPID dose maps were back-projected using the planning computed tomographic (CT) images to calculate dose at prespecified points within the patient and compared with treatment planning system dose offline using point dose difference and point γ analysis. The deviation of the results was used to inform future action levels. Results: Two hundred twenty-five transit images were analyzed, composed of breast, prostate, and head and neck IMRT fields. Patient measurements demonstrated the potential of the dose verification protocol to model dose well under complex conditions: 83.8% of all delivered beams achieved the initial set tolerance level of Δ{sub D} of 0 ± 5 cGy or %Δ{sub D} of 0% ± 5%. Importantly, the protocol was also sensitive to anatomic changes and spotted that 3 patients from 20 measured prostate patients had undergone anatomic change in comparison with the planning CT. Patient data suggested an EPID-reconstructed versus treatment planning system dose difference action level of 0% ± 7% for breast fields. Asymmetric action levels were more appropriate for inversed IMRT fields, using absolute dose difference (−2 ± 5 cGy) or summed field percentage dose difference (−6% ± 7%). Conclusions: The in vivo dose verification method was easy to use and simple to implement, and it could detect patient anatomic changes that impacted dose delivery. The system required no extra dose to the patient or treatment time delay and so could be used throughout the course of treatment to identify and limit systematic and random errors in dose delivery for patient groups.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Preliminary analysis of market potential for a hydropneumatic ground-level integrated diverse energy storage system

With the increasing share of renewable energy in the power grid, energy storage is considered a viable and effective solution to mitigate intermittency and improve power system stability. To quantify and estimate market potential for a recently developed Ground-Level Integrated Diverse Energy Storage (GLIDES) technology, the first operation model is developed for this hydropneumatic system and the bilinear pressure-volume terms are then linearized using the binary McCormick Relaxation method to improve solving efficiency. Based on up-to-date 5-min price data in four different real-time markets, several sets of experiments have been designed with three objective scenarios: maximize arbitrage, maximize arbitrage + reserve, maximize arbitrage + reserve + regulation. The experimental results show that the monetary value of GLIDES could be boosted by participation in ancillary markets and that regulation service might contribute more than half of the total revenue, even as high as 90% in PJM and ISONE markets. In addition, a case study for 1-day operation is used to illustrate the volume control of the GLIDES system.

25 ENERGY STORAGE↗
DOE OSTI2025

Hardware acceleration for HPS algorithms in two and three dimensions

We provide a flexible, open-source framework for hardware acceleration, namely massively-parallel execution on general-purpose graphics processing units (GPUs), applied to the hierarchical Poincaré–Steklov (HPS) family of algorithms for building fast direct solvers for linear elliptic partial differential equations. To take full advantage of the power of hardware acceleration, we propose two variants of HPS algorithms to improve performance on two- and three-dimensional problems. In the two-dimensional setting, we introduce a novel recomputation strategy that minimizes costly data transfers to and from the GPU; in three dimensions, we modify and extend the adaptive discretization technique of Geldermans and Gillman [1] to greatly reduce peak memory usage. We provide an open-source implementation of these methods written in JAX, a high-level accelerated linear algebra package, which allows for the first integration of a high-order fast direct solver with automatic differentiation tools. We conclude with extensive numerical examples showing our methods are fast and accurate on two- and three-dimensional problems.

Fast direct solvers↗
DOE OSTI2024

Tricyclononenes and tricyclononadienes as efficient monomers for controlled ROMP: understanding structure–propagation rate relationships and enabling facile post-polymerization modification

Grubbs 3rd-generation (G3) pre-catalyst-initiated ring-opening metathesis polymerization (ROMP) remains an indispensable tool in the polymer chemist's toolbox. Tricyclononenes (TCN) and tricyclononadienes (TCND) represent under-explored classes of monomers for ROMP that have the potential to both advance fundamental knowledge (e.g., structure-polymerization kinetics relationships) and serve as practical tools for the polymer chemist (e.g., post-polymerization functionalization). In this work, a library of TCN and TCND imides, monoesters, and diesters, along with their exo-norbornene counterparts, were synthesized to compare their behaviors in G3-initiated ROMP. Real-time 1 H NMR was used to study their polymerization kinetics; propagation rates ( k p ) were extracted for each monomer. To understand the relationships between monomer structure and ROMP propagation rates, density functional theory methods were used to calculate a variety of electronic and steric parameters for each monomer. While electronic parameters (e.g., HOMO energy levels) correlated positively with the measured k p values, steric parameters generally gave improved correlations, which indicates that monomer size and shape are better predictors for k p than electronic parameters for this data set. Furthermore, the TCND diester—which contains an electron-deficient cyclobutene that is resistant to ROMP—and its polymer p(TCND) are shown to be highly reactive toward DBU-catalyzed conjugate addition reactions with thiols, providing a protecting- and activating-group free strategy for post-polymerization modification.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Benchmarking third-order cluster perturbation theory for electronically excited states

In this study, we investigate the reliability of cluster perturbation (CP) theory applied to the calculation of electronically excited states through a comprehensive benchmark. In CP theory, perturbative corrections are added to the properties of a parent excitation space, which converge toward the properties of a target excitation space. For the CPS(D-n) model, perturbative corrections through order n are added to the coupled cluster singles (CCS) excitation energies to target the coupled cluster singles and doubles (CCSD) excitation energies. Through a comparative analysis of excitation energy calculations across a diverse set of molecules and wavefunction methods, we present a comprehensive evaluation of the accuracy of the third-order CPS(D) model, CPS(D-3), in calculating excitation energies. Further, our findings demonstrate that CPS(D-3) is a reliable alternative to established methods, particularly CCSD, while systematically overestimating the excitation energies compared to high-level coupled cluster methods such as CC3. These results highlight the strengths and limitations of CPS(D-3), as well as the promising directions for its future development.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗