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A Guided Tour of ab initio Nuclear Many-Body Theory
Over the last decade, new developments in Similarity Renormalization Group techniques and nuclear many-body methods have dramatically increased the capabilities of ab initio nuclear structure and reaction theory. Ground and excited-state properties can be computed up to the tin region, and from the proton to the presumptive neutron drip lines, providing unprecedented opportunities to confront two- plus three-nucleon interactions from chiral Effective Field Theory with experimental data. In this contribution, I will give a broad survey of the current status of nuclear many-body approaches, and I will use selected results to discuss both achievements and open issues that need to be addressed in the coming decade.
Latent Twins
Over the past decade, scientific machine learning has transformed the development of mathematical and computational frameworks for analyzing, modeling, and predicting complex systems. From inverse problems to numerical partial differential equations (PDEs), dynamical systems, and model reduction, these advances have pushed the boundaries of what can be simulated. Yet they have often progressed in parallel, with representation learning and algorithmic solution methods evolving largely as separate pipelines. With Latent Twins, we propose a unifying mathematical framework that creates a hidden surrogate in latent space for the underlying equations. Whereas digital twins mirror physical systems in the digital world, Latent Twins mirror mathematical systems in a learned latent space governed by operators. Through this lens, classical modeling, inversion, model reduction, and operator approximation all emerge as special cases of a single principle. We establish the fundamental approximation properties of Latent Twins for both ordinary differential equations (ODEs) and PDEs and demonstrate the framework across three representative settings: (i) canonical ODEs, capturing diverse dynamical regimes; (ii) a PDE benchmark using the shallow-water equations, contrasting Latent Twin simulations with deep operator network and forecasts with a four-dimensional variational method baseline; and (iii) a challenging real-data geopotential reanalysis dataset, reconstructing and forecasting from sparse, noisy observations. Latent Twins provide a compact, interpretable surrogate for solution operators that evaluate across arbitrary time gaps in a single-shot, while remaining compatible with scientific pipelines such as assimilation, control, and uncertainty quantification. Looking forward, this framework offers scalable, theory-grounded surrogates that bridge data-driven representation learning and classical scientific modeling across disciplines.
Exploring Professor Motivations and Implementations of a Real-World Problem-Solving Project
Engineering education literature offers a variety of theoretical and conceptual frameworks for project-based learning. This study explores the implementation of real-world problem-solving projects in engineering education. The research team analyzed the motivations and methods behind professors' adoption of such projects through exploratory qualitative interviews with seven professor participants who integrated a nation-wide student competition into their courses. We analyzed the resulting data using a constructivist grounded theory approach to identify key themes of professor practices. Findings reveal that the real-world aspect of the projects and alignment with values and research interests were primary motivators for implementation. While implementation methods varied significantly based on context (i.e., university setting, course type), we found that these projects could be effectively integrated into various classroom settings. The findings support the recommendation for non-academic institutions to develop and manage competitions that can be integrated into classrooms and which offer a point of engagement that is available to professors from a wide range of disciplines.
Commercial Building Sensors and Controls Systems: Barriers, Drivers, and Costs
Building sensors and controls systems, including building automation systems, consists of the sensor-based devices installed in buildings and the control and automation of those devices. Optimized sensors and controls systems could lead to 29% annual energy savings in commercial buildings and are integral to the growth of grid-interactive efficient buildings. Only 8% of small commercial buildings, however, have installed sensors and controls systems. This is largely due to cost barriers. This work seeks to increase the transparency of system costs and identify specific barriers and drivers for increased adoption. To gather industry insights, the team reached out to building owners, vendors, and contractors and conducted 20 interviews with the goal of collecting cost data and market characteristics regarding building sensors and controls. The qualitative interview data was analyzed using grounded theory to identify overarching concepts, such as barriers, drivers, and future directions on the field. From this analysis, primary barriers were found to be complexity, a lack of knowledge, and money. Primary drivers were found to be the knowledge of data, savings, and remote access. The qualitative cost data was collected in the form of invoices during the interviews. The cost values were used to develop a percentage-based cost stack which identifies the average fraction of the total cost attributed to each category (hardware, software, labor, fees, and taxes). This greater understanding of costs and barriers associated with commercial building sensors and controls systems lays the groundwork for future steps in increasing system adoption, reducing energy consumption, and market transformation.
Commercial Building Sensors and Controls Systems: Barriers and Drivers: Preprint
Building sensors and controls systems, including building automation systems, comprise the sensor-based devices installed in buildings as well as the control and automation of those devices. Optimized sensors and controls systems could lead to 29% annual energy savings in commercial buildings and are integral to the growth of grid-interactive efficient buildings. Only 13% of small commercial buildings, however, have installed sensors and controls systems, largely because of cost barriers. To accelerate adoption, this work seeks to increase the transparency of system costs and identify specific barriers and drivers. To gather industry insights, the team reached out to building owners, vendors, and contractors and conducted 21 interviews with the goal of collecting cost data and market characteristics regarding building sensors and controls. We collected the cost data in the form of invoices and used it to develop a percentage-based cost category breakdown. The interview data were analyzed using grounded theory to identify overarching concepts such as barriers, drivers, and future directions. From this analysis, we found the primary barriers to be complex and confusing systems, lack of user skills, and financial concerns, and the primary drivers to be operational benefits, insight into operations, and remote access to data. The future directions analysis highlighted the potential technological solutions to address gaps and barriers, as well as predicted drivers to increase adoption. This greater understanding of the costs, barriers, and drivers associated with commercial building sensors and controls systems lays the groundwork for increasing system adoption, reducing energy consumption, and transforming the market.
Exploring Professor Motivations and Implementations of a Real-World Problem-Solving Project: A Case Study in Preparing Students for the Emerging Building Science Industry: Preprint
Engineering education literature offers a variety of theoretical and conceptual frameworks for project-based learning. This study explores the implementation of real-world problem-solving projects in engineering education. The research team analyzed the motivations and methods behind professors' adoption of such projects through exploratory qualitative interviews with seven professor participants who integrated a nation-wide student competition into their courses. We analyzed the resulting data using a constructivist grounded theory approach to identify key themes of professor practices. Findings reveal that the real-world aspect of the projects and alignment with values and research interests were primary motivators for implementation. While implementation methods varied significantly based on context (i.e., university setting, course type), we found that these projects could be effectively integrated into various classroom settings. The findings support the recommendation for non-academic institutions to develop and manage competitions that can be integrated into classrooms and which offer a point of engagement that is available to professors from a wide range of disciplines.
The Predictive Power of Exact Constraints and Appropriate Norms in Density Functional Theory
Ground-state Kohn-Sham density functional theory provides, in principle, the exact ground-state energy and electronic spin densities of real interacting electrons in a static external potential. In practice, the exact density functional for the exchange-correlation (xc) energy must be approximated in a computationally efficient way. About 20 mathematical properties of the exact xc functional are known. In this work, we review and discuss these known constraints on the xc energy and hole. By analyzing a sequence of increasingly sophisticated density functional approximations (DFAs), we argue that (a) the satisfaction of more exact constraints and appropriate norms makes a functional more predictive over the immense space of many-electron systems and (b) fitting to bonded systems yields an interpolative DFA that may not extrapolate well to systems unlike those in the fitting set. We discuss both how the class of well-described systems has grown along with constraint satisfaction and the possibilities for future functional development.
Wavefunction theory and density functional theory analysis of ground and excited electronic states of TaB and WB
Full potential energy curves, electronic configurations, spectroscopic parameters, dipole moments, and energetics of ground and excited electronic states of TaB and WB were studied using MRCI, MRCI+Q, CCSD(T), and DFT levels of theory.
Benchmarks and results of the two-band Hubbard model from the Gutzwiller conjugate gradient minimization theory
Ground-state properties, such as energies and double occupancies, of a one-dimensional two-band Hubbard model are calculated using a first-principles Gutzwiller conjugate gradient minimization theory. The favorable agreement with the results from the density matrix renormalization group theory demonstrates the accuracy of our method. A rotationally invariant approach is further incorporated into the method to greatly reduce the computational complexity with a speedup of approximately 50 times. Moreover, we investigate the Mott transition between a metal and a Mott insulator by evaluating the charge gap. In conclusion, with greatly reduced computational effort, our method reproduces the phase diagram in reasonable agreement with the density matrix renormalization group theory.
Advanced Electronic Structure Theories for Strongly Correlated Ground and Excited States
The aim of this project was to create widely applicable, numerically robust, and systematically improvable multireference theories based on the Driven Similarity Renormalization Group (DSRG). The DSRG is a many-body formalism recently developed in our lab that maps a complex problem involving strongly and weakly interacting electrons to a simpler one in which a few electrons interact strongly via "renormalized" interactions. During this project, we focused our efforts on developing a multireference version of the DSRG (MR-DSRG) applicable to a wide range of problems in theoretical chemistry, including the computation of global ground and excited potential energy surfaces, spin-splittings in transition metal complexes and predicting the interaction of near-degenerate electronically excited states.
Interpretations of ground-state symmetry breaking and strong correlation in wavefunction and density functional theories
Significance The ground state of a quantum mechanical system is the lowest-energy eigenstate of the Hamiltonian. In isolation, it persists unchanged forever, with symmetries dictated by those of the Hamiltonian. But near-eigenstates of broken symmetry can persist for long times, even on the scale of human measurement. The appearance of broken symmetries of the electron density or spin density in a density functional calculation can reveal strong correlations among the electrons that are present in a symmetry-unbroken wavefunction. Symmetry breaking can arise when a wave-like fluctuation drops to zero frequency. The presented examples are the stretched hydrogen molecule, antiferromagnetism in solids, and the static charge-density wave in a low-density jellium, which is shown quantitatively to be a zero-frequency plasma wave.
Using ground state and excited state density functional theory to decipher 3d dopant defects in GaN
Abstract Using ground state density functional theory (DFT) and implementing an occupation-constrained DFT (occ-DFT) for self-consistent excited state calculations, we decipher the electronic structure of the Mn dopant and other 3 d defects in GaN across the band gap. Our analysis, validated with broad agreement with defect levels (ground-state calculations) and photoluminescence data (excited-state calculations), mandates reinterpretation and reassignment of 3 d defect data in GaN. The Mn Ga defect is determined to span stable charge states from (1−) in n -type GaN through (2+) in p -type GaN. The Mn(2+) is predicted to be a d 2 ground state spin triplet defect with a singlet excited state, isoelectronic with the defect associated with the 1.19 eV photoluminescence in n -type GaN. The combined analysis of defect levels and excited states invites reassessment of all d 2 -capable dopants in GaN. We demonstrate that the 1.19 eV defect, a candidate defect for optically controlled quantum applications, cannot be the Cr(1+) assumed in literature and instead must be the V(0). The combined ground-state/excited-state DFT analysis is shown to be able to chemically fingerprint defects.
Thermal coupled cluster theory for SU(2) systems
Coupled cluster (CC) has established itself as a powerful theory to study correlated quantum many-body systems. Finite-temperature generalizations of CC theory have attracted considerable interest and have been shown to work as nicely as the ground-state theory. However, most of these recent developments address only fermionic or bosonic systems. The distinct structure of the su(2) algebra requires the development of a similar thermal CC theory for spin degrees of freedom. In this paper, we provide a formulation of our thermofield-inspired thermal CC for SU(2) systems. Here, we apply the thermal CC to the Lipkin-Meshkov-Glick system as well as the one-dimensional transverse field Ising model as benchmark applications to highlight the accuracy of thermal CC in the study of finite-temperature phase diagrams in SU(2) systems.
Tensor Hypercontraction of Cluster Perturbation Theory: Quartic Scaling Perturbation Series for the Coupled Cluster Singles and Doubles Ground-State Energies
Even though Cluster Perturbation has been shown to be a robust non-iterative alternative to Coupled Cluster Theory, it is still plagued by high order polynomial computational scaling and the storage of higher order tensors. Here we present a proof-of-concept strategy for implementing Cluster Perturbation Theory ground state energy series for the coupled cluster singles and doubles energy with N 4 computational scaling using Tensor Hypercontraction (THC). The reduction in computational scaling by two orders is achieved by decomposing two electron repulsion integrals, doubles amplitudes and multipliers, as well as selected doubles intermediates to Tensor Hypercontraction format. Using the outlined strategy, we showcase that the Tensor Hypercontraction pilot implementations retain numerical accuracy to within 1 kcal/mol relative to corresponding conventional and density fitting implementations and we empirically verify the N 4 scaling.
Ground electronic state description of thiourea coordination in homoleptic Zn 2+ , Ni 2+ and Co 2+ complexes using sulfur K -edge X-ray absorption spectroscopy
Sulfur K-edge X-ray absorption spectroscopy (XAS) was employed to experimentally characterize the coordinative bond between the thiourea (TU) or thiocarbamide ligand and transition metal (TM) ions Zn 2+ , Co 2+ and Ni 2+ in distorted tetrahedral and octahedral homoleptic coordination environments. Comparisons of XAS spectra of the free TU ligand and [Zn(TU) 4 ] 2+ , [Co(TU) 4 ] 2+ and [Ni(TU) 6 ] 2+ complexes clearly identify spectral features unique to TM 2+ –S(TU) bonding. Quantitative analysis of pre-edge intensities describes the covalency of Ni 2+ —S(TU) and Co 2+ —S(TU) bonding to be at most 21% and 9% as expressed by the S 3p contributions per TM 3d electron hole. Using relevant Ni 2+ complexes with dithiocarbamate and thioether ligands, we evaluated the empirical S 1s → 3p transition dipole integrals developed for S-donor ligands and their dependence on heteroatom substitutions. With the aid of density functional theory-based ground electronic state calculations, we found evidence for the need of using a transition dipole that is dependent on the presence of conjugated heteroatom (N) substitution in these S-donor ligands.
Assessing cathode property prediction via exchange-correlation functionals with and without long-range dispersion corrections
In this work, we benchmark calculated interlayer spacings, average topotactic voltages, thermodynamic stabilities, and band gaps in layered lithium transition-metal oxides (TMOs) and their de-lithiated counterparts, which are used in lithium-ion batteries as positive electrode materials, against available experimental data. Specifically, we examine the accuracy of properties calculated within density functional theory (DFT) using eight different treatments of electron exchange-correlation: the strongly constrained and appropriately normed (SCAN) and Perdew–Burke–Ernzerhof (PBE) density functionals, Hubbard-U-corrected SCAN and PBE (i.e., SCAN+U and PBE+U), and SCAN(+U) and PBE(+U) with added long-range dispersion (D) interactions (i.e., DFT(+U)+D). van der Waals interactions are included respectively via the revised Vydrov-Van Voorhis (rVV10) for SCAN(+U) and the DFT-D3 for PBE(+U). We find that SCAN-based functionals predict larger voltages due to an underestimation of stability of the MO2 systems, while also predicting smaller interlayer spacings compared to their PBE-based counterparts. Furthermore, adding dispersion corrections to PBE has a greater effect on voltage predictions and interlayer spacings than with SCAN, indicating that DFT-SCAN – despite being a ground-state theory – fortuitously captures some short and medium-range dispersion interactions better than PBE. While SCAN-based and PBE-based functionals yield qualitatively similar band gap predictions, there is no significant quantitative improvement of SCAN-based functionals over the corresponding PBE-based versions. Finally, we expect SCAN-based functionals to yield more accurate property predictions than the respective PBE-based functionals for most TMOs, given SCAN's stronger theoretical underpinning and better predictions of systematic trends in interlayer spacings, intercalation voltages, and band gaps obtained in this work.
Disentangling natural and anthropogenic effects on benthic macroinvertebrate assemblages in western US streams
Abstract Stream macroinvertebrate assemblages are shaped by natural and human‐related factors that operate through complex hierarchical pathways. Quantifying these relationships can provide additional insights into stream ecological assessment. We applied a structural equation modeling framework to evaluate hypothesized pathways by which watershed, riparian, and in‐stream factors affect benthic macroinvertebrate condition in the Western Mountains (WMT) and Xeric (XER) ecoregions in the United States. We developed a conceptual model grounded in theory, empirical evidence, and expert opinion to evaluate the following hypotheses: (1) macroinvertebrate assemblages are primarily driven by proximal, in‐stream factors (e.g., water quality and physical habitat); (2) anthropogenic land uses affect macroinvertebrates indirectly by altering in‐stream characteristics; and (3) riparian vegetation cover attenuates land use effects. We tested our model separately on three measures of benthic macroinvertebrate assemblage condition: ratio of observed‐to‐expected taxonomic richness (O/E); a multimetric index (MMI); and richness of Ephemeroptera, Plecoptera, and Trichoptera taxa (EPT). In the WMT, site‐level riparian cover, in‐stream physical habitat (relative bed stability), and water chemistry (total nitrogen) were the top three predictors of macroinvertebrate assemblages, each having over two times the magnitude of effect on macroinvertebrates compared with watershed‐level predictors. In the arid XER, annual precipitation and stream flow characteristics were top predictors of macroinvertebrate assemblages and had similar magnitudes of effect as in‐stream water chemistry. Path analyses revealed that land use activities in the watershed and at the stream site degraded macroinvertebrate assemblages indirectly by altering relative bed stability, water quality, and riparian cover/complexity. Increased riparian cover was associated with greater macroinvertebrate condition by reducing land use impacts on stream flow, streambed substrate, and water quality, but the pathways differed among ecoregions. In the WMT, site‐level riparian cover affected macroinvertebrate assemblages partly through indirect pathways associated with greater streambed stability and reduced total nitrogen concentrations. In contrast, in the XER, watershed‐level riparian cover affected macroinvertebrate assemblages through greater specific stream power. Identifying the relative effects of and pathways by which natural and anthropogenic factors affect macroinvertebrates can serve as a framework for prioritizing management and conservation efforts.