arXiv subjects

Jaron T. Krogel

Publications and source records attributed to Jaron T. Krogel.

At least 19 recordsLinked to original sources

arXiv2026

Toward Predictive Hydride Bond Energetics with Neural-Network Wavefunctions

Accurate prediction of transition-metal hydride (TM-H) bond dissociation energies (BDEs) remains challenging because of strong electron correlation, relativistic effects, and nuclear quantum contributions. Here, we assess the performance of neural-network variational Monte Carlo (NN-VMC) based on the Psiformer ansatz for systems with increasing complexity (LiH, OH, TiH and NiH), and compare it against CCSDT(Q)/CBS as well as available theoretical and experimental data. Throughout this study, both NN-VMC and ab initio calculations employ a common ccECP Hamiltonian to enable tractable and consistent comparisons. To reduce finite-training errors, we introduce complementary zero-variance and infinite-step extrapolation schemes. For LiH and OH systems, NN-VMC yields total energies that differ within sub-milli Hartree compared to CBS extrapolated ab initio results and the BDE differences remain under 2$σ$. In case of Ti and TiH, the variational NN-VMC energies at the end of training are already consistent with CCSD(T)/CBS, while post-training extrapolation systematically closes the gap towards the CCSDT(Q)/CBS results. In contrast, NiH provides a stringent test of wavefunction expressivity, where increasing the number of determinants in NN-wavefunction ansatz improves the recovered correlation energy. The comparison of NiH BDE reveals that, while the existing theoretical predictions cluster into distinct high and low BDE groups, the broad scatter in available experimental data prevents a definitive assessment of the most accurate theoretical approach. This work demonstrates that NN-VMC with ccECPs Hamiltonian provides a competitive framework for quantitative prediction of main-group and early TM-H energetics, while identifying late TM-H as an important benchmark for future developments in neural-network wavefunctions and electronic structure theory.

physics.chem-ph↗

arXiv2026

Diffusion Quantum Monte Carlo Benchmark of Interlayer Binding and Charge Redistribution in Chemically Distinct Two-Dimensional Van Der Waals Bilayers

Interlayer interactions in two-dimensional materials can generate emergent phenomena absent in their constituent monolayers, including unconventional magnetic order, multiferroicity, and topological magnetic phases. Predicting such emergent behavior requires simultaneously resolving long-range dispersion, short-range orbital hybridization, and electronic correlation interactions that are intrinsically nonlocal and many-body and remain challenging even for advanced density-functional approximations. Here we establish a systematically controlled many-body benchmark for diverse bilayer materials using diffusion Monte Carlo (DMC), spanning single-sheet materials, transition-metal dichalcogenides, and magnetic transition-metal halides. We obtain equilibrium separations, binding energetics, interlayer vibrational modes, and charge redistribution, finding excellent agreement with available experiments while revealing systematic and property-dependent failures across widely used semilocal, meta-GGA, and dispersion-corrected density functionals. Beyond energetics, DMC resolves subtle interlayer charge rearrangements, particularly leading to long-ranged dipolar tails in magnetic Cr trihalides that survive well beyond the regime where semilocal DFT predicts appreciable interlayer polarization, providing a many-body basis for understanding interlayer-coupled ferroic and magnetic phenomena. The resulting energies, response properties, and high-accuracy electron densities constitute a transferable benchmark for developing next-generation density functionals. Finally, we provide a scalable high-performance-computing workflow that enables systematic expansion of many-body benchmark datasets across emerging families of layered quantum materials by the scientific community.

cond-mat.mtrl-sci↗

arXiv2026

Denoising Diffusion Monte Carlo Electron Densities with Physically Informed Variance Stabilization: From Fourier Filters to 3D UNETs

Obtaining accurate electron densities is important for the fundamental description of molecular and condensed matter systems, as well as for the development of next-generation density functionals. Diffusion Monte Carlo (DMC), in particular, is known to produce benchmark-quality data; however, the predicted real-space electron densities contain substantial amounts of statistical noise. In this work, we study denoising approaches for DMC densities, judged on the basis of the information-theoretic Jensen-Shannon divergence. The denoising is facilitated by an approximate heteroscedastic to homoscedastic transformation leveraging the density functional theory density as a physical prior. We systematically compare a range of denoising techniques-including Fourier transform, regression, and 3D UNETs-on materials showing a wide range of density variations: carbon diamond, blue phosphorus, and rutile VO2. Our results indicate that simple flattened machine learning models and 2D image-based models introduce line artifacts and struggle to capture the full spatial correlation. In contrast, when using variance stabilization, regression methods outperform all others in both the high and low- noise limits across all materials considered. The best denoisers reduce the required cost of density-generating DMC simulations by 10-100x, providing a promising route forward for application in noise-sensitive tasks such as DFT functional inversion.

cond-mat.mtrl-sci↗

arXiv2026

Thickness-Dependent Interlayer Coupling and Semiconductor-to-Semimetal Crossover in Arsenene Multilayers

Interlayer interactions in layered materials are often assumed to transfer from the bilayer to the bulk, but this assumption can fail when chemically active out-of-plane orbitals participate in bonding. We combine diffusion quantum Monte Carlo (DMC) and density functional theory (DFT) to determine how interlayer coupling evolves in arsenene multilayers. DMC shows that bulk gray arsenic is compact, whereas the corresponding few-layer structures remain at substantially larger interlayer separations despite sharing the same nominal A$_{1}$B$_{-1}$ adjacent-layer registry. Registry alone therefore does not determine the bonding regime; thickness and coordination reshape the interlayer interaction. Among the tested functionals, SCAN+rVV10 most closely reproduces DMC equilibrium separations and stacking energetics. Using the DMC-benchmarked SCAN+rVV10 calculations, we predict a thickness-driven stacking sequence from A$_{1}$A$_{1}$ to A$_{1}$B$_{1}$ and finally bulk-like A$_{1}$B$_{-1}$. The structural crossover coincides with a stacking-dependent DFT band-gap collapse driven by enhanced interlayer As p$_{z}$ hybridization.

cond-mat.mtrl-sci↗

arXiv2026

Nonmagnetic Ground State of Rutile RuO$_2$ from Diffusion Quantum Monte Carlo

Rutile RuO$_2$ has been proposed as an altermagnet, but its bulk magnetic ground state is still under debate because density-functional calculations give conflicting predictions. Using fixed-node diffusion quantum Monte Carlo, we find that stoichiometric bulk RuO$_2$ is nonmagnetic in the pristine structure, lying 23(9) meV per formula unit below the lowest antiferromagnetic state considered. A 3$\%$ compressive strain instead stabilizes antiferromagnetism, placing RuO$_2$ near a strain-tunable magnetic instability and helping reconcile apparently conflicting experimental reports.

cond-mat.mtrl-sci↗

arXiv2026

Optimizing Density Functional Theory for Strain-Dependent Magnetic Properties of Monolayer MnBi$_2$Te$_4$ with Diffusion Monte Carlo

Monolayer MnBi$_{2}$Te$_{4}$ (MBT) is an intrinsically magnetic topological insulator whose magnetic response is strongly affected by strain and electron correlation. In density functional theory with an on-site Hubbard correction (DFT+$U$), however, predictions vary substantially with the choice of Hubbard $U$, making it difficult to establish a reliable strain-dependent picture of magnetism in this system. Here we use diffusion Monte Carlo (DMC) to benchmark DFT+$U$ for monolayer MBT and to determine an effective $U$ as a function of strain. We find that the predicted magnetic phase diagram depends strongly on $U$, indicating that a single fixed value is not sufficient across the strain range considered. DMC nodal optimization further shows that the optimal $U$ increases with strain magnitude and is well captured by a simple quadratic form. When this DMC-informed strain-dependent $U$ is used in PBE+$U$, the calculated Mn local moments are brought into close agreement with DMC and are improved relative to commonly used fixed-$U$ choices. These results show that, for monolayer MBT, correlation strength itself should be treated as strain dependent, and they provide a practical many-body-guided strategy for improving strain-dependent DFT+$U$ descriptions of magnetic van der Waals materials.

cond-mat.mtrl-sci↗

arXiv2026

Assessing Orbital Optimization in Variational and Diffusion Monte Carlo

In this work, we investigate the fidelity of orbital optimization in variational Monte Carlo to improve diffusion Monte Carlo results on correlated magnetic systems, using CrSBr as a model system. We compare the performance of different optimization methods, showing that stochastic reconfiguration is a robust and reliable optimizer. We show that short range Jastrow factors are important for improving diffusion Monte Carlo, regardless of the quality of orbitals. Large active spaces are required to converge the variational energy, but ulitmately orbital optimization produces worse diffusion Monte Carlo energies when compared to standard orbitals from density functional theory. We show that this increased bias is due to larger locality errors from the use of pseudopotentials, while the fixed-node error is actually improved by using orbital optimization. Additionally, for observables other than energy, orbital optimization produces a systematically smaller mixed-estimator bias. Ultimately, we believe orbital optimization provides a reliable method to improve variational and pure fixed-node energies as well as lower mixed-estimator bias.

cond-mat.str-el↗

arXiv2025

Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water-methane dimer

Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely-trusted many-body method for solving the Schrödinger equation, known for its reliable predictions of material and molecular properties. Furthermore, its excellent scalability with system complexity and near-perfect utilization of computational power makes FN-DMC ideally positioned to leverage new advances in computing to address increasingly complex scientific problems. Even though the method is widely used as a computational gold standard, reproducibility across the numerous FN-DMC code implementations has yet to be demonstrated. This difficulty stems from the diverse array of DMC algorithms and trial wave functions, compounded by the method's inherent stochastic nature. This study represents a community-wide effort to assess the reproducibility of the method, affirming that: Yes, FN-DMC is reproducible (when handled with care). Using the water-methane dimer as the canonical test case, we compare results from eleven different FN-DMC codes and show that the approximations to treat the non-locality of pseudopotentials are the primary source of the discrepancies between them. In particular, we demonstrate that, for the same choice of determinantal component in the trial wave function, reliable and reproducible predictions can be achieved by employing the T-move (TM), the determinant locality approximation (DLA), or the determinant T-move (DTM) schemes, while the older locality approximation (LA) leads to considerable variability in results. These findings demonstrate that, with appropriate choices of algorithmic details, fixed-node DMC is reproducible across diverse community codes-highlighting the maturity and robustness of the method as a tool for open and reliable computational science.

physics.comp-ph↗

arXiv2025

Toward improved property prediction of 2D materials using many-body quantum Monte Carlo methods

The field of two-dimensional (2D) materials has grown dramatically in the last two decades. 2D materials can be utilized for a variety of next-generation optoelectronic, spintronic, clean energy, and quantum computation applications. These 2D structures, which are often exfoliated from layered van der Waals (vdW) materials, possess highly inhomogeneous electron densities and can possess short- and long-range electron correlations. The complexities of 2D materials make them challenging to study with standard mean-field electronic structure methods such as density functional theory (DFT), which relies on approximations for the unknown exchange-correlation functional. In order to overcome the limitations of DFT, highly accurate many-body electronic structure approaches such as Diffusion Monte Carlo (DMC) can be utilized. In the past decade, DMC has been used to calculate accurate magnetic, electronic, excitonic, and topological properties in addition to accurately capturing interlayer interactions and cohesion and adsorption energetics of 2D materials. This approach has been applied to 2D systems of wide interest including graphene, phosphorene, MoS$_2$, CrI$_3$, VSe$_2$, GaSe, GeSe, borophene, and several others. In this review article, we highlight some successful recent applications of DMC to 2D systems for improved property predictions beyond standard DFT.

cond-mat.mtrl-sci↗

arXiv2025

Pushing the Accuracy Limit of Foundation Neural Network Models with Quantum Monte Carlo Forces and Path Integrals

We propose an end-to-end integrated strategy to produce highly accurate quantum chemistry (QC) synthetic datasets (energies and forces) aimed at deriving Foundation Machine Learning models for molecular simulation. Starting from Density Functional Theory (DFT), a "Jacob's Ladder" approach leverages computationally-optimized layers of massively GPU-accelerated software with increasing accuracy. Thanks to Exascale, this is the first time that the computationally intensive calculation of Quantum Monte Carlo forces (QMC), and the combination of multi-determinant QMC energies and forces with selected-Configuration Interaction wavefunctions, are computed at such scale at the complete basis-set limit. To bridge the gap between accurate QC and condensed-phase Molecular Dynamics, we leverage transfer learning to improve the DFT-based FeNNix-Bio1 foundation model. The resulting approach is coupled to path integrals adaptive sampling quantum dynamics to perform nanosecond reactive simulations at unprecedented accuracy. These results demonstrate the promise of Exascale to deepen our understanding of the inner machinery of complex biosystems.

physics.chem-ph↗

arXiv2025

Quantum Monte Carlo and density functional theory study of strain and magnetism in 2D 1T-VSe$_2$ with charge density wave states

Two-dimensional (2D) 1T-VSe$_2$ has prompted significant interest due to the discrepancies regarding alleged ferromagnetism (FM) at room temperature, charge density wave (CDW) states and the interplay between the two. We employed a combined Diffusion Monte Carlo (DMC) and density functional theory (DFT) approach to accurately investigate the magnetic properties, CDW states, and their response to strain in monolayer 1T-VSe$_2$. Our calculations show the delicate competition between various phases, revealing critical insights into the relationship between their energetic and structural properties. We performed classical Monte Carlo simulations informed by our DMC and DFT results, and found the magnetic transition temperature ($T_c$) of the undistorted (non-CDW) FM phase to be 228 K and the distorted (CDW) phase to be 68 K. Additionally, we studied the response of biaxial strain on the energetic stability and magnetic properties of various phases of 2D 1T-VSe$_2$ and found that small amounts of strain can increase the $T_c$, suggesting a promising route for engineering and enhancing magnetic behavior. Finally, we synthesized 1T-VSe$_2$ and performed Raman spectroscopy measurements, which were in close agreement with our calculated results, validating our computational approach. Our work emphasizes the role of highly accurate DMC methods in advancing the understanding of monolayer 1T-VSe$_2$ and provides a robust framework for future studies of 2D magnetic materials.

cond-mat.mtrl-sci↗

arXiv2024

Identifying Band Inversions in Topological Materials Using Diffusion Monte Carlo

Topological insulators are characterized by insulating bulk states and robust metallic surface states. Band inversion is a hallmark of topological insulators: at time-reversal invariant points in the Brillouin zone, spin-orbit coupling (SOC) induces a swapping of orbital character at the bulk band edges. In this work, we develop a novel method to detect band inversion within continuum quantum Monte Carlo (QMC) methods that can accurately treat the electron correlation and spin-orbit coupling crucial to the physics of topological insulators. Our approach applies a momentum-space-resolved atomic population analysis throughout the first Brillouin zone utilizing the Löwdin method and the one-body reduced density matrix produced with Diffusion Monte Carlo (DMC). We integrate this method into QMCPACK, an open source ab initio QMC package, so that these ground state methods can be used to complement experimental studies and validate prior DFT work on predicting the band structures of correlated topological insulators. We demonstrate this new technique on the topological insulator bismuth telluride, which displays band inversion between its Bi-p and Te-p states at the $Γ$-point. We show an increase in charge on the bismuth p orbital and a decrease in charge on the tellurium p orbital when comparing band structures with and without SOC. Additionally, we use our method to compare the degree of band inversion present in monolayer Bi$_2$Te$_3$, which has no interlayer van der Waals interactions, to that seen in the bulk. The method presented here will enable future, many-body studies of band inversion that can shed light on the delicate interplay between correlation and topology in correlated topological materials.

cond-mat.str-el↗

arXiv2024

Stacking Polymorphism of PtSe$_{2}$: Its Implication to Layer-dependent Metal-insulator Transitions

Using diffusion Monte Carlo (DMC) and density functional theory (DFT) calculations, we examined the structural stability and interlayer binding properties of PtSe$_2$, a representative transition metal dichalcogenide (TMD) with strong interlayer interaction. Our DMC results for the bilayer revealed that AA and AB-r stacking modes are nearly degenerate, highlighting the significant role of interlayer hybridization in offsetting the energy cost due to larger interlayer separations in the AB-r mode. Additionally, our DMC-benchmarked DFT studies with the r$^2$SCAN+rVV10 functional demonstrated pronounced stacking polymorphism in few-layer PtSe$_2$, suggesting the potential for stacking faults and the formation of grain boundaries between different stacking domains which could develop metallic electronic structures. Thus this polymorphism, along with selenium vacancies, influences a layer-dependent metal-insulator transition observed in few-layer PtSe$_2$. Our findings emphasize the importance of both van der Waals interactions and interlayer hybridization in determining the phase stability and electronic properties of TMDs, advancing our understanding of their fundamental properties and refining theoretical models for practical applications in nanoelectronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Optimizing Density Functional Theory for Strain-Dependent Magnetic Properties of MnBi$_2$Te$_4$ with Diffusion Monte Carlo

In this study, we evaluate the predictive power of density functional theory (DFT) for the magnetic properties of MnBi$_2$Te$_4$ (MBT), an intrinsically magnetic topological insulator with potential applications in spintronics and quantum computing. Our theoretical understanding of MBT has been challenged by discrepancies between experimental results and \textit{ab initio} calculations, particularly with respect to its electronic and magnetic properties. Our results show that the magnetic phase diagram of MBT varies significantly depending on the Hubbard $U$ parameter in the DFT framework, highlighting the importance of benchmark calculations. To address these challenges, we establish an optimized Hubbard $U$ approach derived from Diffusion Monte Carlo (DMC) calculations, which directly solves the many-body Schrödinger equation based on the stochastic process, and implement it in the DFT framework. Once the optimized $U$ value is determined as a function of strain, we apply it to achieve DMC-level accuracy within our DFT framework. This approach is instrumental in accurately describing the magnetic states of MBT and understanding the underlying mechanisms governing its magnetic properties and their dependence on external factors.

cond-mat.mtrl-sci↗

arXiv2024

Exploring interlayer coupling in the twisted bilayer PtTe$_{2}$

We have investigated interlayer interactions in the bilayer PtTe$_{2}$ system, which influence the electronic energy bands near the Fermi levels. Our diffusion Monte Carlo (DMC) calculations for the high-symmetry bilayer stackings (AA, AB, AC) manifest distinct interlayer binding characteristics among the stacking modes by revealing significantly different interlayer separations depending on the stackings, which is critical to understanding the interlayer coupling of the twisted bilayers consisting of various local stacking arrangements. Furthermore, a comparison between the interlayer separations obtained from DMC and density functional theory (DFT) shows that meta-GGA-based vdW-DFT results agree with DMC for different layer stackings, including twisted bilayers, but only the ground-state AA stacking matches well with GGA-based DFT predictions. This underscores the importance of accurate exchange-correlation potentials even for capturing the stacking-dependent interlayer binding properties. We further show that the variability in DFT-predicted interlayer separations is responsible for the large discrepancy of band structures in the 21.79$^{\circ}$-twisted bilayer PtTe$_{2}$, affecting its classification as metallic or semiconducting. These results demonstrate the importance of obtaining a correct description of stacking-dependent interlayer coupling in modeling delicate bilayer systems at finite twists.

cond-mat.mtrl-sci↗

arXiv2024

Surrogate optimization of variational quantum circuits

Variational quantum eigensolvers are touted as a near-term algorithm capable of impacting many applications. However, the potential has not yet been realized, with few claims of quantum advantage and high resource estimates, especially due to the need for optimization in the presence of noise. Finding algorithms and methods to improve convergence is important to accelerate the capabilities of near-term hardware for VQE or more broad applications of hybrid methods in which optimization is required. To this goal, we look to use modern approaches developed in circuit simulations and stochastic classical optimization, which can be combined to form a surrogate optimization approach to quantum circuits. Using an approximate (classical CPU/GPU) state vector simulator as a surrogate model, we efficiently calculate an approximate Hessian, passed as an input for a quantum processing unit or exact circuit simulator. This method will lend itself well to parallelization across quantum processing units. We demonstrate the capabilities of such an approach with and without sampling noise and a proof-of-principle demonstration on a quantum processing unit utilizing 40 qubits.

quant-ph↗

arXiv2024

JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods

Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with both perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard

cond-mat.mtrl-sci↗

arXiv2024

Enhanced Twist-Averaging Technique for Magnetic Metals: Applications using Quantum Monte Carlo

We propose an improved twist-averaging scheme for quantum Monte Carlo methods that use converged Kohn-Sham or Hartree-Fock orbitals as the reference. This twist-averaging technique is tailored to sample the Brillouin zone of magnetic metals, although it naturally extends to nonmagnetic conducting systems. The proposed scheme aims to reproduce the reference magnetization and achieves charge neutrality by construction, thus avoiding the large energy fluctuations and the postprocessing needed to correct the energies. It shows the most robust convergence of total energy and magnetism to the thermodynamic limit when compared to four other twist-averaging schemes. Diffusion Monte Carlo applications are shown on nonmagnetic Al and ferromagnetic $α$-Fe. The cohesive energy of Al in the thermodynamic limit shows an excellent agreement with the experimental result. Furthermore, the magnetic moments in $α$-Fe exhibit rapid convergence with an increasing number of twists.

physics.comp-ph↗