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.
Kayahan Saritas, Hyeon-Jong Shin, Jaron T. Krogel et al.· 0 citations
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$\sigma$. 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.
Aqsa Shaikh, L. Mitas, P. Ganesh et al.· 0 citations
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.
Kenneth O Berard, Brenda M. Rubenstein, Jaron T. Krogel· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
