Publications

Quantum embedding methods are powerful tools to exploit the locality of electron correlation, but thus far many wave function-in-wave function methods have focused on small (e.g., minimal) basis sets. One major challenge for extended basis sets lies in defining consistent atom- or fragment-localized orbitals in spite of the larger spatial extent of the underlying atomic orbitals. In this work, we modify a particular form of quantum embedding, bootstrap embedding (BE), to the case of extended basis sets. We find that using intrinsic atomic orbital (IAO) localization schemes alongside BE converges to ∼99.7% of the CCSD correlation energy in 3-21G, 6-311G, and cc-pVDZ basis sets for reasonably sized fragments. These results mark an important first step in extending the success of embedding methods to properly studying dynamic correlation.

Thermal atomic layer etching (ALE) was utilized to remove sputter damage from InGaP samples. Removal of sputter damage from InGaP surfaces was measured using x-ray photoelectron spectroscopy (XPS). Damage was identified by the shifted doublets in the P 2p region of the XPS spectrum. Density functional theory identified the damage as corresponding to the undercoordinated atoms in the InGaP lattice. InGaP substrates were sputtered with Ar+ ions at 500 eV or 2 keV as a model system to simulate the exposure of InGaP to energetic species during plasma processing. The InGaP thermal ALE process used sequential exposures of hydrogen fluoride for fluorination and either trimethylaluminum or dimethylaluminum chloride for ligand exchange at 300 °C. The XPS spectra revealed that InGaP thermal ALE successfully removed damage from sputtering. The area of the shifted doublets in the P 2p region was progressively reduced versus the number of ALE cycles. After ALE, the resulting XPS spectra were equivalent to the spectrum of an InGaP sample with no sputter damage. A bulklike XPS spectrum showing minimal damage was recovered after 50 ALE cycles for a sample initially exposed to 500 eV sputtering. Sputtering at 2 keV required 100 ALE cycles to largely remove the surface defects. The etch depth consistent with 100 ALE cycles indicated a damaged material depth of ∼5–6 nm. In addition, Auger electron spectroscopy (AES) revealed that the Ar AES signal from implanted Ar in InGaP after sputtering was also progressively removed versus the number of ALE cycles.

Given the growing significance of 2D materials in various optoelectronic applications, it is imperative to have simulation tools that can accurately and efficiently describe electron correlation effects in these systems. Here, we show that the recently developed bootstrap embedding (BE) accurately predicts electron correlation energies and structural properties for 2D systems. Without explicit dependence on the reciprocal space sum (k-points) in the correlation calculation, our proof-of-concept calculations shows that BE can typically recover ∼99.5% of the total minimal basis electron correlation energy in 2D semimetal, insulator, and semiconductors. We demonstrate that BE can predict lattice constants and bulk moduli for 2D systems with high precision. Furthermore, we highlight the capability of BE to treat electron correlation in twisted bilayer graphene superlattices with large unit cells containing hundreds of carbon atoms. We find that as the twist angle decreases toward the magic angle, the correlation energy initially decreases in magnitude, followed by a subsequent increase. We conclude that BE is a promising electronic structure method for future applications to 2D materials.

Modern graphics processing units (GPUs) provide an unprecedented level of computing power. In this study, we present a high-performance, multi-GPU implementation of the analytical nuclear gradient for Kohn–Sham time-dependent density functional theory (TDDFT), employing the Tamm–Dancoff approximation (TDA) and Gaussian-type atomic orbitals as basis functions. We discuss GPU-efficient algorithms for the derivatives of electron repulsion integrals and exchange–correlation functionals within the range-separated scheme. As an illustrative example, we calculate the TDA-TDDFT gradient of the S1 state of a full-scale green fluorescent protein with explicit water solvent molecules, totaling 4353 atoms, at the ωB97X/def2-SVP level of theory. Our algorithm demonstrates favorable parallel efficiencies on a high-speed distributed system equipped with 256 Nvidia A100 GPUs, achieving >70% with up to 64 GPUs and 31% with 256 GPUs, effectively leveraging the capabilities of modern high-performance computing systems.

An accurate treatment of electronic spectra in large systems with a technique such as time-dependent density functional theory is computationally challenging. Due to the Nyquist sampling theorem, direct real-time simulations must be prohibitively long to achieve suitably sharp resolution in frequency space. Super-resolution techniques such as compressed sensing and MUSIC assume only a small number of excitations contribute to the spectrum, which fails in large molecular systems where the number of excitations is typically very large. We present an approach that combines exact short-time dynamics with approximate frequency space methods to capture large narrow features embedded in a dense manifold of smaller nearby peaks. We show that our approach can accurately capture narrow features and a broad quasi-continuum of states simultaneously, even when the features overlap in frequency. Our approach is able to reduce the required simulation time to achieve reasonable accuracy by a factor of 20-40 with respect to standard Fourier analysis and shows promise for accurately predicting the whole spectrum of large molecules and materials.