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Density functional theory (DFT) provides convenient electronic structure methods for the study of molecular systems and materials. Regular Kohn-Sham DFT calculations rely on unitary transformations to determine the ground-state electronic…
The density relaxation phenomenon is modeled using both Monte Carlo and dissipative MD simulations to investigate the effects of regular taps applied to a vessel having a planar floor filled with monodisperse spheres. Results suggest the…
The interaction between excited states of a closed-shell chromophore and a nearby free radical species gives rise to spin-coupled doublet states, namely singdoublet and tripdoublet, as well as a quartet state. This coupling facilitates…
Many chemical systems cannot be described by quantum chemistry methods based on a singlereference wave function. Accurate predictions of energetic and spectroscopic properties require a delicate balance between describing the most important…
Neural Network Potentials (NNPs) have attracted significant attention as a method for accelerating density functional theory (DFT) calculations. However, conventional NNP models typically do not incorporate spin degrees of freedom, limiting…
We present a theoretical model to investigate the dynamics and spectroscopic properties of a plexciton system consisting of a molecular exciton coupled to a single short-lived plasmonic mode. The exciton is described as a two-level system…
Simulating liquid water to an accuracy that matches its wealth of available experimental data requires both precise electronic structure methods and reliable sampling of nuclear (quantum) motion. This is challenging because applying the…
In $e$-beam evaporated amorphous silicon ($a$-Si), the densities of two-level systems (TLS), $n_{0}$ and $\overline{P}$, determined from specific heat $C$ and internal friction $Q^{-1}$ measurements, respectively, have been shown to vary by…
We present an active learning framework for efficiently generating training data for machine-learned interatomic potentials (MLIPs). The method combines local entropy-driven molecular dynamics with global dataset-aware filtering: a…
We investigate theoretically and numerically the light-matter interaction in a two-level system (TLS) as a model system for excitation in a solid-state band structure. We identify five clearly distinct excitation regimes, categorized with…
At sub-Kelvin temperatures, two-level systems (TLS) present in amorphous dielectrics source a permittivity noise, degrading the performance of a wide range of devices using superconductive resonators such as qubits or kinetic inductance…
We present a density-functional theory (DFT) approach to the study of the phase diagram of the maximum density droplet (MDD) in two-dimensional quantum dots in a magnetic field. Within the lowest Landau level (LLL) approximation, analytical…
We present an application of our new theoretical formulation of quantum dynamics, moment propagation theory (MPT) (Boyer et al., J. Chem. Phys. 160, 064113 (2024)), for employing machine-learning techniques to simulate the quantum dynamics…
Understanding the properties of warm dense hydrogen is of key importance for the modeling of compact astrophysical objects and to understand and further optimize inertial confinement fusion (ICF) applications. The work horse of warm dense…
The density matrix renormalization group is one of the most powerful numerical methods for computing ground-state properties of two-dimensional (2D) quantum lattice systems. Here we show its finite-temperature extensions are also viable for…
This chapter provides a basic introduction to excited-state extensions of density functional theory (DFT), including time-dependent (TD-)DFT in both its linear-response and its explicitly time-dependent formulations. As applied to the…
The swift progression of machine learning (ML) has not gone unnoticed in the realm of statistical mechanics. ML techniques have attracted attention by the classical density-functional theory (DFT) community, as they enable discovery of…
Material disorders are one of the major sources of noise and loss in solid-state quantum devices, whose behaviors are often modeled as two-level systems (TLSs) formed by charge tunneling between neighboring sites. However, the role of their…
We present a numerical modeling workflow based on machine learning (ML) which reproduces the the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible…
Understanding how structural flexibility affects the properties of metal-organic frameworks (MOFs) is crucial for the design of better MOFs for targeted applications. Flexible MOFs can be studied with molecular dynamics simulations, whose…