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  1. Free, publicly-accessible full text available January 1, 2024
  2. Free, publicly-accessible full text available December 1, 2023
  3. Routinely assessing the stability of molecular crystals with high accuracy remains an open challenge in the computational sciences. The many-body expansion decomposes computation of the crystal lattice energy into an embarrassingly parallel collection of computations over molecular dimers, trimers, and so forth, making quantum chemistry techniques tractable for many crystals of small organic molecules. By examining the range-dependence of different types of energetic contributions to the crystal lattice energy, we can glean qualitative understanding of solid-state intermolecular interactions as well as practical, exploitable reductions in the number of computations required for accurate energies. Here, we assess the range-dependent character of two-body interactions of 24 small organic molecular crystals by using the physically interpretable components from symmetry-adapted perturbation theory (electrostatics, exchange-repulsion, induction/polarization, and London dispersion). We also examine correlations between the convergence rates of electrostatics and London dispersion terms with molecular dipole moments and polarizabilities, to provide guidance for estimating convergence rates in other molecular crystals.
    Free, publicly-accessible full text available August 28, 2023
  4. Free, publicly-accessible full text available October 1, 2023
  5. The rapid development and application of machine learning (ML) techniques in materials science have led to new tools for machine-enabled and autonomous/high-throughput materials design and discovery. Alongside, efforts to extract data from traditional experiments in the published literature with natural language processing (NLP) algorithms provide opportunities to develop tremendous data troves for these in silico design and discovery endeavors. While NLP is used in all aspects of society, its application in materials science is still in the very early stages. This perspective provides a case study on the application of NLP to extract information related to the preparation of organic materials. We present the case study at a basic level with the aim to discuss these technologies and processes with researchers from diverse scientific backgrounds. We also discuss the challenges faced in the case study and provide an assessment to improve the accuracy of NLP techniques for materials science with the aid of community contributions.
  6. We analyze the rotational dynamics of six magnetic dipoles of identical strength at the vertices of a regular hexagon with a variable-strength dipole in the center. The seven dipoles spin freely about fixed axes that are perpendicular to the plane of the hexagon, with their dipole moments directed parallel to the plane. Equilibrium dipole orientations are calculated as a function of the relative strength of the central dipole. Small-amplitude perturbations about these equilibrium states are calculated in the absence of friction and are compared with analytical results in the limit of zero and infinite central dipole strength. Normal modes and frequencies are presented. Bifurcations are seen at two critical values of the central dipole strength, with bistability between these values.
  7. Recent atom interferometry (AI) experiments involving Bose–Einstein condensates (BECs) have been conducted under extreme conditions of volume and interrogation time. Numerical solution of the rotating-frame Gross–Pitaevskii equation (RFGPE), which is the standard mean-field theory applied to these experiments, is impractical due to the excessive computation time and memory required. We present a variational model that provides approximate solutions of the RFGPE for a power-law potential on a practical time scale. This model is well-suited to the design and analysis of AI experiments involving BECs that are split and later recombined to form an interference pattern. We derive the equations of motion of the variational parameters for this model and illustrate how the model can be applied to the sequence of steps in a recent AI experiment where BECs were used to implement a dual-Sagnac atom interferometer rotation sensor. We use this model to investigate the impact of finite-size and interaction effects on the single-Sagnac-interferometer phase shift.