Semi-Empirical Haken-Strobl Model for Molecular Spin Qubits

Understanding the physical processes that determine the relaxation $T_{1}$ and dephasing $T_2$ times of molecular spin qubits is critical for envisioned applications in quantum metrology and information processing. Recent spin-echo $T_1$ measurements of solid-state molecular spin qubits have stimulated the development of quantum mechanical models for predicting intrinsic spin qubit timescales using first-principles electronic structure methods. We develop an alternative semi-empirical approach to construct Redfield quantum master equations for molecular spin qubits using a stochastic Haken-Strobl model for a central spin with a fluctuating gyromagnetic tensor due to spin-lattice interaction and a fluctuating local magnetic field due to interactions with other lattice spins. Using a vanadium-based spin qubit as a case study, we compute qubit population and decoherence timescales as a function of temperature and magnetic field using a bath spectral density parametrized with a small number of $T_{1}$ measurements. The theory quantitatively agrees with experimental data over a range of conditions beyond those used to parametrize the model, demonstrating the generalization potential of the method. The ability of the model to describe the temperature dependence of the ratio $T_2/T_1$ is discussed and possible applications for designing novel molecule-based quantum magnetometers are suggested.Comment: 7 pages, 5 figure

Model reduction for molecular diffusion in nanoporous media

Porous materials are widely used for applications in gas storage and separation. The diffusive properties of a variety of gases in porous media can be modeled using molecular dynamics simulations that can be computationally demanding depending on the pore geometry, complexity and amount of gas adsorbed. We explore a dimensionality reduction approach for estimating the self-diffusion coefficient of gases in simple pores using Langevin dynamics, such that the three-dimensional (3D) atomistic interactions that determine the diffusion properties of realistic systems can be reduced to an effective one-dimensional (1D) diffusion problem along the pore axis. We demonstrate the approach by modeling the transport of nitrogen molecules in single-walled carbon nanotubes of different radii, showing that 1D Langevin models can be parametrized with a few single-particle 3D atomistic simulations. The reduced 1D model predicts accurate diffusion coefficients over a broad range of temperatures and gas densities. Our work paves the way for studying the diffusion process of more general porous materials as zeolites or metal-organics frameworks with effective models of reduced complexity.Comment: 8 pages, 6 figure

First-Principles Screening of Metal-Organic Frameworks for Entangled Photon Pair Generation

The transmission of strong laser light in nonlinear optical materials can generate output photons sources that carry quantum entanglement in multiple degrees of freedom, making this process a fundamentally important tool in optical quantum technology. However, the availability of efficient optical crystals for entangled light generation is severely limited in terms of diversity, thus reducing the prospects for the implementation of next-generation protocols in quantum sensing, communication and computing. To overcome this, we developed and implemented a multi-scale first-principles modeling technique for the computational discovery of novel nonlinear optical devices based on metal-organic framework (MOF) materials that can efficiently generate entangled light via spontaneous parametric down-conversion(SPDC). Using collinear degenerate type-I SPDC as a case study, we computationally screen a database of 114,373 synthesized MOF materials to establish correlations between the structure and chemical composition of MOFs with the brightness and coherence properties of entangled photon pairs. We identify a subset of 49 non-centrosymmetric mono-ligand MOF crystals with high chemical and optical stability that produce entangled photon pairs with intrinsic $G^{(2)}$ correlation times $\tau_c\sim 10-30$ fs and pair generation rates in the range $10^4-10^{8}$ s$^{-1}$mW$^{-1}$mm$^{-1}$ at 1064 nm. Conditions for optimal type-I phase matching are given for each MOF and relationships between pair brightness, crystal band gap and optical birefringence are discussed. Correlations between the optical properties of crystals and their constituent molecular ligands are also given. Our work paves the way for the computational design of MOF-based devices for optical quantum technology.Comment: Supplementary Material (13 pages, 12 figures, 2 table) at the end of manuscript. Github (https://github.com/snoozynooj/Type-I-SPDC). arXiv admin note: text overlap with arXiv:1807.10885 by other author

Giant Generation of Polarization-Entangled Photons in Metal Organic Framework Waveguides

Parametric nonlinear optical processes are instrumental in optical quantum technology for generating entangled light. However, the range of materials conventionally used for producing entangled photons is limited. Metal-organic frameworks (MOFs) have emerged as a novel class of optical materials with customizable nonlinear properties and proven chemical and optical stability. The large number of combinations of metal atoms and organic ligand from which bulk MOF crystals are known to form, facilitates the search of promising candidates for nonlinear optics. To accelerate the discovery of next-generation quantum light sources, we employ a multi-scale modeling approach to study phase-matching conditions for collinear degenerate type-II spontaneous parametric down conversion (SPDC) with MOF-based one dimensional waveguides. Using periodic-DFT calculations to compute the nonlinear optical properties of selected zinc-based MOF crystals, we predict polarization-entangled pair generation rates of $\sim 10^3-10^6$ s$^{-1}$mW$^{-1}$mm$^{-1}$ at 1064 nm, which are comparable with industry materials used in quantum optics. We find that the biaxial MOF crystal Zn(4-pyridylacrylate)$_2$ improves two-fold the conversion efficiency over a periodically-poled KTP waveguide of identical dimensions. This work underscores the great potential of MOF single crystals as entangled light sources for applications in quantum communication and sensing.Comment: Supplementary Material (9 pages, 7 figures, 1 table) at the end of manuscrip

Multiscale structural control of linked metal–organic polyhedra gel by aging-induced linkage-reorganization

Assembly of permanently porous metal–organic polyhedra/cages (MOPs) with bifunctional linkers leads to soft supramolecular networks featuring both porosity and processability. However, the amorphous nature of such soft materials complicates their characterization and thus limits rational structural control. Here we demonstrate that aging is an effective strategy to control the hierarchical network of supramolecular gels, which are assembled from organic ligands as linkers and MOPs as junctions. Normally, the initial gel formation by rapid gelation leads to a kinetically trapped structure with low controllability. Through a controlled post-synthetic aging process, we show that it is possible to tune the network of the linked MOP gel over multiple length scales. This process allows control on the molecular-scale rearrangement of interlinking MOPs, mesoscale fusion of colloidal particles and macroscale densification of the whole colloidal network. In this work we elucidate the relationships between the gel properties, such as porosity and rheology, and their hierarchical structures, which suggest that porosity measurement of the dried gels can be used as a powerful tool to characterize the microscale structural transition of their corresponding gels. This aging strategy can be applied in other supramolecular polymer systems particularly containing kinetically controlled structures and shows an opportunity to engineer the structure and the permanent porosity of amorphous materials for further applications

SSAGES : Software Suite for Advanced General Ensemble Simulations

Molecular simulation has emerged as an essential tool for modern-day research, but obtaining proper results and making reliable conclusions from simulations requires adequate sampling of the system under consideration. To this end, a variety of methods exist in the literature that can enhance sampling considerably, and increasingly sophisticated, effective algorithms continue to be developed at a rapid pace. Implementation of these techniques, however, can be challenging for experts and non-experts alike. There is a clear need for software that provides rapid, reliable, and easy access to a wide range of advanced sampling methods and that facilitates implementation of new techniques as they emerge. Here we present SSAGES, a publicly available Software Suite for Advanced General Ensemble Simulations designed to interface with multiple widely used molecular dynamics simulations packages. SSAGES allows facile application of a variety of enhanced sampling techniques—including adaptive biasing force, string methods, and forward flux sampling—that extract meaningful free energy and transition path data from all-atom and coarse-grained simulations. A noteworthy feature of SSAGES is a user-friendly framework that facilitates further development and implementation of new methods and collective variables. In this work, the use of SSAGES is illustrated in the context of simple representative applications involving distinct methods and different collective variables that are available in the current release of the suite. The code may be found at: https://github.com/MICCoM/SSAGES-public

Strong influence of the H₂ binding energy on the Maxwell–Stefan diffusivity in NU-100, UiO-68 and IRMOF-16

Molecular dynamics simulations of H2 at 243 K in NU-100, UiO-68 and IRMOF-16 with zero, one, three and six Mg alkoxide functional groups per linker were performed, revealing interesting behavior of the Maxwell-Stefan (M-S) diffusivity in these systems. A strong relationship between the isosteric heat of adsorption and the M-S diffusivity was found, with the M-S diffusivity decreasing exponentially with increasing heat of adsorption. The insights obtained may be valuable for future studies of diffusion and gas storage in nanoporous materials with strongly interacting functional groups

Considerations in the use of ML interaction potentials for free energy calculations

Machine learning potentials (MLPs) offer the potential to accurately model the energy and free energy landscapes of molecules with the precision of quantum mechanics and an efficiency similar to classical simulations. This research focuses on using equivariant graph neural networks MLPs due to their proven effectiveness in modeling equilibrium molecular trajectories. A key issue addressed is the capability of MLPs to accurately predict free energies and transition states by considering both the energy and the diversity of molecular configurations. We examined how the distribution of collective variables (CVs) in the training data affects MLP accuracy in determining the free energy surface (FES) of systems, using Metadynamics simulations for butane and alanine dipeptide (ADP). The study involved training forty-three MLPs, half based on classical molecular dynamics data and the rest on ab initio computed energies. The MLPs were trained using different distributions that aim to replicate hypothetical scenarios of sampled CVs obtained if the underlying FES of the system was unknown. Findings for butane revealed that training data coverage of key FES regions ensures model accuracy regardless of CV distribution. However, missing significant FES regions led to correct potential energy predictions but failed free energy reconstruction. For ADP, models trained on classical dynamics data were notably less accurate, while ab initio-based MLPs predicted potential energy well but faltered on free energy predictions. These results emphasize the challenge of assembling an all-encompassing training set for accurate FES prediction and highlight the importance of understanding the FES in preparing training data. The study points out the limitations of MLPs in free energy calculations, stressing the need for comprehensive data that encompasses the system's full FES for effective model training

Machine learning identification of organic compounds using visible light

Identifying chemical compounds is essential in several areas of science and engineering. Laser-based techniques are promising for autonomous compound detection because the optical response of materials encodes enough electronic and vibrational information for remote chemical identification. This has been exploited using the fingerprint region of infrared absorption spectra, which involves a dense set of absorption peaks that are unique to individual molecules, thus facilitating chemical identification. However, optical identification using visible light has not been realized. Using decades of experimental refractive index data in the scientific literature of pure organic compounds and polymers over a broad range of frequencies from the ultraviolet to the far-infrared, we develop a machine learning classifier that can accurately identify organic species based on a single-wavelength dispersive measurement in the visible spectral region, away from absorption resonances. The optical classifier proposed here could be applied to autonomous material identification protocols or applications.Comment: 18 pages, 7 figures. Open database and python code. Version adds comparison with Raman classifiers (Table 1

Understanding correlation between structure and entangled photon pair properties with metal-organic frameworks

Spontaneous parametric down conversion (SPDC) is a quantum second-order non- linear optical process where the photons generated are frequently used in quantum information processing. Materials with large second-order nonlinearities (χ(2)) can be used as entangled photon sources with high brightness. The source brightness scales as the square of the effective nonlinearity (deff ) which is an intrinsic property of the mate- rial. Understanding material factors which can significantly alter this intrinsic property is useful in developing new materials which are SPDC efficient. In our work, we focus on understanding factors affecting the entangled photon pair properties such as the arrangements of ligands within the Zn(3-ptz)3 metal-organic framework (MOF) crystal and temperature. We find that the arrangement and alignment of the pyridine rings in the crystal structure significantly affect the deff and birefringence (∆n). Smaller pyri- dine ring alignments relative to the optic c-axis increases the ∆n, which in turn leads to larger photon pair correlation times (τc) in coincidence measurements. Our work has significant implication in understanding the effect of ligand arrangement on deff and τc for any MOF crystal structure, providing a tool to rationalize the optimization of MOF crystals for the development of efficient nonlinear optical devices