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Metallization and dissociation are key transformations in diatomic molecules at high densities particularly significant for modeling giant planets. Using X-ray absorption spectroscopy and atomistic modeling, we demonstrate that in halogens, the formation of a textit{connected} molecular structure takes place at pressures well below metallization. Here we show that the iodine diatomic molecule first elongates of $sim$0.007 AA~up to a critical pressure of $P_c$ $backsim$7~GPa developing bonds between molecules. Then its length continuously decreases with pressure up to 15-20~GPa. Universal trends in halogens are shown and allow to predict for chlorine a pressure of 42$pm$8~GPa for molecular bond-length reversal. Our findings tackle the molecule invariability paradigm in diatomic molecular phases at high pressures and may be generalized to other abundant diatomic molecules in the universe, including hydrogen.
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Evolutionary structure searches predict three new phases of iodine polyhydrides stable under pressure. Insulating P1-H5I, consisting of zigzag chains of HI (delta+)and H2(delta-) molecules, is stable between 30-90 GPa. Cmcm-H2I and P6/mmm-H4I are found on the 100, 150 and 200 GPa convex hulls. These two phases are good metals, even at 1 atm, because they consist of monoatomic lattices of iodine. At 100 GPa the Tc of H2I and H4I are estimated to be 7.8 and 17.5 K, respectively. The increase in Tc relative to elemental iodine results from a larger omega-log from the light mass of hydrogen, and an enhanced lambda from modes containing H/I and H/H vibrations.
A proof-of-concept framework for identifying molecules of unknown elemental composition and structure using experimental rotational data and probabilistic deep learning is presented. Using a minimal set of input data determined experimentally, we describe four neural network architectures that yield information to assist in the identification of an unknown molecule. The first architecture translates spectroscopic parameters into Coulomb matrix eigenspectra, as a method of recovering chemical and structural information encoded in the rotational spectrum. The eigenspectrum is subsequently used by three deep learning networks to constrain the range of stoichiometries, generate SMILES strings, and predict the most likely functional groups present in the molecule. In each model, we utilize dropout layers as an approximation to Bayesian sampling, which subsequently generates probabilistic predictions from otherwise deterministic models. These models are trained on a modestly sized theoretical dataset comprising ${sim}$83,000 unique organic molecules (between 18 and 180 amu) optimized at the $omega$B97X-D/6-31+G(d) level of theory where the theoretical uncertainty of the spectroscopic constants are well understood and used to further augment training. Since chemical and structural properties depend highly on molecular composition, we divided the dataset into four groups corresponding to pure hydrocarbons, oxygen-bearing, nitrogen-bearing, and both oxygen- and nitrogen-bearing species, training each type of network with one of these categories thus creating experts within each domain of molecules. We demonstrate how these models can then be used for practical inference on four molecules, and discuss both the strengths and shortcomings of our approach, and the future directions these architectures can take.
Rotation of molecules embedded in He nanodroplets is explored by a combination of fs laser-induced alignment experiments and angulon quasiparticle theory. We demonstrate that at low fluence of the fs alignment pulse, the molecule and its solvation shell can be set into coherent collective rotation lasting long enough to form revivals. With increasing fluence, however, the revivals disappear -- instead, rotational dynamics as rapid as for an isolated molecule is observed during the first few picoseconds. Classical calculations trace this phenomenon to transient decoupling of the molecule from its He shell. Our results open novel opportunities for studying non-equilibrium solute-solvent dynamics and quantum thermalization.
We present an update on the development of techniques to adapt Single Molecule Fluorescent Imaging for the tagging of individual barium ions in high pressure xenon gas detectors, with the goal of realizing a background-free neutrinoless double beta decay technology. Previously reported progress is reviewed, including the recent demonstration of single barium dication sensitivity using SMFI. We then describe two important advances: 1) the development of a new class of custom barium sensing fluorescent dyes, which exhibit a significantly stronger response to barium than commercial calcium sensing compounds in aqueous solution; 2) the first demonstration of a dry-phase chemosensor for barium ions. This proceeding documents work presented at the 9th Symposium on Large TPCs for Rare Event Detection in Paris, France.
Low-gap conjugated polymers have enabled an impressive increase in the efficiencies of organic solar cells, primarily due to their red absorption which allows harvesting of that part of the solar spectrum. Here, we report that the true optical gap of one prototypical material, PTB7, is in fact at significantly higher energy than has previously been reported, indicating that the red absorption utilized in these materials in solar cells is entirely due to chain aggregation. Using single-molecule spectroscopy we find that PL from isolated nanoscale aggregates consists of multiple independently emitting chromophores. At the single-molecule level, however, straight single chains with a high degree of emission polarization are observed. The PL is found to be ~0.4 eV higher in energy, with a longer lifetime than the red aggregates, and is attributed to single chromophores. Our findings indicate that the impressive light-harvesting abilities of PTB7 in the red spectral region arises solely from chain aggregation.
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