Nuclear quadrupole hyperfine structure in HC14N/H14NC and DC15N/D15NC isomerization: a diagnostic tool for characterizing vibrational localization

NMR Properties of the Cyanide Anion, a Quasisymmetric Two-Faced Hydrogen Bonding Acceptor

The isotopically enriched cyanide anion, (13C≡15N)−, has a great potential as the NMR probe of non-covalent interactions. However, hydrogen cyanide is highly toxic and can decompose explosively. It is therefore desirable to be able to theoretically estimate any valuable results of certain experiments in advance in order to carry out experimental studies only for the most suitable molecular systems. We report the effect of hydrogen bonding on NMR properties of 15N≡13CH···X and 13C≡15NH···X hydrogen bonding complexes in solution, where X = 19F, 15N, and O=31P, calculated at the ωB97XD/def2tzvp and the polarizable continuum model (PCM) approximations. In many cases, the isotropic 13C and 15N chemical shieldings of the cyanide anion are not the most informative NMR properties of such complexes. Instead, the anisotropy of these chemical shieldings and the values of scalar coupling constants, including those across hydrogen bonds, can be used to characterize the geometry of such complexes in solids and solutions. 1J(15N13C) strongly correlates with the length of the N≡C bond.

Harnessing deep neural networks to solve inverse problems in quantum dynamics: machine-learned predictions of time-dependent optimal control fields

Inverse problems continue to garner immense interest in the physical sciences, particularly in the context of controlling desired phenomena in non-equilibrium systems. In this work, we utilize a series of deep neural networks for predicting time-dependent optimal control fields, E(t), that enable desired electronic transitions in reduced-dimensional quantum dynamical systems. To solve this inverse problem, we investigated two independent machine learning approaches: (1) a feedforward neural network for predicting the frequency and amplitude content of the power spectrum in the frequency domain (i.e., the Fourier transform of E(t)), and (2) a cross-correlation neural network approach for directly predicting E(t) in the time domain. Both of these machine learning methods give complementary approaches for probing the underlying quantum dynamics and also exhibit impressive performance in accurately predicting both the frequency and strength of the optimal control field. We provide detailed architectures and hyperparameters for these deep neural networks as well as performance metrics for each of our machine-learned models. From these results, we show that machine learning, particularly deep neural networks, can be employed as cost-effective statistical approaches for designing electromagnetic fields to enable desired transitions in these quantum dynamical systems.

Photodissociation transition states characterized by chirped pulse millimeter wave spectroscopy

The 193-nm photolysis of CH₂CHCN illustrates the capability of chirped-pulse Fourier transform millimeter-wave spectroscopy to characterize transition states. We investigate the HCN, HNC photofragments in highly excited vibrational states using both frequency and intensity information. Measured relative intensities of J = 1-0 rotational transition lines yield vibrational-level population distributions (VPD). These VPDs encode the properties of the parent molecule transition state at which the fragment molecule was born. A Poisson distribution formalism, based on the generalized Franck-Condon principle, is proposed as a framework for extracting information about the transition-state structure from the observed VPD. We employ the isotopologue CH₂CDCN to disentangle the unimolecular 3-center DCN elimination mechanism from other pathways to HCN. Our experimental results reveal a previously unknown transition state that we tentatively associate with the HCN eliminated via a secondary, bimolecular reaction.

Electronic Properties of Vinylene-Linked Heterocyclic Conducting Polymers: Predictive Design and Rational Guidance from DFT Calculations

The band structure and electronic properties in a series of vinylene-linked heterocyclic conducting polymers are investigated using density functional theory (DFT). In order to accurately calculate electronic band gaps, we utilize hybrid functionals with fully periodic boundary conditions to understand the effect of chemical functionalization on the electronic structure of these materials. The use of predictive first-principles calculations coupled with simple chemical arguments highlights the critical role that aromaticity plays in obtaining a low band gap polymer. Contrary to some approaches which erroneously attempt to lower the band gap by increasing the aromaticity of the polymer backbone, we show that being aromatic (or quinoidal) in itself does not ensure a low band gap. Rather, an iterative approach which destabilizes the ground state of the parent polymer toward the aromatic ↔ quinoidal level crossing on the potential energy surface is a more effective way of lowering the band gap in these conjugated systems. Our results highlight the use of predictive calculations guided by rational chemical intuition for designing low band gap polymers in photovoltaic materials.