Quantum Algorithm for Molecular Properties and Geometry Optimization

It is known that quantum computers, if available, would allow an exponential decrease in the computational cost of quantum simulations. We extend this result to show that the computation of molecular properties (energy derivatives) could also be sped up using quantum computers. We provide a quantum algorithm for the numerical evaluation of molecular properties, whose time cost is a constant multiple of the time needed to compute the molecular energy, regardless of the size of the system. Molecular properties computed with the proposed approach could also be used for the optimization of molecular geometries or other properties. For that purpose, we discuss the benefits of quantum techniques for Newton's method and Householder methods. Finally, global minima for the proposed optimizations can be found using the quantum basin hopper algorithm, which offers an additional quadratic reduction in cost over classical multi-start techniques.Comment: 6 page

Studies of molecular properties of polymeric materials

Aerospace environment effects (high energy electrons, thermal cycling, atomic oxygen, and aircraft fluids) on polymeric and composite materials considered for structural use in spacecraft and advanced aircraft are examined. These materials include Mylar, Ultem, and Kapton. In addition to providing information on the behavior of the materials, attempts are made to relate the measurements to the molecular processes occurring in the material. A summary and overview of the technical aspects are given along with a list of the papers that resulted from the studies. The actual papers are included in the appendices and a glossary of technical terms and definitions is included in the front matter

Many Molecular Properties from One Kernel in Chemical Space

We introduce property-independent kernels for machine learning modeling of arbitrarily many molecular properties. The kernels encode molecular structures for training sets of varying size, as well as similarity measures sufficiently diffuse in chemical space to sample over all training molecules. Corresponding molecular reference properties provided, they enable the instantaneous generation of ML models which can systematically be improved through the addition of more data. This idea is exemplified for single kernel based modeling of internal energy, enthalpy, free energy, heat capacity, polarizability, electronic spread, zero-point vibrational energy, energies of frontier orbitals, HOMO-LUMO gap, and the highest fundamental vibrational wavenumber. Models of these properties are trained and tested using 112 kilo organic molecules of similar size. Resulting models are discussed as well as the kernels' use for generating and using other property models

Molecular properties of (U)LIRGs: CO, HCN, HNC and HCO+

The observed molecular properties of a sample of FIR-luminous and OH megamaser (OH-MM) galaxies have been investigated. The ratio of high and low-density tracer lines is found to be determined by the progression of the star formation in the system. The HCO+/HCN and HCO+/HNC line ratios are good proxies for the density of the gas, and PDR and XDR sources can be distinguished using the HNC/HCN line ratio. The properties of the OH-MM sources in the sample can be explained by PDR chemistry in gas with densities higher than 10^5.5 cm^-3, confirming the classical OH-MM model of IR pumped amplification with (variable) low gains.Comment: 5 pages, 2 figures, to appear in: IAU Symposium 242 Astrophysical Masers and their Environment

Anatomical and molecular properties of long descending propriospinal neurons in mice

Long descending propriospinal neurons (LDPNs) are interneurons that form direct connections between cervical and lumbar spinal circuits. LDPNs are involved in interlimb coordination and are important mediators of functional recovery after spinal cord injury (SCI). Much of what we know about LDPNs comes from a range of species, however, the increased use of transgenic mouse lines to better define neuronal populations calls for a more complete characterisation of LDPNs in mice. In this study, we examined the cell body location, inhibitory neurotransmitter phenotype, developmental provenance, morphology and synaptic inputs of mouse LDPNs throughout the cervical and upper thoracic spinal cord. LDPNs were retrogradely labelled from the lumbar spinal cord to map cell body locations throughout the cervical and upper thoracic segments. Ipsilateral LDPNs were distributed throughout the dorsal, intermediate and ventral grey matter as well as the lateral spinal nucleus and lateral cervical nucleus. In contrast, contralateral LDPNs were more densely concentrated in the ventromedial grey matter. Retrograde labelling in GlyT2GFP and GAD67GFP mice showed the majority of inhibitory LDPNs project either ipsilaterally or adjacent to the midline. Additionally, we used several transgenic mouse lines to define the developmental provenance of LDPNs and found that V2b positive neurons form a subset of ipsilaterally projecting LDPNs. Finally, a population of Neurobiotin (NB) labelled LDPNs were assessed in detail to examine morphology and plot the spatial distribution of contacts from a variety of neurochemically distinct axon terminals. These results provide important baseline data in mice for future work on their role in locomotion and recovery from SCI