Laboratory studies of atomic collision processes of importance in planetary atmospheres

Progress in the following research supported under NSG 7386 is reported: (1) measurement of differential cross sections for atomic and molecular collisions relevant to analysis and modeling of data from Pioneer 11, Pioneer 12, Voyager 1, and Voyager 2; (2) analysis of measured differential cross section results to provide scattering data in forms that are easy to apply to atmospheric modeling work; (3) analysis of the data to give basic information on the molecular potentials involved in the scattering process; and (4) development and initial use of apparatus to study dissociative processes in neutral molecules

Do we need to know the temperature in prestellar cores?

Molecular line observations of starless (prestellar) cores combined with a chemical evolution modeling and radiative transfer calculations are a powerful tool to study the earliest stages of star formation. However, conclusions drawn from such a modeling may noticeably depend on the assumed thermal structure of the cores. The assumption of isothermality, which may work well in chemo-dynamical studies, becomes a critical factor in molecular line formation simulations. We argue that even small temperature variations, which are likely to exist in starless cores, can have a non-negligible effect on the interpretation of molecular line data and derived core properties. In particular, ``chemically pristine'' isothermal cores (low depletion) can have centrally peaked C$^{18}$O and C$^{34}$S radial intensity profiles, while having ring-like intensity distributions in models with a colder center and/or warmer envelope assuming the same underlying chemical structure. Therefore, derived molecular abundances based on oversimplified thermal models may lead to a mis-interpretation of the line data.Comment: ApJL, accepte

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

Chemical Informatics Functionality in R

The flexibility and scope of the R programming environment has made it a popular choice for statistical modeling and scientific prototyping in a number of fields. In the field of chemistry, R provides several tools for a variety of problems related to statistical modeling of chemical information. However, one aspect common to these tools is that they do not have direct access to the information that is available from chemical structures, such as contained in molecular descriptors. We describe the rcdk package that provides the R user with access to the CDK, a Java framework for cheminformatics. As a result, it is possible to read in a variety of molecular formats, calculate molecular descriptors and evaluate fingerprints. In addition, we describe the rpubchem that will allow access to the data in PubChem, a public repository of molecular structures and associated assay data for approximately 8 million compounds. Currently, the package allows access to structural information as well as some simple molecular properties from PubChem. In addition the package allows access to bio-assay data from the PubChem FTP servers.

Uncertainty Estimates for Theoretical Atomic and Molecular Data

Sources of uncertainty are reviewed for calculated atomic and molecular data that are important for plasma modeling: atomic and molecular structure and cross sections for electron-atom, electron-molecule, and heavy particle collisions. We concentrate on model uncertainties due to approximations to the fundamental many-body quantum mechanical equations and we aim to provide guidelines to estimate uncertainties as a routine part of computations of data for structure and scattering.Comment: 65 pages, 18 Figures, 3 Tables. J. Phys. D: Appl. Phys. Final accepted versio

A Molecular Biology Database Digest

Computational Biology or Bioinformatics has been defined as the application of mathematical and Computer Science methods to solving problems in Molecular Biology that require large scale data, computation, and analysis [18]. As expected, Molecular Biology databases play an essential role in Computational Biology research and development. This paper introduces into current Molecular Biology databases, stressing data modeling, data acquisition, data retrieval, and the integration of Molecular Biology data from different sources. This paper is primarily intended for an audience of computer scientists with a limited background in Biology