6,490 research outputs found
Automated Identification and Classification of Stereochemistry: Chirality and Double Bond Stereoisomerism
Stereoisomers have the same molecular formula and the same atom connectivity
and their existence can be related to the presence of different
three-dimensional arrangements. Stereoisomerism is of great importance in many
different fields since the molecular properties and biological effects of the
stereoisomers are often significantly different. Most drugs for example, are
often composed of a single stereoisomer of a compound, and while one of them
may have therapeutic effects on the body, another may be toxic. A challenging
task is the automatic detection of stereoisomers using line input
specifications such as SMILES or InChI since it requires information about
group theory (to distinguish stereoisomers using mathematical information about
its symmetry), topology and geometry of the molecule. There are several
software packages that include modules to handle stereochemistry, especially
the ones to name a chemical structure and/or view, edit and generate chemical
structure diagrams. However, there is a lack of software capable of
automatically analyzing a molecule represented as a graph and generate a
classification of the type of isomerism present in a given atom or bond.
Considering the importance of stereoisomerism when comparing chemical
structures, this report describes a computer program for analyzing and
processing steric information contained in a chemical structure represented as
a molecular graph and providing as output a binary classification of the isomer
type based on the recommended conventions. Due to the complexity of the
underlying issue, specification of stereochemical information is currently
limited to explicit stereochemistry and to the two most common types of
stereochemistry caused by asymmetry around carbon atoms: chiral atom and double
bond. A Webtool to automatically identify and classify stereochemistry is
available at http://nams.lasige.di.fc.ul.pt/tools.ph
Classes of Terminating Logic Programs
Termination of logic programs depends critically on the selection rule, i.e.
the rule that determines which atom is selected in each resolution step. In
this article, we classify programs (and queries) according to the selection
rules for which they terminate. This is a survey and unified view on different
approaches in the literature. For each class, we present a sufficient, for most
classes even necessary, criterion for determining that a program is in that
class. We study six classes: a program strongly terminates if it terminates for
all selection rules; a program input terminates if it terminates for selection
rules which only select atoms that are sufficiently instantiated in their input
positions, so that these arguments do not get instantiated any further by the
unification; a program local delay terminates if it terminates for local
selection rules which only select atoms that are bounded w.r.t. an appropriate
level mapping; a program left-terminates if it terminates for the usual
left-to-right selection rule; a program exists-terminates if there exists a
selection rule for which it terminates; finally, a program has bounded
nondeterminism if it only has finitely many refutations. We propose a
semantics-preserving transformation from programs with bounded nondeterminism
into strongly terminating programs. Moreover, by unifying different formalisms
and making appropriate assumptions, we are able to establish a formal hierarchy
between the different classes.Comment: 50 pages. The following mistake was corrected: In figure 5, the first
clause for insert was insert([],X,[X]
Finding unprecedentedly low-thermal-conductivity half-Heusler semiconductors via high-throughput materials modeling
The lattice thermal conductivity ({\kappa}{\omega}) is a key property for
many potential applications of compounds. Discovery of materials with very low
or high {\kappa}{\omega} remains an experimental challenge due to high costs
and time-consuming synthesis procedures. High-throughput computational
pre-screening is a valuable approach for significantly reducing the set of
candidate compounds. In this article, we introduce efficient methods for
reliably estimating the bulk {\kappa}{\omega} for a large number of compounds.
The algorithms are based on a combination of machine-learning algorithms,
physical insights, and automatic ab-initio calculations. We scanned
approximately 79,000 half-Heusler entries in the AFLOWLIB.org database. Among
the 450 mechanically stable ordered semiconductors identified, we find that
{\kappa}{\omega} spans more than two orders of magnitude- a much larger range
than that previously thought. {\kappa}{\omega} is lowest for compounds whose
elements in equivalent positions have large atomic radii. We then perform a
thorough screening of thermodynamical stability that allows to reduce the list
to 77 systems. We can then provide a quantitative estimate of {\kappa}{\omega}
for this selected range of systems. Three semiconductors having
{\kappa}{\omega} < 5 W /(m K) are proposed for further experimental study.Comment: 9 pages, 4 figure
An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis
open access articleThis article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions
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