1,105 research outputs found
Coarse-grained interaction potentials for polyaromatic hydrocarbons
Using Kohn-Sham density functional theory (KS-DFT), we have studied the
interaction between various polyaromatic hydrocarbon molecules. The systems
range from mono-cyclic benzene up to hexabenzocoronene (hbc). For several
conventional exchange-correlation functionals potential energy curves of
interaction of the - stacking hbc dimer are reported. It is found
that all pure local density or generalized gradient approximated functionals
yield qualitatively incorrect predictions regarding structure and interaction.
Inclusion of a non-local, atom-centered correction to the KS-Hamiltonian
enables quantitative predictions. The computed potential energy surfaces of
interaction yield parameters for a coarse-grained potential, which can be
employed to study discotic liquid-crystalline mesophases of derived
polyaromatic macromolecules
Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
Classical intermolecular potentials typically require an extensive
parametrization procedure for any new compound considered. To do away with
prior parametrization, we propose a combination of physics-based potentials
with machine learning (ML), coined IPML, which is transferable across small
neutral organic and biologically-relevant molecules. ML models provide
on-the-fly predictions for environment-dependent local atomic properties:
electrostatic multipole coefficients (significant error reduction compared to
previously reported), the population and decay rate of valence atomic
densities, and polarizabilities across conformations and chemical compositions
of H, C, N, and O atoms. These parameters enable accurate calculations of
intermolecular contributions---electrostatics, charge penetration, repulsion,
induction/polarization, and many-body dispersion. Unlike other potentials, this
model is transferable in its ability to handle new molecules and conformations
without explicit prior parametrization: All local atomic properties are
predicted from ML, leaving only eight global parameters---optimized once and
for all across compounds. We validate IPML on various gas-phase dimers at and
away from equilibrium separation, where we obtain mean absolute errors between
0.4 and 0.7 kcal/mol for several chemically and conformationally diverse
datasets representative of non-covalent interactions in biologically-relevant
molecules. We further focus on hydrogen-bonded complexes---essential but
challenging due to their directional nature---where datasets of DNA base pairs
and amino acids yield an extremely encouraging 1.4 kcal/mol error. Finally, and
as a first look, we consider IPML in denser systems: water clusters,
supramolecular host-guest complexes, and the benzene crystal.Comment: 15 pages, 9 figure
Tuning dissociation using isoelectronically doped graphene and hexagonal boron nitride: water and other small molecules
Novel uses for 2-dimensional materials like graphene and hexagonal boron nitride (h-BN) are being frequently discovered especially for membrane and catalysis applications. Still however, a great deal remains to be understood about the interaction of environmentally and industrially elevant molecules such as water with these materials. Taking inspiration from advances in hybridising graphene and h-BN, we explore using density functional theory, the dissociation of water, hydrogen, methane, and methanol on graphene, h-BN, and their isoelectronic doped counterparts: BN doped graphene and C doped h-BN. We find that doped surfaces are considerably more reactive than their pristine counterparts and by comparing the reactivity of several small molecules we develop a general framework for dissociative adsorption. From this a particularly attractive consequence of isoelectronic doping emerges: substrates can be doped to enhance their reactivity specifically towards either polar or non-polar adsorbates. As such, these substrates are potentially viable candidates for selective catalysts and membranes, with the implication that a range of tuneable materials can be designed
Constant Size Molecular Descriptors For Use With Machine Learning
A set of molecular descriptors whose length is independent of molecular size
is developed for machine learning models that target thermodynamic and
electronic properties of molecules. These features are evaluated by monitoring
performance of kernel ridge regression models on well-studied data sets of
small organic molecules. The features include connectivity counts, which
require only the bonding pattern of the molecule, and encoded distances, which
summarize distances between both bonded and non-bonded atoms and so require the
full molecular geometry. In addition to having constant size, these features
summarize information regarding the local environment of atoms and bonds, such
that models can take advantage of similarities resulting from the presence of
similar chemical fragments across molecules. Combining these two types of
features leads to models whose performance is comparable to or better than the
current state of the art. The features introduced here have the advantage of
leading to models that may be trained on smaller molecules and then used
successfully on larger molecules.Comment: 18 pages, 5 figure
Self-reported psychopathy in the Middle East: a cross-national comparison across Egypt, Saudi Arabia, and the United States
Background: The construct of psychopathy is sparsely researched in the non-Western world, particularly in the Middle East. As such, the extent to which the psychopathy construct can be generalized to other cultures, including Middle Eastern Arab cultures, is largely unknown. Methods: The present study investigated the cross-cultural/national comparability of self-reported psychopathy in the United States (N = 786), Egypt (N = 296), and Saudi Arabia (N = 341). Results: A widely used psychopathy questionnaire demonstrated largely similar properties across the American and Middle Eastern samples and associations between Five Factor Model (FFM) personality and psychopathy were broadly consistent. Nevertheless, several notable cross-cultural differences emerged, particularly with regard to the internal consistencies of psychopathy dimensions and the correlates of Coldheartedness. Additionally, in contrast to most findings in Western cultures, associations between psychopathy and FFM personality varied consistently by gender in the Egyptian sample. Conclusions: These findings lend preliminary support to the construct validity of self-reported psychopathy in Arabic-speaking cultures, providing provisional evidence for the cross-cultural generalizability of certain core characteristics of psychopathy
Correlates of Psychopathic Personality Traits in Everyday Life: Results from a Large Community Survey
Although the traits of psychopathic personality (psychopathy) have received extensive attention from researchers in forensic psychology, psychopathology, and personality psychology, the relations of these traits to aspects of everyday functioning are poorly understood. Using a large internet survey of members of the general population (N = 3388), we examined the association between psychopathic traits, as measured by a brief but well-validated self-report measure, and occupational choice, political orientation, religious affiliation, and geographical residence. Psychopathic traits, especially those linked to fearless dominance, were positively and moderately associated with holding leadership and management positions, as well as high-risk occupations. In addition, psychopathic traits were positively associated with political conservatism, lack of belief in God, and living in Europe as opposed to the United States, although the magnitudes of these statistical effects were generally small in magnitude. Our findings offer preliminary evidence that psychopathic personality traits display meaningful response penetration into daily functioning, and raise provocative questions for future research
Fifty Psychological and Psychiatric Terms to Avoid: a List of Inaccurate, Misleading, Misused, Ambiguous, and Logically Confused Words and Phrases
The goal of this article is to promote clear thinking and clear writing among students and teachers of psychological science by curbing terminological misinformation and confusion. To this end, we present a provisional list of 50 commonly used terms in psychology, psychiatry, and allied fields that should be avoided, or at most used sparingly and with explicit caveats. We provide corrective information for students, instructors, and researchers regarding these terms, which we organize for expository purposes into five categories: inaccurate or misleading terms, frequently misused terms, ambiguous terms, oxymorons, and pleonasms. For each term, we (a) explain why it is problematic, (b) delineate one or more examples of its misuse, and (c) when pertinent, offer recommendations for preferable terms. By being more judicious in their use of terminology, psychologists and psychiatrists can foster clearer thinking in their students and the field at large regarding mental phenomena
Library of dispersion-corrected atom-centered potentials for generalized gradient approximation functionals: Elements H, C, N, O, He, Ne, Ar, and Kr
Parameters for analytical dispersion-corrected atom-centered potentials (DCACPs) are presented to improve the description of London dispersion forces within the generalized gradient approximation functionals BLYP, BP, and PBE. A library of DCACPs for hydrogen, carbon, nitrogen, oxygen, helium, neon, argon, and krypton was obtained by calibrating against high-level CCSD(T) or configuration interaction references. The performance and transferability of DCACPs were tested on weakly bound complexes and provide excellent results throughout all investigated systems
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