1,185 research outputs found
Toward transferable interatomic van der Waals interactions without electrons: The role of multipole electrostatics and many-body dispersion
We estimate polarizabilities of atoms in molecules without electron density,
using a Voronoi tesselation approach instead of conventional density
partitioning schemes. The resulting atomic dispersion coefficients are
calculated, as well as many-body dispersion effects on intermolecular potential
energies. We also estimate contributions from multipole electrostatics and
compare them to dispersion. We assess the performance of the resulting
intermolecular interaction model from dispersion and electrostatics for more
than 1,300 neutral and charged, small organic molecular dimers. Applications to
water clusters, the benzene crystal, the anti-cancer drug
ellipticine---intercalated between two Watson-Crick DNA base pairs, as well as
six macro-molecular host-guest complexes highlight the potential of this method
and help to identify points of future improvement. The mean absolute error made
by the combination of static electrostatics with many-body dispersion reduces
at larger distances, while it plateaus for two-body dispersion, in conflict
with the common assumption that the simple correction will yield proper
dissociative tails. Overall, the method achieves an accuracy well within
conventional molecular force fields while exhibiting a simple parametrization
protocol.Comment: 13 pages, 8 figure
Exploring water adsorption on isoelectronically doped graphene using alchemical derivatives
The design and production of novel 2-dimensional materials has seen great
progress in the last decade, prompting further exploration of the chemistry of
such materials. Doping and hydrogenating graphene is an experimentally realised
method of changing its surface chemistry, but there is still a great deal to be
understood on how doping impacts on the adsorption of molecules. Developing
this understanding is key to unlocking the potential applications of these
materials. High throughput screening methods can provide particularly effective
ways to explore vast chemical compositions of materials. Here, alchemical
derivatives are used as a method to screen the dissociative adsorption energy
of water molecules on various BN doped topologies of hydrogenated graphene. The
predictions from alchemical derivatives are assessed by comparison to density
functional theory. This screening method is found to predict dissociative
adsorption energies that span a range of more than 2 eV, with a mean absolute
error eV. In addition, we show that the quality of such predictions can
be readily assessed by examination of the Kohn-Sham highest occupied molecular
orbital in the initial states. In this way, the root mean square error in the
dissociative adsorption energies of water is reduced by almost an order of
magnitude (down to eV) after filtering out poor predictions. The
findings point the way towards a reliable use of first order alchemical
derivatives for efficient screening procedures
Alchemical normal modes unify chemical space
In silico design of new molecules and materials with desirable quantum
properties by high-throughput screening is a major challenge due to the high
dimensionality of chemical space. To facilitate its navigation, we present a
unification of coordinate and composition space in terms of alchemical normal
modes (ANMs) which result from second order perturbation theory. ANMs assume a
predominantly smooth nature of chemical space and form a basis in which new
compounds can be expanded and identified. We showcase the use of ANMs for the
energetics of the iso-electronic series of diatomics with 14 electrons, BN
doped benzene derivatives (C(BN)H with ),
predictions for over 1.8 million BN doped coronene derivatives, and genetic
energy optimizations in the entire BN doped coronene space. Using Ge lattice
scans as reference, the applicability ANMs across the periodic table is
demonstrated for III-V and IV-IV-semiconductors Si, Sn, SiGe, SnGe, SiSn, as
well as AlP, AlAs, AlSb, GaP, GaAs, GaSb, InP, InAs, and InSb. Analysis of our
results indicates simple qualitative structure property rules for estimating
energetic rankings among isomers. Useful quantitative estimates can also be
obtained when few atoms are changed to neighboring or lower lying elements in
the periodic table. The quality of the predictions often increases with the
symmetry of system chosen as reference due to cancellation of odd order terms.
Rooted in perturbation theory the ANM approach promises to generally enable
unbiased compound exploration campaigns at reduced computational cost
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
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
Ontologies, Mental Disorders and Prototypes
As it emerged from philosophical analyses and cognitive research, most concepts exhibit typicality effects, and resist to the efforts of defining them in terms of necessary and sufficient conditions. This holds also in the case of many medical concepts. This is a problem for the design of computer science ontologies, since knowledge representation formalisms commonly adopted in this field do not allow for the representation of concepts in terms of typical traits. However, the need of representing concepts in terms of typical traits concerns almost every domain of real world knowledge, including medical domains. In particular, in this article we take into account the domain of mental disorders, starting from the DSM-5 descriptions of some specific mental disorders. On this respect, we favor a hybrid approach to the representation of psychiatric concepts, in which ontology oriented formalisms are combined to a geometric representation of knowledge based on conceptual spaces
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
Prediction of the Atomization Energy of Molecules Using Coulomb Matrix and Atomic Composition in a Bayesian Regularized Neural Networks
Exact calculation of electronic properties of molecules is a fundamental step
for intelligent and rational compounds and materials design. The intrinsically
graph-like and non-vectorial nature of molecular data generates a unique and
challenging machine learning problem. In this paper we embrace a learning from
scratch approach where the quantum mechanical electronic properties of
molecules are predicted directly from the raw molecular geometry, similar to
some recent works. But, unlike these previous endeavors, our study suggests a
benefit from combining molecular geometry embedded in the Coulomb matrix with
the atomic composition of molecules. Using the new combined features in a
Bayesian regularized neural networks, our results improve well-known results
from the literature on the QM7 dataset from a mean absolute error of 3.51
kcal/mol down to 3.0 kcal/mol.Comment: Under review ICANN 201
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