3,360 research outputs found
Protein Structure Determination Using Chemical Shifts
In this PhD thesis, a novel method to determine protein structures using
chemical shifts is presented.Comment: Univ Copenhagen PhD thesis (2014) in Biochemistr
Evolution of Swarm Robotics Systems with Novelty Search
Novelty search is a recent artificial evolution technique that challenges
traditional evolutionary approaches. In novelty search, solutions are rewarded
based on their novelty, rather than their quality with respect to a predefined
objective. The lack of a predefined objective precludes premature convergence
caused by a deceptive fitness function. In this paper, we apply novelty search
combined with NEAT to the evolution of neural controllers for homogeneous
swarms of robots. Our empirical study is conducted in simulation, and we use a
common swarm robotics task - aggregation, and a more challenging task - sharing
of an energy recharging station. Our results show that novelty search is
unaffected by deception, is notably effective in bootstrapping the evolution,
can find solutions with lower complexity than fitness-based evolution, and can
find a broad diversity of solutions for the same task. Even in non-deceptive
setups, novelty search achieves solution qualities similar to those obtained in
traditional fitness-based evolution. Our study also encompasses variants of
novelty search that work in concert with fitness-based evolution to combine the
exploratory character of novelty search with the exploitatory character of
objective-based evolution. We show that these variants can further improve the
performance of novelty search. Overall, our study shows that novelty search is
a promising alternative for the evolution of controllers for robotic swarms.Comment: To appear in Swarm Intelligence (2013), ANTS Special Issue. The final
publication will be available at link.springer.co
Hybrid RHF/MP2 geometry optimizations with the Effective Fragment Molecular Orbital Method
The frozen domain effective fragment molecular orbital method is extended to
allow for the treatment of a single fragment at the MP2 level of theory. The
approach is applied to the conversion of chorismate to prephenate by chorismate
mutase, where the substrate is treated at the MP2 level of theory while the
rest of the system is treated at the RHF level. MP2 geometry optimization is
found to lower the barrier by up to 3.5 kcal/mol compared to RHF optimzations
and ONIOM energy refinement and leads to a smoother convergence with respect to
the basis set for the reaction profile. For double zeta basis sets the increase
in CPU time relative to RHF is roughly a factor of two.Comment: 11 pages, 3 figure
Alchemical and structural distribution based representation for improved QML
We introduce a representation of any atom in any chemical environment for the
generation of efficient quantum machine learning (QML) models of common
electronic ground-state properties. The representation is based on scaled
distribution functions explicitly accounting for elemental and structural
degrees of freedom. Resulting QML models afford very favorable learning curves
for properties of out-of-sample systems including organic molecules,
non-covalently bonded protein side-chains, (HO)-clusters, as well as
diverse crystals. The elemental components help to lower the learning curves,
and, through interpolation across the periodic table, even enable "alchemical
extrapolation" to covalent bonding between elements not part of training, as
evinced for single, double, and triple bonds among main-group elements
- …