53,989 research outputs found
ReaxFF parameter optimization with Monte-Carlo and evolutionary algorithms : guidelines and insights
ReaxFF is a computationally efficient force field to simulate complex reactive dynamics in extended molecular models with diverse chemistries, if reliable force-field parameters are available for the chemistry of interest. If not, they must be optimized by minimizing the error ReaxFF makes on a relevant training set. Because this optimization is far from trivial, many methods, in particular, genetic algorithms (GAs), have been developed to search for the global optimum in parameter space. Recently, two alternative parameter calibration techniques were proposed, that is, Monte-Carlo force field optimizer (MCFF) and covariance matrix adaptation evolutionary strategy (CMA-ES). In this work, CMA-ES, MCFF, and a GA method (OGOLEM) are systematically compared using three training sets from the literature. By repeating optimizations with different random seeds and initial parameter guesses, it is shown that a single optimization run with any of these methods should not be trusted blindly: nonreproducible, poor or premature convergence is a common deficiency. GA shows the smallest risk of getting trapped into a local minimum, whereas CMA-ES is capable of reaching the lowest errors for two-third of the cases, although not systematically. For each method, we provide reasonable default settings, and our analysis offers useful guidelines for their usage in future work. An important side effect impairing parameter optimization is numerical noise. A detailed analysis reveals that it can be reduced, for example, by using exclusively unambiguous geometry optimization in the training set. Even without this noise, many distinct near-optimal parameter vectors can be found, which opens new avenues for improving the training set and detecting overfitting artifacts
Dynamics simulation of human box delivering task
Thesis (M.S.) University of Alaska Fairbanks, 2018The dynamic optimization of a box delivery motion is a complex task. The key component is to achieve an optimized motion associated with the box weight, delivering speed, and location. This thesis addresses one solution for determining the optimal delivery of a box. The delivering task is divided into five subtasks: lifting, transition step, carrying, transition step, and unloading. Each task is simulated independently with appropriate boundary conditions so that they can be stitched together to render a complete delivering task. Each task is formulated as an optimization problem. The design variables are joint angle profiles. For lifting and carrying task, the objective function is the dynamic effort. The unloading task is a byproduct of the lifting task, but done in reverse, starting with holding the box and ending with it at its final position. In contrast, for transition task, the objective function is the combination of dynamic effort and joint discomfort. The various joint parameters are analyzed consisting of joint torque, joint angles, and ground reactive forces. A viable optimization motion is generated from the simulation results. It is also empirically validated. This research holds significance for professions containing heavy box lifting and delivering tasks and would like to reduce the chance of injury.Chapter 1 Introduction -- Chapter 2 Skeletal Human Modeling -- Chapter 3 Kinematics and Dynamics -- Chapter 4 Lifting Simulation -- Chapter 5 Carrying Simulation -- Chapter 6 Delivering Simulation -- Chapter 7 Conclusion and Future Research -- Reference
Exploration of Reaction Pathways and Chemical Transformation Networks
For the investigation of chemical reaction networks, the identification of
all relevant intermediates and elementary reactions is mandatory. Many
algorithmic approaches exist that perform explorations efficiently and
automatedly. These approaches differ in their application range, the level of
completeness of the exploration, as well as the amount of heuristics and human
intervention required. Here, we describe and compare the different approaches
based on these criteria. Future directions leveraging the strengths of chemical
heuristics, human interaction, and physical rigor are discussed.Comment: 48 pages, 4 figure
Development of a Transferable Reactive Force Field of P/H Systems: Application to the Chemical and Mechanical Properties of Phosphorene
ReaxFF provides a method to model reactive chemical systems in large-scale
molecular dynamics simulations. Here, we developed ReaxFF parameters for
phosphorus and hydrogen to give a good description of the chemical and
mechanical properties of pristine and defected black phosphorene. ReaxFF for
P/H is transferable to a wide range of phosphorus and hydrogen containing
systems including bulk black phosphorus, blue phosphorene, edge-hydrogenated
phosphorene, phosphorus clusters and phosphorus hydride molecules. The
potential parameters were obtained by conducting unbiased global optimization
with respect to a set of reference data generated by extensive ab initio
calculations. We extend ReaxFF by adding a 60{\deg} correction term which
significantly improves the description of phosphorus clusters. Emphasis has
been put on obtaining a good description of mechanical response of black
phosphorene with different types of defects. Compared to nonreactive SW
potential [1], ReaxFF for P/H systems provides a huge improvement in describing
the mechanical properties the pristine and defected black phosphorene and the
thermal stability of phosphorene nanotubes. A counterintuitive phenomenon is
observed that single vacancies weaken the black phosphorene more than double
vacancies with higher formation energy. Our results also show that mechanical
response of black phosphorene is more sensitive to defects for the zigzag
direction than for the armchair direction. Since ReaxFF allows straightforward
extensions to the heterogeneous systems, such as oxides, nitrides, ReaxFF
parameters for P/H systems build a solid foundation for the reactive force
field description of heterogeneous P systems, including P-containing 2D van der
Waals heterostructures, oxides, etc
The dynamics of copper intercalated molybdenum ditelluride
Layered transition metal dichalcogenides are emerging as key materials in
nanoelectronics and energy applications. Predictive models to understand their
growth, thermomechanical properties and interactions with metals are needed in
order to accelerate their incorporation into commercial products. Interatomic
potentials enable large-scale atomistic simulations at the device level, beyond
the range of applications of first principle methods. We present a ReaxFF
reactive force field to describe molybdenum ditelluride and its interactions
with copper. We optimized the force field parameters to describe the properties
of layered MoTe2 in various phases, the intercalation of Cu atoms and clusters
within its van der Waals gap, including a proper description of energetics,
charges and mechanical properties. The training set consists of an extensive
set of first principle calculations computed from density functional theory. We
use the force field to study the adhesion of a single layer MoTe2 on a Cu(111)
surface and the results are in good agreement with density functional theory,
even though such structures were not part of the training set. We characterized
the mobility of the Cu ions intercalated into MoTe2 under the presence of an
external electric fields via molecular dynamics simulations. The results show a
significant increase in drift velocity for electric fields of approximately 0.4
V/A and that mobility increases with Cu ion concentration.Comment: 21 pages, 9 Figure
A Hierarchal Planning Framework for AUV Mission Management in a Spatio-Temporal Varying Ocean
The purpose of this paper is to provide a hierarchical dynamic mission
planning framework for a single autonomous underwater vehicle (AUV) to
accomplish task-assign process in a limited time interval while operating in an
uncertain undersea environment, where spatio-temporal variability of the
operating field is taken into account. To this end, a high level reactive
mission planner and a low level motion planning system are constructed. The
high level system is responsible for task priority assignment and guiding the
vehicle toward a target of interest considering on-time termination of the
mission. The lower layer is in charge of generating optimal trajectories based
on sequence of tasks and dynamicity of operating terrain. The mission planner
is able to reactively re-arrange the tasks based on mission/terrain updates
while the low level planner is capable of coping unexpected changes of the
terrain by correcting the old path and re-generating a new trajectory. As a
result, the vehicle is able to undertake the maximum number of tasks with
certain degree of maneuverability having situational awareness of the operating
field. The computational engine of the mentioned framework is based on the
biogeography based optimization (BBO) algorithm that is capable of providing
efficient solutions. To evaluate the performance of the proposed framework,
firstly, a realistic model of undersea environment is provided based on
realistic map data, and then several scenarios, treated as real experiments,
are designed through the simulation study. Additionally, to show the robustness
and reliability of the framework, Monte-Carlo simulation is carried out and
statistical analysis is performed. The results of simulations indicate the
significant potential of the two-level hierarchical mission planning system in
mission success and its applicability for real-time implementation
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