scholarly journals An Efficient First-Principles Saddle Point Searching Method Based on Distributed Kriging Metamodels

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Anh Tran ◽  
Lijuan He ◽  
Yan Wang
Keyword(s):  
Potential Energy ◽  
Saddle Point ◽  
Density Functional ◽  
Materials Science ◽  
Mean Squared Error ◽  
Dimensional Space ◽  
Minimum Energy ◽  
Saddle Points ◽  
Computational Materials ◽  
Searching Method

Searching for local minima, saddle points, and minimum energy paths (MEPs) on the potential energy surface (PES) is challenging in computational materials science because of the complexity of PES in high-dimensional space and the numerical approximation errors in calculating the potential energy. In this work, a local minimum and saddle point searching method is developed based on kriging metamodels of PES. The searching algorithm is performed on both kriging metamodels as the approximated PES and the calculated one from density functional theory (DFT). As the searching advances, the kriging metamodels are further refined to include new data points. To overcome the dimensionality problem in classical kriging, a distributed kriging approach is proposed, where clusters of data are formed and one metamodel is constructed within each cluster. When the approximated PES is used during the searching, each predicted potential energy value is an aggregation of the ones from those metamodels. The dimension of each metamodel is further reduced based on the observed symmetry in materials systems. The uncertainty associated with the ground-state potential energy is quantified using the statistical mean-squared error in kriging to improve the robustness of the searching method.

An Efficient Saddle Point Search Method Using Kriging Metamodels

2015 ◽  
Author(s):  
Lijuan He ◽  
Yan Wang
Keyword(s):  
Potential Energy ◽  
Saddle Point ◽  
Density Functional ◽  
Search Algorithm ◽  
Saddle Points ◽  
Surrogate Models ◽  
Functional Theory ◽  
Search Results ◽  
Point Search ◽  
Large Systems

Simulating phase transformation of materials at the atomistic scale requires the knowledge of saddle points on the potential energy surface (PES). In the existing first-principles saddle point search methods, the requirement of a large number of expensive evaluations of potential energy, e.g. using density functional theory (DFT), limits the application of such algorithms to large systems. Thus, it is meaningful to minimize the number of functional evaluations as DFT simulations during the search process. Furthermore, model-form uncertainty and numerical errors are inherent in DFT and search algorithms. Robustness of the search results should be considered. In this paper, a new search algorithm based on Kriging is presented to search local minima and saddle points on a PES efficiently and robustly. Different from existing searching methods, the algorithm keeps a memory of searching history by constructing surrogate models and uses the search results on the surrogate models to provide the guidance of future search on the PES. The surrogate model is also updated with more DFT simulation results. The algorithm is demonstrated by the examples of Rastrigin and Schwefel functions with a multitude of minima and saddle points.

A Review of Recent Phase Transition Simulation Methods: Saddle Point Search

2008 ◽  
Author(s):  
Devendra Alhat ◽  
Vernet Lasrado ◽  
Yan Wang
Keyword(s):  
Potential Energy ◽  
Potential Energy Surface ◽  
Saddle Point ◽  
Energy Surface ◽  
Kinetic Monte Carlo ◽  
Saddle Points ◽  
Transition Process ◽  
Point Search ◽  
Recent Phase ◽  
First Order Phase

A review of saddle point search methods on a potential energy surface is presented in this paper. Finding saddle points on a complex potential energy surface is the major challenge in modeling and simulating the kinetics of first-order phase transitions. Once the saddle points have been identified and the activation energy for the transition is known, one can apply the kinetic Monte Carlo method to simulate the transition process. We consider some factors while reviewing the methods, such as whether the solution is global, the knowledge of the Hessian during the search, the capability to locate multiple saddle points and higher order saddle points, the kind of approximations used for potential energy surface, if any; and the convergence of the methods.

Nanomechanics and Testing of Core-Shell Composite Ligaments for High Strength, Light Weight Foams

MRS Advances ◽  
2017 ◽  
Vol 2 (58-59) ◽  
pp. 3577-3583
Author(s):  
Aiganym Yermembetova ◽  
Raheleh M. Rahimi ◽  
Chang-Eun Kim ◽  
Jack L. Skinner ◽  
Jessica M. Andriolo ◽  
...  
Keyword(s):  
Density Functional ◽  
Materials Science ◽  
High Strength ◽  
Core Shell ◽  
Plating Bath ◽  
Fiber Mats ◽  
Computational Materials Science ◽  
Metallic Shell ◽  
Computational Materials ◽  
Pre Treatment

ABSTRACT Composite nanostructured foams consisting of a metallic shell deposited on a polymeric core were formed by plating copper via electroless deposition on electrospun polycaprolactone (PCL) fiber mats. The final structure consisted of 1000-nm scale PCL fibers coated with 100s of nm of copper, leading to final core-shell thicknesses on the order of 1000-3000 nm. The resulting open cell, core-shell foams had relative densities between 4 and 15 %. By controlling the composition of the adjuncts in the plating bath, particularly the composition of formaldehyde, the relative thickness of copper coating as the fiber diameter could be controlled. As-spun PCL mats had a nominal compressive modulus on the order of 0.1 MPa; adding a uniform metallic shell increased the modulus up to 2 MPa for sub-10 % relative density foams. A computational materials science analysis using density functional theory was used to explore the effects pre-treatment with Pd may have on the density of nuclei formed during electroless plating.

scholarly journals Exploring the effects of quantum decoherence on the excited-state dynamics of molecular systems

2021 ◽  
Vol 140 (4) ◽  
Author(s):  
Eric R. Heller ◽  
Jan-Ole Joswig ◽  
Gotthard Seifert
Keyword(s):  
Excited State ◽  
Potential Energy ◽  
Density Functional ◽  
Bond Cleavage ◽  
Minimum Energy ◽  
Quantum Decoherence ◽  
Ensemble Averaging ◽  
Excited State Dynamics ◽  
Complete Active Space ◽  
Related Compounds

AbstractFewest-switches surface hopping (FSSH) is employed in order to investigate the nonadiabatic excited-state dynamics of thiophene and related compounds and hence to establish a connection between the electronic system, the critical points in configuration space and the deactivation dynamics. The potential-energy surfaces of the studied molecules were calculated with complete active space self-consistent field and time-dependent density-functional theory. They are analyzed thoroughly to locate and optimize minimum-energy conical intersections, which are essential to the dynamics of the system. The influence of decoherence on the dynamics is examined by employing different decoherence schemes. We find that irrespective of the employed decoherence algorithm, the population dynamics of thiophene give results which are sound with the expectations grounded on the analysis of the potential-energy surface. A more detailed look at single trajectories as well as on the excited-state lifetimes, however, reveals a substantial dependence on how decoherence is accounted for. In order to connect these findings, we describe how ensemble averaging cures some of the overcoherence problems of uncorrected FSSH. Eventually, we identify carbon–sulfur bond cleavage as a common feature accompanying electronic transitions between different states in the simulations of all thiophene-related compounds studied in this work, which is of interest due to their relevance in organic photovoltaics.

CRYSTAL: a computational tool for the ab initio study of the electronic properties of crystals

2005 ◽  
Vol 220 (5/6) ◽  
Author(s):  
Roberto Dovesi ◽  
Roberto Orlando ◽  
Bartolomeo Civalleri ◽  
Carla Roetti ◽  
Victor R. Saunders ◽  
...  
Keyword(s):  
Density Functional ◽  
Materials Science ◽  
Theoretical Chemistry ◽  
Structure And Properties ◽  
Computational Materials Science ◽  
Hartree Fock ◽  
Science Group ◽  
Computational Materials ◽  
Basic Features ◽  
Hybrid Approximations

AbstractCRYSTAL [1] computes the electronic structure and properties of periodic systems (crystals, surfaces, polymers) within Hartree-Fock [2], Density Functional and various hybrid approximations.CRYSTAL was developed during nearly 30 years (since 1976) [3] by researchers of the Theoretical Chemistry Group in Torino (Italy), and the Computational Materials Science group in CLRC (Daresbury, UK), with important contributions from visiting researchers, as documented by the main authors list and the bibliography.The basic features of the program CRYSTAL are presented, with two examples of application in the field of crystallography [4, 5].

scholarly journals Density functional theory study of the Jahn-Teller effect in cobaltocene

2009 ◽  
Vol 81 (8) ◽  
pp. 1397-1411 ◽  
Author(s):  
Matija Zlatar ◽  
Carl-Wilhelm Schläpfer ◽  
Emmanuel Penka Fowe ◽  
Claude A. Daul
Keyword(s):  
Density Functional Theory ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Density Functional ◽  
Energy Surface ◽  
Minimum Energy ◽  
Normal Modes ◽  
Coupling Coefficients ◽  
Functional Theory ◽  
Jahn Teller

A detailed discussion of the potential energy surface of bis(cyclopentadienyl)cobalt(II), cobaltocene, is given. Vibronic coupling coefficients are calculated using density functional theory (DFT). Results are in good agreement with experimental findings. On the basis of our calculation there is no second-order Jahn–Teller (JT) effect as predicted by group theory. The JT distortion can be expressed as a linear combination of all totally symmetric normal modes of the low-symmetry, minimum-energy conformation. The out-of-plane ring deformation is the most important mode. The JT distortion is analyzed by seeking the path of minimal energy of the adiabatic potential energy surface.

scholarly journals Applications of Data Driven Methods in Computational Materials Design

2021 ◽  
Author(s):  
Christoph Dösinger ◽  
Tobias Spitaler ◽  
Alexander Reichmann ◽  
Daniel Scheiber ◽  
Lorenz Romaner
Keyword(s):  
Grain Boundary ◽  
Density Functional ◽  
Materials Science ◽  
Boundary Segregation ◽  
Large Data ◽  
Data Driven ◽  
Gradient Boosting ◽  
Model Parameters ◽  
Data Repositories ◽  
Computational Materials

AbstractIn today’s digitized world, large amounts of data are becoming available at rates never seen before. This holds true also for materials science where high-throughput simulations and experiments continuously produce new data. Data driven methods are required which can make best use of the information stored in large data repositories. In the present article, two of such data driven methods are presented. First, we apply machine learning to generalize and extend the results obtained from computationally intense density functional theory (DFT) simulations. We show how grain boundary segregation energies can be trained with gradient boosting regression and extended to many more positions in the grain boundary for a complete description. The second method relies on Bayesian inference, which can be used to calibrate models to give data and quantification of the model uncertainty. The method is applied to calibrate parameters in thermodynamic models of the Gibbs energy of Ti-W alloys. The uncertainty of the model parameters is quantified and propagated to the phase boundaries of the Ti-W system.

scholarly journals Toward Computational Materials Design: The Impact of Density Functional Theory on Materials Research

MRS Bulletin ◽  
2006 ◽  
Vol 31 (9) ◽  
pp. 659-668 ◽  
Author(s):  
Jürgen Hafner ◽  
Christopher Wolverton ◽  
Gerbrand Ceder
Keyword(s):  
Density Functional Theory ◽  
Surface Science ◽  
Density Functional ◽  
Materials Science ◽  
Electron Systems ◽  
Functional Theory ◽  
Materials Research ◽  
Properties Of Materials ◽  
Computational Materials ◽  
The Impact

The development of modern materials science has led to a growing need to understand the phenomena determining the properties of materials and processes on an atomistic level. The interactions between atoms and electrons are governed by the laws of quantum mechanics; hence, accurate and efficient techniques for solving the basic quantum-mechanical equations for complex many-atom, many-electron systems must be developed. Density functional theory (DFT) marks a decisive breakthrough in these efforts, and in the past decade DFT has had a rapidly growing impact not only on fundamental but also industrial research. This article discusses the fundamental principles of DFT and the highly efficient computational tools that have been developed for its application to complex problems in materials science. Also highlighted are state-of-the-art applications in many areas of materials research, such as structural materials, catalysis and surface science, nanomaterials, and biomaterials and geophysics.

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