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 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.

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.

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 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.

The Molecular Designing of Materials and Devices

MRS Bulletin ◽  
2005 ◽  
Vol 30 (11) ◽  
pp. 859-863
Author(s):  
Morrel H. Cohen
Keyword(s):  
Density Functional ◽  
Materials Science ◽  
Molecular Design ◽  
Rapid Evolution ◽  
Computer Hardware ◽  
Quantitative Prediction ◽  
Complex Materials ◽  
Band Theory ◽  
Pseudopotential Theory ◽  
Computational Materials

AbstractArthur von Hippel, a pioneer in the emergence of modern materials science, had a great goal: “the molecular designing of materials and devices.” In this article, I describe how computational materials theory has evolved over the last half century, helping to transform that goal from dream to reality. I start with two great puzzles of the 1950s: why band theory and the nearly free electron picture work. These were resolved by Landau's quasiparticle theory and by pseudopotential theory, respectively.Together with the creation and development of density functional theory, key methodological advances, and the rapid evolution of computer hardware and software, these two insights have resulted in the achievement of the quantitative prediction of the structures and properties of complex materials. Bandgapengineering and design of multilayer multifunctional materials are given as examples of “molecular design.”

scholarly journals The Significance of Entropy in Grain Boundary Segregation

Materials ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 492 ◽  
Author(s):  
Pavel Lejček ◽  
Siegfried Hofmann ◽  
Václav Paidar
Keyword(s):  
Grain Boundary ◽  
Materials Science ◽  
Boundary Segregation ◽  
Decisive Role ◽  
Grain Boundary Segregation ◽  
Bcc Iron ◽  
Boundary Chemistry ◽  
Materials Behavior

The role of entropy in materials science is demonstrated in this report in order to establish its importance for the example of solute segregation at the grain boundaries of bcc iron. We show that substantial differences in grain boundary chemistry arise if their composition is calculated with or without consideration of the entropic term. Another example which clearly documents the necessity of implementing the entropic term in materials science is the enthalpy-entropy compensation effect. Entropy also plays a decisive role in the anisotropy of grain boundary segregation and in interface characterization. The consequences of the ambiguous determination of grain boundary segregation on the prediction of materials behavior are also briefly discussed. All the mentioned examples prove the importance of entropy in the quantification of grain boundary segregation and consequently of other materials properties.

scholarly journals Predictive Modeling of Critical Temperatures in Superconducting Materials

Molecules ◽  
2020 ◽  
Vol 26 (1) ◽  
pp. 8
Author(s):  
Natalia Sizochenko ◽  
Markus Hofmann
Keyword(s):  
Cross Validation ◽  
Materials Science ◽  
Predictive Ability ◽  
Inorganic Materials ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Structure Property ◽  
Critical Temperatures ◽  
Computational Materials ◽  
Fold Cross Validation

In this study, we have investigated quantitative relationships between critical temperatures of superconductive inorganic materials and the basic physicochemical attributes of these materials (also called quantitative structure-property relationships). We demonstrated that one of the most recent studies (titled "A data-driven statistical model for predicting the critical temperature of a superconductor” and published in Computational Materials Science by K. Hamidieh in 2018) reports on models that were based on the dataset that contains 27% of duplicate entries. We aimed to deliver stable models for a properly cleaned dataset using the same modeling techniques (multiple linear regression, MLR, and gradient boosting decision trees, XGBoost). The predictive ability of our best XGBoost model (R2 = 0.924, RMSE = 9.336 using 10-fold cross-validation) is comparable to the XGBoost model by the author of the initial dataset (R2 = 0.920 and RMSE = 9.5 K in ten-fold cross-validation). At the same time, our best model is based on less sophisticated parameters, which allows one to make more accurate interpretations while maintaining a generalizable model. In particular, we found that the highest relative influence is attributed to variables that represent the thermal conductivity of materials. In addition to MLR and XGBoost, we explored the potential of other machine learning techniques (NN, neural networks and RF, random forests).

Study on the Stabilization of Heavy Metal by Cement with Quantum Chemistry

2014 ◽  
Vol 955-959 ◽  
pp. 2935-2939
Author(s):  
Lei Wang ◽  
Qi Chen
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Quantum Chemistry ◽  
Density Functional ◽  
Materials Science ◽  
Theoretical Research ◽  
Functional Theory ◽  
Computational Materials Science ◽  
Core Technology ◽  
Computational Materials

The quantum chemistry is a kind of efficient theoretical research methodology; it has become an important foundation and core technology to the computational materials science. The researches of melting mechanism, doping mechanism, mechanism of hydration activity can be used in the related areas of stabilization of heavy metal by cement. Density functional theory is reviewed in the study of the affective mechanism of cement hydration activity and the intensity of hydration by heavy metal, the mechanism of fixating heavy metals by mineral and the mechanism of lowering melting temperature. It is considered that quantum chemistry can be used to make a simulation at the micro level to explore the mechanism of cement-enclosed heavy metals and has a perfect theoretical guiding significance for further research.

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