Progress of Physics-based Mean-field Modeling and Simulation of Steel

The progress of mean-field modeling and simulation in steel is presented. In the modeling, the focus is put on the development and application of a physical modeling base, including Calphad, diffusion assessment, nucleation and growth of precipitates, and dislocation dynamics. This leads to an improved prediction of the materials response after different thermo-mechanical treatments in terms of microstructure evolution and mechanical properties. The presented case studies represent the success of the integrated computational materials engineering approach.

Strain and interface energy of ellipsoidal inclusions subjected to volumetric eigenstrains: shape factors

Thermodynamic modeling of the development of non-spherical inclusions as precipitates in alloys is an important topic in computational materials science. The precipitates may have markedly different properties compared to the matrix. Both the elastic contrast and the misfit eigenstrain may yield a remarkable generation of elastic strain energy which immediately influences the kinetics of the developing precipitates. The relevant thermodynamic framework has been mostly based on spherical precipitates. However, the shapes of actual particles are often not spherical. The energetics of such precipitates can be met by adapting the spherical energy terms with shape factors. The well-established Eshelby framework is used to evaluate the elastic strain energy of inclusions with ellipsoidal shapes (described by the axes a, b, and c) that are subjected to a volumetric transformation strain. The outcome of the study is two shape factors, one for the elastic strain energy and the other for the interface energy. Both quantities are provided in the form of easy-to-use diagrams. Furthermore, threshold elastic contrasts yielding strain energy shape factors with the value 1.0 for any ellipsoidal shape are studied.

METHODOLOGY OF CONVERTING OF THE COORDINATES OF THE BASIS ATOMS IN A UNIT CELL OF CRYSTALLINE Β-GA2O3, SPECIFIED IN A MONOCLINIC CRYSTALLOGRAPHIC SYSTEM, IN THE LABORATORY CARTESIAN COORDINATES FOR COMPUTER APPLICATIONS

One of the most important areas of modern technology is the creation of new structural materials with predetermined properties. Along with industrial methods for their preparation and technologies associated with the artificial growth of crystalline structures, various methods of computer modeling of new materials have recently become increasingly important. Such approaches can significantly reduce the number of full-scale experiments. Many applications of the computational materials science are related to the need to establish a relationship between structure and electronic characteristics, and other physical properties of crystals. This article on the example of crystalline β-Ga2O3 presents the algorithms used in the converting of the coordinates of the basis atoms in a unit cell of crystal, specified in a crystallographic system, in the Cartesian coordinates for the computational experiment.

Applications of Data Driven Methods in Computational Materials Design

In 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.

A shortcut to the thermodynamic limit for quantum many-body calculations of metals

Computationally efficient and accurate quantum mechanical approximations to solve the many-electron Schrödinger equation are crucial for computational materials science. Methods such as coupled cluster theory show potential for widespread adoption if computational cost bottlenecks can be removed. For example, extremely dense k-point grids are required to model long-range electronic correlation effects, particularly for metals. Although these grids can be made more effective by averaging calculations over an offset (or twist angle), the resultant cost in time for coupled cluster theory is prohibitive. We show here that a single special twist angle can be found using the transition structure factor, which provides the same benefit as twist averaging with one or two orders of magnitude reduction in computational time. We demonstrate that this not only works for metal systems but also is applicable to a broader range of materials, including insulators and semiconductors.

Numerical Modelling of the Powder Metallurgical Manufacturing Chain of High Strength Sintered Gears

This paper presents a digital model for the powder metallurgical (PM) production chain of high-performance sintered gears based on an integrated computational materials engineering (ICME) platform. Discrete and finite element methods (DEM and FEM) were combined to describe the macroscopic material response to the thermomechanical loads and process conditions during the entire production process. The microstructural evolution during the sintering process was predicted on the meso-scale using a Monte-Carlo Model. The effective elastic properties were determined by a homogenization method based on modelling a representative volume element (RVE). The results were subsequently used for the FE modelling of the heat treatment process. Through the development of multi-scale models, it was possible obtain characteristics of the microstructural features. The predicted hardness and residual stress distributions allowed the calculation of the tooth root load bearing capacity of the heat-treated sintered gears.