Computational Materials Science: The Simulation of Materials Microstructures and Properties

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This free service is available to anyone who has published and whose publication is in Scopus. Researcher Academy Author Services Try out personalized alert features. The most downloaded articles from Computational Materials Science in the last 90 days.

High-throughput electronic band structure calculations: Challenges and tools August An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2 March Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set July Simulation of metal additive manufacturing microstructures using kinetic Monte Carlo - Open access July Accelerating materials science with high-throughput computations and machine learning 15 April Machine learning guided design of functional materials with targeted properties 15 June Python Materials Genomics pymatgen : A robust, open-source python library for materials analysis February In this section, some of the recent numerical studies on modeling kinetics of solidification and microstructural evolution are presented.

Dendrites are the tree-like microstructures that form during solidification. They form when molten metal or alloy freezes from liquid state, which may happen during many industrial manufacturing processes. The morphology, size, and spacing between dendritic arms have a significant influence on material properties.

ISBN 13: 9783527295418

We developed a numerical model based on lattice Boltzmann LB and cellular automaton CA methods to simulate dendrite growth in 3D. The low computational cost and great scalability of the LB-CA model enabled us to perform large-scale 3D simulations in macro size domains. It is known that melt flow can significantly alter the dendrite growth kinetics by affecting solutal gradient around the dendrites.

Improves component performance by the development of customized properties and functions. Produces higher manufacturing yield and cost-effectiveness by computer-aided process optimization.

Computational Materials Science An Introduction

Promotes the development of new or modification of existing manufacturing processes, which are optimized on a project by project basis. Empowers advances in situations where validation cannot be accomplished by means of experimentation or requires unrealistic experimental time frames.

Breadcrumb

Allows for accurate prediction of component life. ICME has been designed for: Modeling and simulation of microstructural evolution. Virtual identification of material data and the development of suitable material models.

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