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Multiscale modeling of dislocations: combining peridynamics with gradient elasticity
Modeling dislocations is an inherently multiscale problem as one needs to simultaneously describe the high stress fields near the dislocation cores,...
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Advancements in the Simulation of 3D Ductile Damage Transition to Fracture with FORGE®
In this work the latest developments on the damage to fracture transition modeling framework of FORGE® are presented. In [8] & [9] the CIPFAR...
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A critical examination of robustness and generalizability of machine learning prediction of materials properties
Recent advances in machine learning (ML) have led to substantial performance improvement in material database benchmarks, but an excellent benchmark...
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Connector theory for reusing model results to determine materials properties
The success of Density Functional Theory (DFT) is partly due to that of simple approximations, such as the Local Density Approximation (LDA), which...
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Quantum Optimal Control: Practical Aspects and Diverse Methods
Quantum controls realize the unitary or nonunitary operations employed in quantum computers, quantum simulators, quantum communications, and other...
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Machine-learning correction to density-functional crystal structure optimization
AbstractDensity functional theory is routinely applied to predict crystal structures. The most common exchange-correlation functionals used to this...
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Third-order topological insulators with wallpaper fermions in Tl4PbTe3 and Tl4SnTe3
Nonsymmorphic symmetries open up horizons of exotic topological boundary states and even generalize the bulk–boundary correspondence, which, however,...
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A First Approach to Machine Learning with Linear Regression
Linear regression is one of the most accessible machine learning methods which has strong roots in the field of statistics. Problems of interest...
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DFU_XAI: A Deep Learning-Based Approach to Diabetic Foot Ulcer Detection Using Feature Explainability
Diabetic foot ulcer (DFU) is a potentially fatal complication of diabetes. Traditional techniques of DFU analysis and therapy are more time-consuming...
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Advanced Methods and Topics of Regression
The previous chapter introduced all conceptual and numerical foundations for solving linear regression problems in the context of machine learning....
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Variable entanglement density constitutive rheological model for polymeric fluids
A variable-entanglement density constitutive model is developed for the description of the rheological properties of entangled polymer melts and...
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Concentration
In this chapter the coupling to solute concentration in an alloy is reviewed. During transformation the concentration between the phases must be...
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Machine learning nonequilibrium electron forces for spin dynamics of itinerant magnets
We present a generalized potential theory for conservative as well as nonconservative forces for the Landau-Lifshitz magnetization dynamics....
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Experimental Study of Slurry Erosion of Ni-Hard Cast Iron and Prediction of Wear of Materials with the Use of Artificial Neural Network (ANN)
The wear resistance of Ni-Hard alloyed cast iron under slurry erosion is studied. An attempt to predict the erosion wear of materials with the help...
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Diffusive migration behavior of single atoms in aluminum alloy substrates: Explaining machine-learning-accelerated first principles calculations
In this paper, we investigated the diffusion migration behavior of single atoms in an aluminum matrix using a machine-learning (ML)-accelerated...
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Pareto optimal driven automation framework for quantitative microstructure simulation towards spinodal decomposition
In this study, we developed a Pareto optimal driven automation framework for quantitative Cahn–Hilliard simulation of spinodal decomposition...
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Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics
As materials researchers increasingly embrace machine-learning (ML) methods, it is natural to wonder what lessons can be learned from other fields...
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Computational Techniques for Nanostructured Materials
The pursuit of novel modern materials has instigated a growing need to understand and explore the basic underlying mechanisms determining the...
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Computational Techniques for Nanostructured Materials
The pursuit of novel modern materials has instigated a growing need to understand and explore the basic underlying mechanisms determining the...
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Artificial Intelligence and Machine Learning In Metallurgy. Part 2. Application Examples
The paper offers a detailed description of the application and significance of machine learning methods during various processing stages of modern...
