Machine Learning Based Approach to Predict Ductile Damage Model Parameters for Polycrystalline Metals

Damage models for ductile materials typically need to be parameterized, often with the appropriate parameters changing for a given material depending on the loading conditions. This can make parameterizing these models computationally expensive, since an inverse problem must be solved for each loading condition. Using standard inverse modeling techniques typically requires hundreds or thousands of high-fidelity computer simulations to estimate the optimal parameters. Additionally, the time of a human expert is required to set up the inverse model. Machine learning has recently emerged as an alternative approach to inverse modeling in these settings, where the machine learning model is trained in an offline manner and new parameters can be quickly generated on the fly, after training is complete. This work utilizes such a workflow to enable the rapid parameterization of a ductile damage model called TEPLA with a machine learning inverse model. The machine learning model can efficiently estimate the model parameters much faster, as compared to previously employed methods, such as Bayesian calibration. The results demonstrate good accuracy on a synthetic test dataset and is validated against experimental data.Comment: 13 pages, 9 figures; v2 minor revisio

Predictive computational materials Modeling with machine learning: creating the next generation of atomistic potential using neural networks

Machine learning techniques using artificial neural networks (ANNs) have proven to be effective tools to rapidly mimic first principles calculations. These tools are capable of sub meV/atom accuracy while operating with linear scaling with respect to the system size. Here novel interatomic potentials are constructed based on the rapid artificial neural network (RANN) formalism. This approach generates precise force fields for various metals that have historically been difficult to describe at the atomic scale. These force fields can be utilized in molecular dynamics simulations to provide new physical insights. The RANN formalism, which is incorporated into a LAMMPS molecular dynamics package, utilizes fingerprints inspired by the modified embedded atom method (MEAM) formalism and angular screening which enables shorter neighbor lists and faster computations. It has been shown that this implementation can replicate speeds comparable to traditional models while maintaining high agreement (~1meV/atom) with DFT. This formalism has been used to predict correct slip modes in Mg and successfully model the challenging structure of zinc for the first time. Also RANN potentials for titanium and zirconium accurately predict the phase diagrams and triple points with high accuracy as computed by relative free energy calculation. New Ti and Zr potential successfully predict the dislocation core structures and slip planes for high pressure phase of these materials. The formalism\u27s precision and transferability enable the construction of a binary Ti-Al system with DFT accuracy at MD speed. Due to the RANN\u27s great fidelity to DFT data and predictive capability, these potentials might be helpful in the future for investigating behavior and interaction in large-scale atomistic simulation

Faceting and Twin–Twin Interactions in {1121} and {1122} Twins in Titanium

Twin–twin interactions are an important component of the microstructural evolution of hexagonal close-packed metals undergoing plasticity. These interactions are prevalent because of the predominance of twinning due to limited easy slip modes. Despite their importance, the complexities of the atomic-scale behavior of interacting twins has limited robust characterization. Using interfacial defect theory, we developed a three-dimensional model of twin–twin interactions, double twinning and other complex interfacial reactions that occur between twins acting on different interface planes. Using molecular dynamics, {1122} and {1121} twins in titanium were activated and produced facets, twin–twin interactions and double twins that we characterized with our model. The results showed excellent agreement between the molecular dynamics results and the model. Surprisingly, some highly ordered and mobile boundaries can be produced by these complex reactions, which could provide important insights for higher scale models of plasticity

Faceting and Twin–Twin Interactions in {11<span style="text-decoration: overline">2</span>1} and {11<span style="text-decoration: overline">2</span>2} Twins in Titanium

Twin–twin interactions are an important component of the microstructural evolution of hexagonal close-packed metals undergoing plasticity. These interactions are prevalent because of the predominance of twinning due to limited easy slip modes. Despite their importance, the complexities of the atomic-scale behavior of interacting twins has limited robust characterization. Using interfacial defect theory, we developed a three-dimensional model of twin–twin interactions, double twinning and other complex interfacial reactions that occur between twins acting on different interface planes. Using molecular dynamics, {1122} and {1121} twins in titanium were activated and produced facets, twin–twin interactions and double twins that we characterized with our model. The results showed excellent agreement between the molecular dynamics results and the model. Surprisingly, some highly ordered and mobile boundaries can be produced by these complex reactions, which could provide important insights for higher scale models of plasticity