Cross-scale analysis of nickel Superalloy fatigue using Markov state model-molecular dynamics method

Molecular dynamics (MD) simulation is an advanced method in microscale modelling of material but it depends on the complexity of the model. The performance of MD simulation is poor once the model size is huge. To accelerate the computing of MD simulation, the Markov state model (MSM) can be applied because of the ability to predict a future state of a stochastic system. With the advantage of MSM and MD applied in material modelling, a good result could be expected where the time scale limitation of MD simulation is bridged by MSM method. In this research, an MSM method based on the MD microstates in which a nickel superalloy's atomic model arrangements and their microstructure evolution have been treated with the Markov properties is presented. This MSM is based and classified by a dislocation model which is a fundamental of the microstructural tessellation evolution. The results indicate that the microstructure evolution in a situation of energy minimisation favours the formation of new faults alongside existing ones. And dislocation accumulation on the grain boundary was observed during fatigue resolving. Some dislocations formatted and grown in the middle of coarse grain and penetrated through the grain

Some Critical Thoughts on Computational Materials Science

1. A Model is a Model is a Model is a Model The title of this report is of course meant to provoke. Why? Because there always exists a menace of confusing models with reality. Does anyone now refer to “first principles simulations”? This point is well taken. However, practically all of the current predictions in this domain are based on simulating electron dynamics using local density functional theory. These simulations, though providing a deep insight into materials ground states, are not exact but approximate solutions of the Schrödinger equation, which - not to forget - is a model itself [1]. Does someone now refer to “finite element simulations”? This point is also well taken. However, also in this case one has to admit that approximate solutions to large sets of non-linear differential equations formulated for a (non-existing) continuum under idealized boundary conditions is what it is: a model of nature but not reality. But us let calm down and render the discussion a bit more serious: current methods of ground state calculations are definitely among the cutting-edge disciplines in computational materials science and the community has learnt much from it during the last years. Similar aspects apply for some continuum-based finite element simulations. After all this report is meant to attract readers into this exciting field and not to repulse them. And for this reason I feel obliged to first make a point in underscoring that any interpretation of a research result obtained by computer simulation should be accompanied by scrutinizing the model ingredients and boundary conditions of that calculation in the same critical way as an experimentalist would check his experimental set-up

Machine Learning for High-entropy Alloys: Progress, Challenges and Opportunities

High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional mechanical properties and the vast compositional space for new HEAs. However, understanding their novel physical mechanisms and then using these mechanisms to design new HEAs are confronted with their high-dimensional chemical complexity, which presents unique challenges to (i) the theoretical modeling that needs accurate atomic interactions for atomistic simulations and (ii) constructing reliable macro-scale models for high-throughput screening of vast amounts of candidate alloys. Machine learning (ML) sheds light on these problems with its capability to represent extremely complex relations. This review highlights the success and promising future of utilizing ML to overcome these challenges. We first introduce the basics of ML algorithms and application scenarios. We then summarize the state-of-the-art ML models describing atomic interactions and atomistic simulations of thermodynamic and mechanical properties. Special attention is paid to phase predictions, planar-defect calculations, and plastic deformation simulations. Next, we review ML models for macro-scale properties, such as lattice structures, phase formations, and mechanical properties. Examples of machine-learned phase-formation rules and order parameters are used to illustrate the workflow. Finally, we discuss the remaining challenges and present an outlook of research directions, including uncertainty quantification and ML-guided inverse materials design.Comment: This review paper has been accepted by Progress in Materials Scienc

Deep Learning of Microstructures

The internal structure of materials also called the microstructure plays a critical role in the properties and performance of materials. The chemical element composition is one of the most critical factors in changing the structure of materials. However, the chemical composition alone is not the determining factor, and a change in the production process can also significantly alter the materials\u27 structure. Therefore, many efforts have been made to discover and improve production methods to optimize the functional properties of materials. The most critical challenge in finding materials with enhanced properties is to understand and define the salient features of the structure of materials that have the most significant impact on the desired property. In other words, by process, structure, and property (PSP) linkages, the effect of changing process variables on material structure and, consequently, the property can be examined and used as a powerful tool in material design with desirable characteristics. In particular, forward PSP linkages construction has received considerable attention thanks to the sophisticated physics-based models. Recently, machine learning (ML), and data science have also been used as powerful tools to find PSP linkages in materials science. One key advantage of the ML-based models is their ability to construct both forward and inverse PSP linkages. Early ML models in materials science were primarily focused on process-property linkages construction. Recently, more microstructures are included in the materials design ML models. However, the inverse design of microstructures, i.e., the prediction of vii process and chemistry from a microstructure morphology image have received limited attention. This is a critical knowledge gap to address specifically for the problems that the ideal microstructure or morphology with the specific chemistry associated with the morphological domains are known, but the chemistry and processing which would lead to that ideal morphology are unknown. In this study, first, we propose a framework based on a deep learning approach that enables us to predict the chemistry and processing history just by reading the morphological distribution of one element. As a case study, we used a dataset from spinodal decomposition simulation of Fe-Cr-Co alloy created by the phase-field method. The mixed dataset, which includes both images, i.e., the morphology of Fe distribution, and continuous data, i.e., the Fe minimum and maximum concentration in the microstructures, are used as input data, and the spinodal temperature and initial chemical composition are utilized as the output data to train the proposed deep neural network. The proposed convolutional layers were compared with pretrained EfficientNet convolutional layers as transfer learning in microstructure feature extraction. The results show that the trained shallow network is effective for chemistry prediction. However, accurate prediction of processing temperature requires more complex feature extraction from the morphology of the microstructure. We benchmarked the model predictive accuracy for real alloy systems with a Fe-Cr-Co transmission electron microscopy micrograph. The predicted chemistry and heat treatment temperature were in good agreement with the ground truth. The treatment time was considered to be constant in the first study. In the second work, we propose a fused-data deep learning framework that can predict the heat treatment time as well as temperature and initial chemical compositions by reading the morphology of Fe distribution and its concentration. The results show that the trained deep neural network has the highest accuracy for chemistry and then time and temperature. We identified two scenarios for inaccurate predictions; 1) There are several paths for an identical microstructure, and 2) Microstructures reach steady-state morphologies after a long time of aging. The error analysis shows that most of the wrong predictions are not wrong, but the other right answers. We validated the model successfully with an experimental Fe-Cr-Co transmission electron microscopy micrograph. Finally, since the data generation by simulation is computationally expensive, we propose a quick and accurate Predictive Recurrent Neural Network (PredRNN) model for the microstructure evolution prediction. Essentially, microstructure evolution prediction is a spatiotemporal sequence prediction problem, where the prediction of material microstructure is difficult due to different process histories and chemistry. As a case study, we used a dataset from spinodal decomposition simulation of Fe-Cr-Co alloy created by the phase-field method for training and predicting future microstructures by previous observations. The results show that the trained network is capable of efficient prediction of microstructure evolution

Vision-Augmented molecular dynamics simulation of nanoindentation

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.This thesis has contributed to the literature by providing a pathway to simplify the process of carrying out molecular dynamics simulation. As a part of the investigation, a user-friendly vision-augmented technique was developed to set up and carry out atomistic simulations using hand-gestures. The system is novel in its concept as it enables the user to directly manipulate the atomic structures on the screen, in 3D space using hand gestures, allowing the exploration and visualisation of molecular interactions at different relative conformations. The hand gestures are used to pick and place atoms on the screen allowing thereby the ease of preparing and carrying out molecular dynamics simulations in a more intuitive way. The end result is that users with limited expertise in developing molecular structures can now do so easily and intuitively by the use of body gestures to interact with the simulator to study the system in question. The proposed system was tested by performing parallel molecular dynamics simulations to study (i) crystal anisotropy of a diamond cubic substrate (crystalline silicon) using nanoindentation with a long-range (Screened bond order) Tersoff potential and (ii) crystal anisotropy of a body centre cubic metal (tantalum) using nanoindentation with an Embedded Atomic Method (EAM) type potential. The MD data was post-processed to reveal size effects observed in anisotropy of both these materials, namely, silicon and tantalum. The value of hardness and elastic modulus obtained from the MD data was found in accordance with what has been discovered previously by experiments, thereby validating the simulations. Based on this, it is anticipated that the proposed system will open up new horizons to the current methods on how an MD simulation is designed and executed.King Abdullah bin Abdul-Aziz Al- Saud Ministry of Higher Education and Northern Borders University, Kingdom of Saudi Arabi